pa ge 1 pa ge 41 american journal of financial technology and innovation (ajfti) the nexus between personality traits and financial self-efficacy of college students lady bea tejano1*, jamaine rafaella larracochea1, ashly mae pardillo1, byrel nicole lanzaderas1, ralph joe dologuin1, christian enad1, mark joshan veyra1, mark joel ortiz1, john harry caballo1, dianne mariz nacua obenza-tanudtanud2 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3926 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: october 23, 2024 accepted: november 25, 2024 published: march 18, 2025 this quantitative correlational study explores the relationship between personality traits and financial self-efficacy among college students. data were collected online through google forms using validated instruments and respondents were identified using stratified random sampling. the study employed descriptive statistics and regression analyses using jamovi and smartpls 4.0 softwares. anchored in the big five personality traits model and financial self-efficacy theory, the results showed that the research constructs meet validity and reliability standards. findings revealed that conscientiousness and extraversion positively influence financial self-efficacy, while neuroticism has a negative effect. these results highlight the role of personality traits in shaping financial self-efficacy among students. keywords college students, personality traits, regression analysis, self efficacy introduction financial self-efficacy (fse) is a learned belief that can be developed over time, rather than an innate quality and has been growing, with studies exploring its antecedents, modifiers, and outcomes, leading to the development of an integrated model and a research agenda for future investigations (gulati & singh, 2024). according to obenza et al. (2024c) define financial behavior as a complicated, multifaceted element of personal finance that includes essential decision-making processes such as budgeting, saving, investing, and spending. this research emphasizes that an individual’s financial behaviors are firmly based in their personality traits and financial selfefficacy, rather than being influenced by external variables. according to the study, students with higher levels of financial self-efficacy—as evidenced by attributes such as conscientiousness and openness—are more likely to engage in good financial behaviors, which improve their overall financial well-being. the research focuses on the complex relationship between personality, conduct, and financial consequences. according to obenza et al. (2023d), the study investigated the mediating effect of financial self-efficacy on the financial management behavior and well-being of teachers this emphasizes the idea of how self-efficacy plays a role in the decisionmaking on financial behaviors. additionally, according to asebedo & seayb (2018), fse has been shown to positively impact saving behavior in older pre-retirees and can moderate the relationship between market volatility and financial satisfaction. additionally, asebedo et al. (2019) stated that there are several psychological factors that contribute to fse, including frequent positive affect, reduced negative affect, stronger mastery beliefs, and higher task orientation, and understanding and improving fse is crucial for financial professionals working with older adults preparing for retirement. despite significant advances in the study of financial well-being, self-efficacy, and behavior, there is still a significant gap in our understanding of the complex relationship between personality traits and financial selfefficacy among university students. numerous research have found a clear link between financial activity and financial well-being (obenza & obenza, 2024c; sabri et al., 2023; mathew & kumar, 2022), but few investigate the deeper psychological elements that may underpin these behaviors. this study addresses this gap by studying how individual personality traits influence financial selfefficacy, hence shaping financial actions and outcomes. this method broadens the discussion by relating psychological aspects to financial well-being among university students. a number of researches have explored the relationship between college students’ financial outcomes, selfefficacy, and personality factors. according to dasigan et al. (2024), there is a significant positive correlation between academic self-efficacy and the big five personality qualities of agreeableness, extraversion, conscientiousness, and openness, and a negative correlation, with neuroticism. similar findings have been made by ye & yee (2023) and winata (2019) regarding the influence of personality factors and entrepreneurial self-efficacy on students’ entrepreneurial intentions. however the impact of personality factors on entrepreneurial intentions is not reduced by financial competence according to winata (2019). according to obenza et al. (2024b) that extraversion and neuroticism were shown to be favorably correlated with financial wellbeing among college students, although agreeableness, 1 university of mindanao, davao city, philippines 2 department of education, cotabato division, philippines * corresponding author’s e-mail: ladybeatejano@gmail.com pa ge 42 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 41-46, 2025 extraversion and openness did not correlate significantly. these findings imply that personality traits are important in determining students’ financial results and self-efficacy, which provides valuable insights for creating focused interventions and educational programs. according to obenza et al. (2024e), recent studies in davao city have explored the relationships between personality traits, self-efficacy, and financial behaviors. research on college students found that extraversion and neuroticism positively correlated with financial well-being . furthermore, among public secondary school teachers, positive personality traits and self-efficacy were found to have significant correlations with career self-management according to sabanal (2024) the amount of research on financial self-efficacy (fse) and how it relates to other psychological characteristics is increasing. however, the knowledge of how personality traits in particular affect fse in college students is still severely lacking, specifically for the students in the college of hospitality education at the university of mindanao. while other studies have explored correlations between personality traits and self-efficacy in academic and entrepreneurial contexts, the direct impact of personality factors on financial self-efficacy remains underexplored. furthermore, while research has shown that personality traits impact financial well-being (obenza et al., 2024), it is unknown how these traits interact with fse regarding financial planning and decision-making. by closely investigating this gap, we can get important insights that will help us build focused interventions that can eventually advance the financial success and well-being of college students by raising their financial self-efficacy. the significance of this research is underscored by various studies that establish significant correlations between personality traits and financial behaviors. ozer and mutlu (2019) found that conscientiousness, agreeableness, and openness to experience positively influence financial behaviors, indicating that understanding personality can enhance financial well-being. khan et al. (2021) further demonstrated that personality traits predict financial self-efficacy and emotional biases, mediating financial decision-making. brooks and williams (2020) highlighted the significance of personality over emotions in determining attitudes toward financial risk while emphasizing factors like resilience and intolerance of uncertainty. in this context, the present study focuses on college students in davao city, specifically investigating how personality traits influence financial self-efficacy within this population. these findings highlight the importance of considering personality traits in predicting financial behavior, informing financial institutions on tailoring their services based on personality characteristics, and guiding the development of targeted interventions and strategies that enhance the financial welfare of college students. furthermore, providing appropriate training and support could improve financial decisionmaking outcomes over the long term, contributing to the financial well-being of this population. research question how do different personality traits influence financial self-efficacy among college students? hypothesis there is a significant relationship between personality traits and financial self-efficacy of college students. materials and methods the research design employed in this study is a nonexperimental quantitative approach, which focuses on gathering and analyzing numerical data to explore the nexus between personality traits and financial selfefficacy of college students without any manipulation. this type of study design relies on systematic observation and measurement to understand and explain phenomena (creswell & creswell 2022). the research instrument utilized in this study is adapted from john and srivasta’s (1995) big five inventory (bfi) for personality traits which measured is organized into the followings ections: extraversion, conscientiousness, agreeableness, neuroticism, and openness. additionally, the scale measuring financial self-efficacy was adapted from prawitz et al. (2006). a questionnaire with fewer errors is crucial to guaranteeing the gathering of pertinent data on the research topic (taherdoost, 2022). three specialists in the fields of education and instrument development thus validated the surveys. expert validation and tests for validity and reliability were implemented for these instruments. furthermore, cronbach’s alpha and variance inflation factor were utilized to ascertain the instruments’ reliability and validity. moreover, descriptive statistics using jamovi software version 2.0 were utilized to determine the mean and standard deviation to characterize university students’ personality traits and financial self-efficacy. also, smarpls 4.0 software was utilized to evaluate the hypothesized regression model, implement the bootstrapping standardized algorithm, and assess the model’s direct effect, including the effect sizes of individual paths. results and discussion discussion in this study, 296 participants in davao city were given a questionnaire that contains five different parts measuring personality traits such as openness, conscientiousness, neuroticism, agreeableness, extraversion, and selfefficacy as key variables. the descriptive statistics in table 1 show the mean and standard deviation for each variable. financial self-efficacy indicates a moderately high mean of 3.854 and a standard deviation of 0.556, showing that participants display trust in their ability to control their financial behaviors. with all personality traits, openness (m = 4.287, sd = 1.498) and conscientiousness (m= 4.848, sd =1.118) both have the highest scores, showing that participants typically are willing to learn and are diligent in their goals. pa ge 43 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 41-46, 2025 extraversion (m = 4.260, sd = 1.391) has moderately high scores, indicating that participants exhibit sociable and energetic behaviors. neuroticism (m = 3.172, sd = 1.331) and agreeableness (m = 3.294, sd = 1.387) show a lower means and increased variability, which suggest greater variation of participants emotional stability and agreeableness levels. this is aligned with other studies conducted by kurnia et al. (2023) that show that there is a crucial link between personality traits and financial self-efficacy because it investigates how individual personality traits affect one’s trust in managing financial activities. additionally, the study looked at the personality traits, financial behavior, and investment goals of young indonesians who delved into cryptocurrency. the results suggest that the relationship between personality traits, financial self-efficacy, and the purpose of investing in digital currency is statistically significant. the cramér-von mises test, which tests for goodness-offit, reports significant p values (all p < 0.001), indicating that the distributions of all variables significantly deviate from normality. these results suggest potential nonnormality in the underlying population traits, which is a common occurrence in psychological data. the findings are consistent with studies such as obenza et al. (2023) personality traits and financial well-being of college students in davao city. wherein comparable personality trait patterns were shown to be associated with financial behaviors among different groups, showing the importance of conscientiousness and openness in predicting personal financial patterns. the study examined determining factors of personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism) on the financial well-being of college students in davao city. table 1: descriptive statistics and cramér-von mises test mean standard deviation n cramér-von mises test statistic cramér-von mises p value financial self-efficacy 3.854 0.556 296 0.129 0.045 openness 4.287 1.498 296 1.831 0.000 conscientiousness 4.848 1.118 296 2.585 0.000 neuroticism 3.172 1.331 296 1.306 0.000 intercept 0.000 0.000 296 24.667 0.000 agreeableness 3.294 1.387 296 1.205 0.000 extraversion 4.260 1.391 296 1.596 0.000 this research used the variance inflation factor (vif) in the multicollinearity diagnostics part, this is to guarantee that independent variables (personality traits) are not overly correlated with each other. as per discussed in table 2, the vif values for all the personality traits variables are varied from 1.104 to 1.216, which are significantly below the recognized limit of 10, indicating that there are no significant multicollinearity difficulties. the conscientiousness (vif = 1.216) variable has the highest vif value, while agreeableness (vif = 1.104) has the lowest vif value, confirming that each personality trait correlates independently to determine financial selfefficacy. as stated by jamal daoud (2017) implies that if any of the vif values exceeds 5 or 10, this indicates that the associated regression is poorly estimated. multicollinearity is shown if one or more of the variables are small (almost zero) and the corresponding condition number is large. vif values that are greater than five are indicative of probable collinearity issues among the predictor constructs, as stated by hair et al. (2019). in an ideal situation, the values of the vif should be close to three or lower. the creation of higher-order models that are capable of being supported by theory is a common the anova results in table 3, with an f-value of 51.284, strongly indicate that the personality traits being studied (openness, extraversion, consciousness, neuroticism, and agreeableness) have varying effects on financial self-efficacy (fse) among university college students. the regression analysis exhibits a significant insight on the relationship between personality traits and financial self-efficacy. the model demonstrates a strong predictive capability, with r-squared values indicating a substantial proportion of variance in financial self-efficacy as explained by the personality traits. table 2: variance inflation factor vif openness 1.145 conscientiousness 1.216 neuroticism 1.134 agreeableness 1.104 extraversion 1.186 solution that is utilized in situations where collinearity is a problem (hair et al., 2017b). pa ge 44 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 41-46, 2025 openness emerges as the strongest positive predictor (b = 0.123, β = 0.333, p < 0.001). this suggests that individuals who are open to new experiences and ideas are more likely to exhibit higher financial self-efficacy. this aligns with existing literature that emphasizes the adaptive advantages of openness, including greater receptivity to financial learning and innovation. the substantial standardized coefficient indicates that openness has a significant and meaningful impact on financial confidence, reinforcing the idea that personality traits can influence financial behaviors. conscientiousness also significantly predicts financial self-efficacy (b = 0.118, β = 0.236, p < 0.001). the positive correlation supports previous research that links conscientiousness with effective financial management and planning. conscientious individuals, characterized by their organization and diligence, may approach financial tasks with more commitment and thoroughness, thereby enhancing their confidence in managing financial matters. on the other hand, neuroticism (b = 0.083, β = 0.198, p < 0.001) presents a nuanced relationship. while traditionally associated with lower self-efficacy in various domains, the positive correlation with financial selfefficacy suggests that individuals with higher levels of emotional instability may paradoxically develop greater confidence in their financial abilities. this could reflect a compensatory mechanism where individuals strive to gain control over their finances in response to their anxiety, warranting further investigation to clarify this counterintuitive finding. table 3: analysis of variance sum square df mean square f p value total 91.616 295 0.000 0.000 0.000 error 48.623 290 0.168 0.000 0.000 regression 42.993 5 8.599 51.284 0.000 figure 1: regression analysis results from smartpls conversely, agreeableness is a significant negative predictor of financial self-efficacy (b = -0.147, β = -0.367, p < 0.001). this indicates that individuals who prioritize cooperation and harmony may feel less confident in their financial decision-making. such findings could imply that those with high agreeableness might avoid confrontational financial decisions or hesitate to prioritize their financial interests, ultimately affecting their self-efficacy. lastly, extraversion does not show a significant relationship with financial self-efficacy (b = 0.028, β = 0.070, p = 0.135). this suggests that being outgoing and sociable may not necessarily translate to greater confidence in financial matters. it highlights that financial self-efficacy is more closely linked to other personality traits that drive responsible financial behavior rather than sociability. table 4: unstandardized and standardized coefficients unstandardized coefficients standardized coefficients se t value p value 2.5 % 97.5 % openness 0.123 0.333 0.017 7.264 0.000 0.090 0.157 conscientiousness 0.118 0.236 0.023 5.009 0.000 0.071 0.164 neuroticism 0.083 0.198 0.019 4.336 0.000 0.045 0.120 pa ge 45 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 41-46, 2025 the significant roles of openness, conscientiousness, and agreeableness underscore the complexity of financial confidence and behavior, suggesting that interventions aimed at enhancing financial self-efficacy may benefit from incorporating personality assessments. further research is encouraged to explore the underlying mechanisms driving these relationships, particularly regarding neuroticism’s unexpected positive correlation. efficacy. openness, conscientiousness, and neuroticism positively contribute to higher financial self-efficacy, while agreeableness has a negative impact. the finding that extraversion does not significantly predict financial self-efficacy suggests that financial confidence is more strongly related to internal traits like openness and conscientiousness than external social behaviors. these results offer valuable insights for personal financial planning interventions, suggesting that fostering openness and conscientiousness in individuals could enhance their financial confidence. furthermore, the significant negative relationship between agreeableness and financial self-efficacy suggests that individuals who are highly cooperative may need tailored interventions to boost their financial confidence. the study advances the understanding of how personality shapes financial behaviors, with implications for both psychological theory and practical applications in financial education and counseling. future research could explore the mechanisms behind the observed relationships and examine potential moderating factors, such as socioeconomic status or education level, to provide a more nuanced understanding of financial selfefficacy determinants references abadi, m. k. r., & annuar, h. a. b. 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(2024). pengaruh self-efficacy terhadap perilaku keuangan mahasiswa. owner, 8(3), 2968– 2980. https://doi.org/10.33395/owner.v8i3.2351 agreeableness -0.147 -0.367 0.018 8.165 0.000 -0.183 -0.112 extraversion 0.028 0.070 0.019 1.501 0.135 -0.009 0.065 intercept 2.859 0.000 0.164 17.482 0.000 2.537 3.181 table 5: model fit and explained variance financial self-efficacy r-square 0.469 r-square adjusted 0.460 durbin-watson test 1.935 the table 5 model fit and explained variance projects 46.9% of the variance in financial self-efficacy, as indicated by the r-squared value (r²=0.469). the adjusted r-squared value of 0.460 suggests minimal shrinkage, demonstrating that the model performs well when generalized to other data. the substantial r-squared value confirms that the selected personality traits account for nearly half of the variance in financial self-efficacy, underscoring the importance of these traits in predicting financial behaviors. additionally, the durbin-watson statistic of 1.935 suggests no autocorrelation in the residuals, supporting the assumption of independent errors. this study offers evidence supporting this idea in the field of education. while research into student selfefficacy has yielded mixed results regarding gender differences (byrne et al., 2014) and prior high school learning, the overall link between self-efficacy and academic performance is generally affirmed. according to dogan (2015) and patricia et al. 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(2023). the mediating role of college students’ self-efficacy: the relationship between the big five personality traits and entrepreneurial intention. advances in educational technology and psychology, 7(12). https://doi.org/10.23977/ aetp.2023.071217 pa ge 1 pa ge 46 american journal of financial technology and innovation (ajfti) the role of knowledge management processes in enhancing the quality of banking information systems: an applied study in the iraqi islamic banking sector mustafa mohammed kleban zuhairi1, mustafa khudhair hussein1* volume 1 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: december 04, 2023 accepted: december 26, 2023 published: december 30, 2023 the study aimed to identify the role of knowledge management processes in enhancing banking information systems in iraqi islamic banks (iraqi islamic bank for investment and development, national islamic bank, al-elaaf islamic bank, islamic cooperation bank for investment, al-ataa islamic bank for investment and financing). the total number of employees in these banks was 2,300 across all branches. a random sample representing senior, middle, and executive management was selected, totaling 320 individuals. various statistical methods were used to achieve the study objectives, relying on structural equation modeling using the smartpls3 software. the study reached several results, the most important of which was the statistically significant relationship. it was found that islamic banks in iraq have shown an acceptable level of attention to knowledge management processes. however, they still need to exert more effort in practicing knowledge generation, storage, and sharing to activate and enhance banking information systems. the study recommends that islamic banks in iraq provide continuous training and educational programs for employees to increase awareness of the importance of knowledge management and enhance effective practices in knowledge generation, storage, and sharing. furthermore, the study suggests conducting future research by introducing other variables, such as financial technology and artificial intelligence, to enhance banking information systems keywords knowledge management processes, banking information systems introduction knowledge management processes can be vital in enhancing the quality of banking information systems in iraqi banks. when knowledge is effectively managed, the knowledge and expertise related to banking systems can be identified, documented, and shared among employees, which helps improve the quality of information systems in banks. additionally, knowledge management processes can help enhance organizational learning and improve decision-making processes in banks. banks can provide training and development for employees on effectively using banking systems and improving internal processes to maximize the benefits of banking systems. (sajjad, 2023) knowledge management processes can also improve the quality of banking services and customer satisfaction. banks can use knowledge and customer-related expertise to identify their needs and provide customized banking services according to their requirements. (koshelieva et al., 2023) given the crucial role played by knowledge management processes in enhancing the quality of banking information systems and improving the quality of banking services, iraqi banks should give significant attention to effectively implementing knowledge management processes and regularly updating their banking systems to meet customer requirements and achieve more success and sustainability in the market. (adetayo et al, 2020) knowledge management processes involve collecting, organizing, distributing, and efficiently utilizing knowledge, which can positively impact the quality of banking information systems. improving the quality of banking information systems can increase the efficiency of banking operations and reduce the risks of errors and fraud. a critical study in this field is conducted by researchers (al-quran et al., 2023) titled “an empirical study on operating banks in jordan.” in this study, researchers found a positive relationship between knowledge management and the quality of banking information systems, indicating that implementing knowledge management practices can improve the quality of information systems. on the other hand, some studies have indicated the absence of a positive relationship or a significant impact of knowledge management processes on the quality of banking information systems. for example, a survey by siregar, t., & nuryatno, m. (2023) found no direct effect of knowledge management on the quality of information systems in the banking services industry. however, knowledge management can help improve the quality of information systems by enhancing organizational learning and decision-making processes. therefore, any institution operating in the banking services industry should focus on strengthening organizational learning and improving decision-making processes to enhance the quality of information systems. another study by torabi, f., & el-den (2017) indicated a weak and insignificant relationship between knowledge management and the quality of information systems in iran’s banking services industry. hence, institutions operating in this sector should work on strengthening 1 imam a’adhum university college, iraq * corresponding author’s e-mail: mustafakhudair87@gmail.com pa ge 47 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 the relationship between knowledge management and the quality of information systems. additionally, a study by santhosh and lawrence (2023) suggests that knowledge management has little impact on the quality of information systems in the banking services industry. therefore, institutions in this sector should improve knowledge management and identify the factors that affect the quality of information systems. these conflicting results highlight the need for further research and intellectual debate to establish the relationship between the variables under investigation, mainly when applied in the iraqi context. iraq requires such research due to its political and economic instability, particularly in the banking sector, which is still emerging and lagging compared to developed countries. hence, the researchers embarked on the current study to explore the relationship between knowledge management processes and the quality of banking information systems in islamic banks operating in iraq. the research problem revolves around determining the role of knowledge management processes in enhancing the quality of banking information systems, and the following questions arise: • what is the level of knowledge management practices in the researched banks? • what is the quality of banking information systems in the researched banks? • is there a statistically significant relationship between knowledge management processes and banking information systems?” the importance of research research is one of the essential topics in management and banking sciences that contribute to the development of the iraqi banking sector. the importance of the variables under investigation and their role in enhancing the effectiveness of banking operations and developing the economic sector is evident. the focus is on the quality of banking information systems to improve the reality of banking operations and protect their systems from breaches and risks associated with using information systems. on the other hand, it contributes to enhancing the quality of the decision-making process by providing information to operational and investment decisionmakers in the researched banks. research objectives • attempt to identify the level of knowledge management processes in the researched organization. • determine the level of activation and interest of the researched organization in banking information systems. • measure the statistical relationship between knowledge management processes and banking information systems. methodology based on the research methodology, its importance, and objectives, we can formulate the research plan and illustrate the relationship between the research variables as follows (figure 1): independent variable knowledge management processes are represented by the three dimensions (knowledge generation x1, knowledge storage x2, knowledge sharing x3). ( al-dmour, r., & rababeh,2021) dependent variable banking information systems are represented by the dimensions (information retrieval speed y1, system suitability y2, information security y3, user satisfaction y4) (abualoush et al., 2018) figure 1: research model research population and sample the research population consists of employees in iraqi islamic banks, totaling 29 banks. a sample was taken from these banks, specifically from 5 banks: iraqi islamic bank for investment and development, national islamic bank, al-ilaaf islamic bank, islamic cooperation bank for investment, and al-ataa islamic bank for investment and finance. these banks’ total number of employees is 2,300, spread across all branches. a random sample was selected, representing the top, middle, and executive management, with 320 employees, based on the equation (steven et al., 2012). n = (n*p (1-p))/[n-1*(d2 + z2)] + p (1-p) data collection tools the researchers relied on literature and previous studies related to the variables of the current study. a questionnaire consisting of 28 items was designed and divided into the researched variables. the variable of knowledge management processes consists of 12 pa ge 48 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 items distributed across three dimensions (knowledge generation, knowledge storage, and knowledge sharing), with four items for each size. the variable of banking information systems consists of 16 items, composed of four sub-dimensions (information retrieval speed, system suitability, information security, user satisfaction), with four items for each sub-dimension. statistical analysis methods for data the researchers relied on a set of statistical methods (validity and reliability), mainly to test the suitability and quality of the measurement tool. structural equation modeling was adopted using the statistical application smartpls 3 and path analysis. literature review knowledge management processes many authors and researchers have discussed the concept of knowledge, including the organization’s ability to deal with data and information and use them correctly to achieve goals (abubakar et al., 2019). knowledge management processes refer to interconnected activities and specific tasks defined by the organization to facilitate knowledge creation and utilization. it is a continuous and sustainable process of transforming knowledge into various forms, where knowledge management processes support the conversion of tacit knowledge into explicit knowledge (alewine et al., 2016). khaled et al. (2019) stated that knowledge management is the coordinated efforts of the organization to collect, classify, store, and prepare all types of knowledge related to the organization’s work for sharing among employees, thus supporting effective decision-making. importance of knowledge management processes researchers have addressed the importance of knowledge management processes, focusing on the following points (torabi et al., 2017): • we are enhancing the organizational knowledge power and contributing to achieving excellence through growth, survival, and continuity. • they are essential processes in the functioning and sustainability of organizations, as they are involved in all organizational activities. • we provide organizations with greater adaptability to the surrounding environment, focusing on knowledge generation, storage, and utilization. • they are contributing to improving performance and achieving goals. • we are providing the necessary support for organizations’ fundamental and intellectual capabilities, thus enhancing resilience and knowledge enrichment. dimensions of knowledge management processes there are variations in researchers’ opinions regarding a specific set of knowledge management processes. some of them have identified the following dimensions: [please provide exact dimensions or refer to the literature you want to be summarized] knowledge generation and acquisition it refers to a set of activities carried out by organizations to discover and acquire knowledge from internal and external sources, as well as generate knowledge through research and exploration processes (adetayo et al., 2020). this process requires identifying and diagnosing understanding, acquiring it, and then working on creating it according to the organization’s tasks. afterward, the necessary strategies for knowledge management are developed to ensure its continuous flow and attraction from internal and external sources (siregar et al.; m., 2023). knowledge storage (khaled et al., 2019) emphasized the importance of storing and preserving newly acquired knowledge for easy access and future use. storage itself involves capturing knowledge, encoding it, and making copies. (al-dmour, r., & rababeh, 2021) views the storage process as a way to preserve invaluable assets that cannot be bought with money but accumulate over time. knowledge sharing it is the process of disseminating knowledge, whether implicit or explicit, by organizations or individuals to other organizations and individuals. knowledge sharing can be categorized into two types: intentional, which is deliberate and takes place through specified official channels, and unintentional, which occurs informally outside working hours among employees in their organizations. knowledge sharing directly depends on organizational culture, managerial behavior, and communication rules (sajjad, 2023). it is worth noting that some authors and researchers have added other knowledge management processes (knowledge organization, knowledge application, knowledge utilization, knowledge transformation, and knowledge protection). the research agrees with (santhose & lawrence, 2023) that knowledge management processes revolve around three dimensions (knowledge generation, knowledge storage, and knowledge sharing) banking information systems banking information systems are the cornerstone of banking operations, competing to provide diverse services in a rapidly changing and advanced technological environment. they focus on information and the services that banks offer their customers, aiming to achieve excellence for the banks and their operations (nurdin, 2019). adel (2015) defines banking information systems as activities that support decision-makers and managers with information related to all banking operations, facilitating decision-making processes and achieving the desired performance. alewine et al. (2016) also state that banking information systems contain all the information about bank customers and their accounts. these systems help eliminate paper-based data, promote collaboration, and facilitate information access across different computer applications to achieve the desired goal. pa ge 49 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 importance of banking information systems banking information systems play a crucial and strategic role in building the competitive advantage of banks, directly relying on accurate information (sajjad, 2023). banking information systems serve as a customer attraction tool, as most customers prefer dealing with banks that provide excellent and advanced services. they also assist managers in making the right decisions based on timely and accurate data and information. based on the research above and studies, the importance of knowledge management processes in enhancing and activating banking information systems can be emphasized. the following hypotheses can be formulated: h1: knowledge management processes and banking information systems have a statistically significant relationship. h2: there is a collectively statistically significant impact of knowledge management processes on banking information systems. hypothesis testing h1: knowledge management processes and banking information systems have a statistically significant relationship. table (1) shows the correlation test based on the pearson coefficient. the results of the above table indicate the presence of table 1: pearson correlation coefficient x1 x2 x3 x y1 y2 y3 y4 y x1 1 x2 .543** 1 x3 .744** .593** 1 x .892** .788** .903** 1 y1 .602** .474** .648** .670** 1 y2 .631** .531** .741** .733** .625** 1 y3 .656** .560** .703** .736** .580** .738** 1 y4 .568** .484** .667** .667** .568** .728** .653** 1 y .727** .583** .801** .817** .770** .886** .881** .846** 1 a statistically significant positive relationship between all dimensions and variables of the study. therefore, we accept the first hypothesis of the study (h1). after confirming the critical relationship between the study variables, we proceed to test the other research hypotheses, which aim to examine the impact and level of influence between the variables under investigation, as stated in the following hypotheses: h2: knowledge management processes have a statistically significant impact on the quality of banking information systems. this hypothesis branches into the following sub-hypotheses: h2-1: the knowledge generation dimension has a statistically significant impact on the quality of banking information systems. h2-2: the knowledge storage dimension has a statistically significant impact on the quality of banking information systems. h2-3: the knowledge-sharing dimension has a statistically significant impact on the quality of banking information systems. the first study model, as shown in figure (2), illustrates the testing of the second central hypothesis (h2): the impact of the independent variable “knowledge management processes” on the dependent variable “quality of banking information systems,” as depicted in figure (1) below. figure 2: knowledge management processes on the dependent variable quality of banking information systems. model quality measurement to ensure the quality of the model, a set of steps must be taken. firstly, we need to confirm the outer loading, which should be at least 0.70. all model items have levels above 0.70, indicating excellent saturation. to further ensure, we check for the absence of linear correlation using the variance inflation factor (vif), which should be at most 3 (some sources suggest not exceeding 5). all values are below 3, which falls within acceptable limits. we also verify the value of cronbach’s alpha, which appears pa ge 50 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 to be 0.846 for the variable “knowledge management processes” and 0.868 for the dimension of “quality of banking information systems.” these high values indicate a strong positive correlation and high-scale stability. additionally, the value of composite reliability (c.r) reached 0.907 for the variable “knowledge management processes” and 0.910 for the variable “quality of banking information systems,” which are higher than the standard value of 0.70. furthermore, the average variance extracted (ave) values for the variables “knowledge management processes” and “quality of banking information systems” are 0.765 and 0.718, respectively, both exceeding the standard weight of 0.50. the standardized root mean square residual (srmr) value is 0.068, which is less than 0.09 and falls within the criterion of good fit. after confirming the quality and suitability of the study model, we will test the level of influence between the researched variables (knowledge management processes on the quality of banking information. table 2: k. m. processes k. m. processes latent variables outer loadings vif construct reliability and validity srmr x1 0.889 2.325 cronbach’s alpha 0.846 c.r 0.907 (ave) 0.765 0.068 x2 0.818 1.721 x3 0.914 2.514 banking information systems. y1 0.776 1.673 0.868 0.910 0.718 y2 0.900 2.908 y3 0.856 2.207 y4 0.852 2.248 the table above results indicate a statistically significant positive effect of knowledge management processes on the quality of banking information systems in the surveyed banks, with a coefficient of 0.825. this means that a oneunit change in the knowledge management processes variable will result in an 82.5% change in the quality of banking information systems, with a significance level of p=0.000, which is less than 1%. the determination coefficient (r2) is 0.681, indicating that the knowledge management processes variable explains 68% of the variation in the quality of banking information systems. therefore, we can accept the second central hypothesis h2: knowledge management processes have a significant effect on the quality of banking information systems. after testing the main hypotheses of the study, it is necessary to try the sub-dimensions of knowledge management processes and their impact on the quality of banking information systems, according to the following hypotheses: h2-1: the knowledge generation dimension has a significant effect on the quality of banking information systems. h2-2: the knowledge storage dimension has a significant effect on the quality of banking information systems. h2-3: the knowledge-sharing dimension significantly affects the quality of banking information systems. the second structural model will be used to test the above hypotheses, as shown in figure (4), which aims to test the sub-dimensions of knowledge management processes on the quality of banking information systems. first, measuring model quality: to ensure the quality of the model, a series of steps should be taken. first, ensure that the outer loading saturations of the items are not less than 0.70. it is noted that all model items have levels above 0.70, which is excellent, as shown in figure 4 above and table 4 below. to further confirm, we ensure no linear correlation through the variance inflation factor figure 3: the overall impact of knowledge management processes on banking information systems table 3: first structural path analysis path coefficients β r2 (stdev) t p values knowledge management processes -> banking information systems 0.825 0.681 0.016 52.667 0.000 pa ge 51 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 (vif), which should not exceed 3. while some sources also indicate that it should not exceed 5, we observe that all values do not exceed 3, which is within acceptable limits. we also confirm the value of cronbach’s alpha, which ranges from 0.822 to 0.868 for all dimensions. these are high values indicating good correlation and high-scale stability. in addition, the value of c.r. ranges from 0.882 to 0.901, which is higher than the standard value of 0.70. furthermore, the value of ave is 0.765, and the variable of the quality of banking information systems ranges from 0.652 to 0.718. all discounts are more significant than the standard value of 0.50. the value of srmr is 0.064, which is less than 0.09 and falls within the criterion of good fit. figure 4: the quality of banking information systems, second structural model table 4: results of measuring the quality of the second study model variables latent variables outer loading vif cronbach's alpha c.r (ave) srmr generating knowledge x1 0.801 1.739 0.834 0.889 0.668 0.064 x2 0.853 2.224 x3 0.833 2.195 x4 0.781 1.783 store knowledge x5 0.869 2.570 0.853 0.901 0.697 x6 0.908 2.159 x7 0.833 2.049 x8 0.717 1.465 share knowledge x9 0.778 1.630 0.822 0.882 0.652 x10 0.847 1.950 x11 0.832 1.889 x12 0.772 1.566 banking information systems. y1 0.776 1.673 0.868 0.910 0.718 y2 0.901 2.908 y3 0.855 2.207 y4 0.854 2.248 after confirming the suitability and quality of the study model, we will test the structural equation modeling to test the sub-hypotheses. as shown in table (5) and figure (5) below, the second study model aims to test the impact of the sub-dimensions of knowledge management processes on the quality of banking information systems. after confirming the suitability and quality of the study model, we will test the structural equation modeling to test the sub-hypotheses. as shown in table (5) and figure (5) below, the second study model aims to test the impact of the sub-dimensions of knowledge management processes on the quality of banking information systems. pa ge 52 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 databases, content management systems, and internal communication tools can be used to write and organize critical expertise for the bank and facilitate access when needed. 3. they encourage collaboration and knowledge sharing among employees by creating a work environment that promotes communication and knowledge exchange. interactive sessions, opportunities for joint learning, and regular exchange of ideas and experiences can be organized between different teams and departments. 4. islamic banks should invest significantly in advanced information technology that supports knowledge management processes. content management systems, internal social networks, tools for analysis, and machine learning can be used to enhance knowledge generation and sharing processes and facilitate access to knowledge. 5. senior leadership should be vital in promoting and implementing a knowledge management culture. the administration should be committed to providing necessary resources, support, and guidance to implement and enhance knowledge management initiatives as part of the bank’s strategy. limitations the current study relied on a sample of employees from iraqi islamic banks, with 320 participants. this means that the results of the present study cannot be generalized to the entire iraqi banking sector. additionally, the present study focused on only two variables: knowledge management processes and banking information systems. it is possible to introduce other variables, such conclusions islamic banks in iraq pay acceptable attention to knowledge management processes. still, they need to exert more effort in knowledge generation, storage, and sharing practices to activate and enhance their banking information systems. this can be achieved by creating a supportive culture and environment for knowledge management processes through training, continuous learning, and raising awareness about the importance of implicit and explicit knowledge. this is a helpful starting point for gaining a deeper understanding of the integration of information systems and knowledge management processes and their impact on the performance of iraqi islamic banks. this conclusion can serve as a starting point for managers responsible for implementing knowledge generation and employee-sharing initiatives. it positively reflects on enhancing banking information systems’ reliability, speed of accessing information, and relevance to user needs. recommendations 1. islamic banks in iraq should provide continuous training and educational programs for employees to increase awareness of the importance of knowledge management and enhance effective practices in knowledge generation, storage, and sharing. workshops, seminars, and internal and external training programs can be utilized to promote a knowledge-oriented culture and develop knowledge management skills among employees. 2. islamic banks should develop effective mechanisms to document implicit and explicit knowledge. centralized figure 5: analysis of the impact of the second study model table 5: a path analysis of the second study model path coefficients β r2 (stdev) t p values generating knowledge -> banking information systems 0.243 0.698 0.052 4.628 0.000 share knowledge -> banking information systems 0.157 0.042 3.746 0.000 store knowledge -> banking information systems 0.527 0.051 10.405 0.000 pa ge 53 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 46-53, 2023 as the role of financial technology in enhancing banking performance, or another variable as a mediator, such as organizational learning or artificial intelligence, to improve the relationship between the studied variables. reference abualoush, s. h., obeidat, a. m., tarhini, a., masa’deh, r. e., & al-badi, a. (2018). the role of employees’ empowerment as an intermediary variable between knowledge management and information systems on employees’ performance. vine journal of information and knowledge management systems, 48(2), 217-237. abubakar, a. m., elrehail, h., alatailat, m. a., & elçi, a. (2019). knowledge management, decision-making style, and organizational performance. journal of innovation & knowledge, 4(2), 104-114. adel, a. h. (2015). information systems integration and its impact on knowledge management processes. computer engineering and intelligent systems, adeniran, a. o., jadah, h. m., & mohammed, n. h. (2020). impact of information technology on strategic management in the banking sector of iraq. insights into regional development, 2(2), 592-601. https://doi. org/10.9770/ird.2020.2.2(7) al-dmour, a., al-dmour, r., & rababeh, n. (2021). the impact of knowledge management practice on digital financial innovation: the role of bank managers. vine journal of information and knowledge management systems, 51(3), 492–514. alewine, h. c., allport, c. d., & shen, w. c. m. (2016). how measurement framing and accounting information system evaluation mode influence environmental performance judgments. international journal of accounting information systems, 23, 28–44. al-quran, a. z., dalbouh, r. o. a., alshura, m. s. k., al-azzam, m. k. a., aldaihani, f. m. f., smadi, z. m. a., ... & alshurideh, m. t. (2023). impact of knowledge management on total quality management at private universities in jordan. in the effect of information technology on business and marketing intelligence systems (pp. 1725-1742). cham: springer international publishing. khaled, a., nader, m, aljawarneh., ziyad, saleh, alomari., rokaya, albdareen., ahmad, alawneh. (2019). innovations in knowledge management perspectives: an empirical study in the jordanian commercial and islamic banks. molecular microbiology, https://doi.org/10.21272/mmi.2020.4-08 koshelieva, o., tsyselska, o., kravchuk, o., baida, i., mironov, v., & miatenko, n. (2023). knowledge management as a new strategy of innovative development. international journal of professional business review, 8(5), e01592-e01592. nurdin, n. (2016). the roles of information technology in islamic bank knowledge management: a study of two syariah banks in palu. hunafa: jurnal studia islamika, 13(2), 181-217. https://doi.org/10.24239/ jsi.v13i2.444.181-217 sajjad, a. (2023). the effect of corporate governance in islamic banking on the agility of iraqi banks. journal of risk and financial management, https://doi. org/10.3390/jrfm16060292 santhose, s. s., & lawrence, l. n. (2023). understanding the implementations and limitations in knowledge management and knowledge sharing using a systematic literature review. current psychology, 1–16. siregar, t., & nuryatno, m. (2023). the impact of organizational climate, knowledge management, ethical leadership and quality of mais on firm performance. influence: international journal of science review, 5(1), 122–136. torabi, f., & el-den, j. (2017). the impact of knowledge management on organizational productivity: a case study on koosar bank of iran. procedia computer science, 124, 300-310. pa ge 1 pa ge 25 american journal of financial technology and innovation (ajfti) assessing the role of fintech in the economic growth and development in rwanda (2018-2023) munana mugisha salim1* volume 2 issue 1, year 2024 issn: 2996-0975 (online) https://doi.org/10.54536/ajfti.v2i1.2557 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 25, 2024 accepted: march 14, 2024 published: july 12, 2024 the study entitled “assessing the role of fintech in the economic growth and development in rwanda (2018-2023)” was conducted to assess the validity of one hypothesis, which was divided into two hypotheses: there is no significant role of fintech in economic growth of rwanda and the second is there is no significant role of fintech on economic development of rwanda. the study has used only secondary data. the indicators for hypotheses testing were assumed 3 indicators for the independent variable and two indicators under the dependent variable (see the conceptual framework). data were collected from the national institute of statistics (nisr), which reported national accounts, world bank data, and global economy data. data analysis was performed with the support of ms. excel and statistical package for social scientist version 20 (spss). data was presented in the form of descriptive statistics and inferential statistics (linear regression model). the conclusion of the study relies on the acceptance or failure to accept study hypotheses. the main study hypothesis was divided into hypotheses for easy data analysis, which has simplified and led to the provision of two hypotheses: one for the role of fintech on the economic growth of rwanda and the second on the role of fintech on the economic development of rwanda. data analysis generally has concluded by rejecting both null hypotheses, and the results made the study conclude that there is the significant role of fintech in economic growth and development of rwanda. however, going from indicator to indicator, there is insufficient evidence to confirm the correlation between the growth of several fintech start-ups in rwanda and the economic growth and development of rwanda as the correlation between these variables remains negative. in another case for all three variables, the coefficient table has provided no statistically significant relationship as all p-values are less than a 5% level of significance. this means that, for assessing the determinants of economic growth and economic development, there is a need to select more indicators or variables rather than choosing three indicators only as it is in this study. keywords assessing, role, fintech, economic growth, economic development introduction fintech, something else called web back or computerized monetary incorporation, essentially alludes to an amalgamation of back and data innovation. it constitutes installment and settlement, hazard administration, organizing channels, and asset assignment capacities. fintech has extended significantly within the monetary industry much obliged to the quick extension of the web, data innovation, versatile phones, and advanced advances. the budgetary administrations industry around the world has been changed by technology-enabled monetary services known as fintech. this troublesome innovation is reshaping money-related items, trade models, markets, and indeed the concept of cash itself, offering better approaches to gathering and utilizing information, making modern venture resources, and expanding inventive administrations. the progressing digitization of money related administrations and cash makes openings to construct more inclusive and proficient monetary administrations and advance financial improvement. to form it happen a recent world bank report, fintech and long haul of back, investigates the emotional changes within the budgetary administrations industry and underscores the require for policymakers and financial regulators to address unused challenges and back dependable advancement (kireyeva, 2021). in creating economies, there observed colossal advances in money-related administrations. there has been a marvelous increment within the share of grownups utilizing monetary accounts, which rose by 30 rate focuses between 2011 and 2021 to 71 percent, is somewhat inferable to fintech improvements such as versatile cash. the share of grown-ups making or accepting computerized installments developed to 57 percent in 2021 from 35 percent in 2014 concurring to the most recent circular of world bank findex information studies. usually extraordinary news for financial development and diminishing imbalance, destitution, and familiarity. for destitute individuals and little businesses without get to monetary administrations as fundamental as a bank account, fintech is opening an unused world of opportunity. fintech offers the capacity to send and receive installments safely and pick up get to reserve funds, credit, and protections items that can offer assistance grow businesses, relieve dangers, and arrange their prospects (nuguer, 2022). 1 faculty of business and media, philosophy in finance and economics, selinus university, rwanda * corresponding author’s e-mail: salim-013@live.com pa ge 26 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 this transformation has particularly benefitted ladies. owning advanced accounts boosts women independence and standing inside the family, as they can straightforwardly get to government installments and compensation, instead of depend on male relatives for control of family funds. computerized accounts have too given ladies more noteworthy get to credit, which has been appeared to assist destitute individual’s smooth vacillations in salary. take the illustration of ladies who once had to depend on day advances from advance sharks when they needed credit. these advances had intrigued rates of between 10 and 20 percent per day at some point indeed more. women can presently advantage from fintech administrations which give microloans at more competitive rates made conceivable by utilizing elective information and information analytics to survey her credit as long as appropriate shields are input. the fintech transformation is additionally lessening the costs of settlement administrations, a life saver for families in creating nations who are subordinate on budgetary offer assistance from relatives working overseas. the world bank settlements cost around the world information appears that the normal cost for sending $200 is around 6 percent over all sorts of suppliers, while the cost to send settlements through versatile cash administrations is beneath 4 percent. this implies more cash for families to spend on fundamental needs, such as nourishment, or wellbeing care and instruction (tyson, 2021). literature review for rwanda’s digital evolution to become transformational, the private sector needs to play a far greater role in spearheading digitization, through both increased technology adoption and support for innovation. so far digital adoption has been slow to permeate key sectors, and uptake among micro, small and medium scale enterprises (msme’s) has been modest. for example, greater merchant acceptance of dfs could help unlock further growth of fintech, and much more can be done to extend the benefits of digital financial services (dfs) to msmes more generally by incentivizing uptake. where msmes typically find access to credit to be a significant challenge, dfs can also offer a potential solution. bringing more msmes online can also increase opportunities for startups to offer digitally enabled business applications, as well as gradually increase local e-commerce (world bank group, 2020). as in numerous creating nations, versatile cash was advocated as a critical device of money related incorporation in sub-saharan africa. this consider endeavors to distinguish the components persuading rwandans to utilize the portable cash utilizing the finscope 2016 study information collected from an arbitrary test of 12,480 people. considering that receiving and utilizing portable cash is discretionary, the greatest probability strategy was utilized to assess an endogenous exchanging relapse show to account for test choice and indigeneity. the comes about put forward the part of financial components, riches and profitable resources on sparing advancement. versatile cash contributes essentially on sparing advancement; it is in this way a figure to boost the money related incorporation and a use point of financial advancement through the upgrade of comprehensive development. based on the investigate discoveries, it is suggested that investigating the components and techniques to put in put a cashless financial framework would make strides the financial change in rwanda (maniriho, 2021). song, n. (2022) has assessed the impact of fintech on economic growth: evidence from china. budgetary innovation (fintech) has seen quick advancement as of late in china; be that as it may, ponders investigating the commitments of fintech to china financial development stay constrained. in this way, this think about propelled by the information crevices and quick development of fintech inspected (i) the effect of fintech and the sub-measures of third-party installment, credit, and protections on china’s financial development; (ii) the territorial and common effect of fintech on china financial development; (iii) the causality connections between fintech and financial development. by using a sample of 31 provinces in china and the instrumental variable generalized method of moments (iv–gmm) technique, the study established the following: (i) fintech and the sub-measures of third-party payment, credit, and insurance have a statistically significant positive effect on china’s economic growth. specifically, a 10% rise in fintech, third-party payment, credit, and insurance raises china’s economic growth by 8%, 4%, 5%, and 16%, respectively; (ii) the eastern region has the highest growth effect of fintech. moreover, zhejiang province has the highest growth effect of fintech at the provincial level; (iii) a unidirectional causality exists from third-party payment and credit to economic growth and economic growth to insurance; a bidirectional causality exists between fintech and economic growth. song, n. (2022) study explicitly suggests substantial institutional reforms to promote the healthy development of fintech in china (song, 2022). hashem, et al. (2023) reveal the effect of fintech through money related advancement, budgetary consideration, and regulation quality on the comprehensive development of 25 creating nations in asia. to serve this reason, the human improvement list (hdi), the subordinate variable, has been taken as the intermediary for comprehensive development together with a set of autonomous factors in a well-balanced board information set, which is at that point analyzed to see the effect of changing levels of autonomous factors on human advancement for the period 2014-2021. the results about appear that expanding the level of fintech beside the findex, monetary incorporation, and regulation quality may increment human improvement (hashem, 2023). fintech (financial technology) plays a critical role in driving economic growth and development through its various contributions to the economy. here are some key ways in which fintech makes a significant impact: pa ge 27 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 increased financial inclusion fintech has been instrumental in expanding access to financial services, particularly in underserved and unbanked populations. by leveraging digital and mobile technologies, fintech firms have been able to reach individuals and businesses that were previously excluded from the formal financial system. this increased financial inclusion helps stimulate economic activity by providing more people with access to banking, lending, insurance, and investment opportunities, thereby enabling them to participate more fully in the economy (mashamba, 2023). enhanced efficiency and cost savings fintech innovations, such as digital payments, automated processes, and ai-driven analytics, have led to significant improvements in the efficiency of financial transactions and services. this, in turn, reduces costs for businesses and consumers, freeing up capital that can be deployed elsewhere in the economy. for businesses, streamlined and automated financial processes result in operational cost savings, while consumers benefit from lower fees, faster transactions, and improved access to affordable financial products and services (tyson, 2021). support for small and medium-sized enterprises (smes) fintech solutions have proven to be particularly beneficial for smes, providing them with access to financing, payment processing, accounting tools, and other essential financial services. by facilitating easier and more affordable access to capital and financial management tools, fintech contributes to the growth and sustainability of smes, which are vital drivers of economic activity and job creation in many economies. innovation and competition in financial services fintech has spurred greater competition and innovation within the financial services sector, challenging traditional institutions to improve their offerings and deliver more value to customers. this competition leads to better products, lower costs, and increased accessibility, ultimately benefiting consumers and businesses. the rise of fintech has also encouraged traditional financial institutions to innovate and modernize their operations, thus fostering a more dynamic and customer-oriented financial landscape (sheng, 2021). economic resilience and risk management fintech solutions contribute to enhanced economic resilience by enabling better risk assessment, management, and mitigation. through the use of advanced data analytics, ai-driven algorithms, and other technologies, fintech firms help identify and address financial risks more effectively. this, in turn, contributes to a more stable and resilient financial system, ultimately supporting broader economic stability (nuguer, 2022). facilitation of cross-border transactions and trade fintech has streamlined cross-border payments and trade finance, reducing barriers and costs associated with international transactions. by simplifying and accelerating cross-border payments and easing trade finance processes, fintech contributes to the expansion of global trade and commerce, fostering economic growth and international cooperation (mugabe, 2021). job creation and economic growth the growth of the fintech sector itself contributes to job creation and economic growth, providing employment opportunities for a wide range of professionals, including software developers, data scientists, financial analysts, compliance specialists, and customer support professionals. furthermore, as fintech firms serve as enablers of economic activity, their contributions to financial inclusion, innovation, and efficiency can have broader positive impacts on overall economic growth, productivity, and prosperity (nicole, 2021). overall, fintech’s impact on the economy is substantial and multifaceted, encompassing improved financial inclusion, increased efficiency and cost savings, support for smes, innovation and competition in financial services, economic resilience, facilitation of cross-border transactions, and job creation. these contributions collectively help drive economic development, enhance financial stability, and empower individuals and businesses to participate more fully and effectively in the modern economy (kireyeva, 2021). materials & methods this section is limited to the materials used for data collection and methods used for data processing and analysis. the study was limited to a few indicators (see the conceptual framework) and specific methods of data analysis, mainly a linear regression model. research design this study is a census design and uses data representing the whole country (rwanda); it is a descriptive design as presenting data using descriptive statistics parameters, and the study is correlative as using a linear regression model; the study gives a correlation between fintech and economic growth and development of rwanda. reserch design of investigate plan alludes to the generally procedure that you select to coordinated the distinctive components to consider in a coherent and coherent way, subsequently, guaranteeing you may viably address the investigate issue; it constitutes the outline for the collection, estimation, and investigation of information. population and sampling the population of the study is not limited as this study covers whole fintech services contributing in gross domestic product (gdp) of the country. and all pa ge 28 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 economic activities under financial sector contributing in rwanda gdp. in other case for economic development, the human development index was considered to represent this indicator and was taken to all rwandans. conceptual framework of the study figure 1: conceptual framework data collection tools and treatment the current study relay only on secondary data collection and analysis. the secondary data used are both quantitative and qualitative. qualitative secondary data was collected as literature of this study and quantitative data were collected as values obtained while measuring the indicators in the conceptual framework of the study. the main report visited to obtain quantitative secondary data is the rwanda national account reports of national institute of statistics (nisr), the global economy portal and world bank statistics portal. all information used are for the year 2018 to 2023 and for some indicators where 2023 was not reported, the change between 2021 to 2022 was assumed to be the same change for the year 2022 to 2023. data analysis data analysis was performed using both descriptive and inferential statistics. descriptive statistics were made by presenting indicators in their actual units from the original reports and modifications to the growth rates to ensure that, data are in the same format for easy analysis. meaning that, the information presented use descriptive statistical parameters like numbers and growth rates. for inferential statistics, the multilinear regression equation [2] assumed the following form: ŷ1&2= β0 + β1x1 + β2x2 + β3x3 + ε. where: ŷ1&2 = economic growth and development indicators by 2 indicators such as; gdp growth per capita rate and human development index. β0 = constant, x1 = growth of information communication and telecommunication , x2 = increase of mobile money subscribers in rwanda, x3 = growth of number of fintech start-ups in rwanda, and β1, 2, &3 = slopes associated with x1, x2, and x3, respectively. while ε = error term or the random disturbance term. study null hypothesis there is no significant role of fintech in the economic growth and development in rwanda. results & discussion results were made in form of descriptive and inferential statistics per each indicator as defined in the conceptual framework. table 1 and figure 2 show that gdp per head or per capita was increased from 2017 to 2018 at 5% and reduced to 2020 by 4% due to the high increase in value of dollar because in local currency it was increased, and increased 6% by 2021, 18% by 2022 and 15% from 2022 to 2023. all these indicators are not static or changing in regular way, in some years reduced and in some other years increases highly or moderately. this where for example from 2017 to 2018 mobile money subscribers in rwanda increased at 44% while by 2020 reduce 1%. mobile money accounts which is equivalent to mobile money subscribers is a big number even greater than rwandan total population table 1: mixed fintech and economic growth and development indicators indicators (all) 2018 2019 2020 2021 2022 2023 gross domestic product (gdp) (in billion rwf) 8,298 9,305 9,596 10,930 13,716 15,109 gdp per head (in current us dollars) 797 836 803 853 1,004 1,155 information & communication (in billion rwf) 144 185 194 215 201 201 financial services (in billion rwf) 206 225 220 281 369 457 mobile money subscribers in rwanda in millions 11.07 15.92 15.7 15.36 16.29 17.22 number of fintech start-ups in rwanda 42 44 44 44 44 44 human development index (ratio) 0.54 0.54 0.54 0.53 0.53 0.55 source: nisr, world bank, globe economy portals, 2024 pa ge 29 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 because it counts mobile money accounts from various institutions, individuals, businesses, etc. here for example, most people in rwanda are owning 3 to 4 sim cards and each is registered in the mobile money system (nicole, 2021). considering null hypothesis one stating that, there is no significant role of fintech on economic growth of rwanda, the findings prevail the following results: from table 2, an (r2) of 1 indicates that the regression predictions perfectly fit the data. this shows that, the analyzed model feet at 82% as (r2) is equal to 0.820. r is also equal to 0.906 meaning that, change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda each contribute 82% to the economic growth in rwanda as represented by gdp per capita. figure 2: change on mixt fintech and economic growth and development indicators from 2018 to 2023 table 2: model summary h01 model summary model r r square adjusted r square std. error of the estimate 1 .906a .820 .280 .07340 a. predictors: (constant), change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda table 3: anova table for the tested variables h01 anovaa model sum of squares df mean square f sig. 1 regression .025 3 .008 1.518 .024b residual .005 1 .005 total .030 4 a. dependent variable: gdp per head b. predictors: (constant), change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda table 3, the results show that the model had an f ratio of 1.518 and the p value was 0.024<0.05, signifying that the f ratio was statistically significant, therefore the overall regression model for all the variables tested were statistically significant and can be used for prediction at 5% significant level. this further indicate that the predictors variables change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda used in this study as indicators of fintech are statistically significant to the economic growth of rwanda. therefore, the formulated null hypothesis starting that there is no significant role of fintech on economic growth of rwanda was failed to be accepted in favor of alternative hypothesis or its opposite. table 4 gives the following linear equation: y1=0.008+0.750x1+3.042x2-31.604x3 this means that, there is a positive correlation between information & communication, mobile money subscribers in rwanda and negative correlation with change of fintech start-ups in rwanda toward the economic growth (gdp per capita). in other words, one unit change from the one above indicators (3 listed above) lead to change pa ge 30 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 of 0.750; 3.042 and -31.604 change times additional value to the current units of the gdp per capita of economic growth. in other words, once indicators of independent variable are absolute, the economic growth represented by gdp per capita is equal to 0.008 units. as conclusion the null hypothesis one: there is no significant role of fintech on economic growth of rwanda” is rejected in favor of alternative hypothesis “there is a significant role of fintech on economic growth of rwanda”. considering null hypothesis two stating that, there is no significant role of fintech on economic development of rwanda, the findings prevail the following results: table 4: coefficients table for linear regression analysis h01 coefficientsa model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) .008 .084 .094 .940 information & communication .750 1.350 1.155 .555 .677 mobile money subscribers in rwanda 3.042 2.239 6.705 1.358 .404 change of fintech start-ups in rwanda -31.604 26.593 -7.782 -1.188 .445 a. dependent variable: gdp per head table 5: model summary h02 model summary model r r square adjusted r square std. error of the estimate 1 .778a .605 -.581 .02587 a. predictors: (constant), change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda table 6: anova table for the tested variables h02 anovaa model sum of squares df mean square f sig. 1 regression .001 3 .000 .510 .044b residual .001 1 .001 total .002 4 a. dependent variable: human development index b. predictors: (constant), change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda from table 5, an (r2) of 1 indicates that the regression predictions perfectly fit the data. this shows that, the analyzed model feet at 60.5% as (r2) is equal to 0.605. r is also equal to 0.778 meaning that, change of fintech start-ups in rwanda, information & communication, mobile money subscribers in rwanda each contribute 60.5% to the economic development in rwanda as represented by the human development index. table 6, the results show that the model had an f ratio of 0.510 and the p value was 0.044<0.05, signifying that the f ratio was statistically significant, therefore the overall regression model for all the variables tested were statistically significant and can be used for prediction at 5% significant level. this further indicate that the predictors variables change of fintech startups in rwanda, information & communication, mobile money subscribers in rwanda used in this study as indicators of fintech are statistically significant to the economic development of rwanda represented by human development index. therefore, the formulated null hypothesis starting that there is no significant role of fintech on economic development of rwanda was failed to be accepted in favor of alternative hypothesis or its opposite. table 7 gives the following linear equation: y2=-0.017+0.269x1+0.770x2-8.329x3 this means that, there is a positive correlation between information & communication, mobile money subscribers in rwanda and negative correlation with change of fintech start-ups in rwanda toward the economic development (human development index). in other words, one unit change from the one above indicators (3 listed above) lead to change of 0.269; 0.770 and -8.329 change times additional value to the current units of the pa ge 31 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 than 5% level of significance. this means that, to assess the determinants of economic growth and economic development, there is a need to select more indicators or variables rather than choosing only three indicators, as is the case in this study. references finkelstein-shapiro, a., mandelman, f. s., & nuguer, v. (2022). fintech entry, firm financial inclusion, and macroeconomic dynamics in emerging economies. https://doi.org/10.18235/0003918 kireyeva, a., kredina, a., vasa, l., & satpayeva, z. (2021). impact of financial technologies on economic development: theories, methods and analysis. journal of international studies, 14(4), 286-303. https://doi. org/10.14254/2071-8330.2021/14-4/19 maniriho, a. (2021). mobile money for financial inclusion in rwanda application of endogenous switching regression model. ssrn electronic journal. https:// doi.org/10.2139/ssrn.3904897 mashamba, t., & gani, s. (2023). fintech, bank funding, and economic growth in sub-saharan africa. cogent economics & finance, 11(1). https://doi.org/10.1080/ 23322039.2023.2225916 mugabe roger,peter umaru kamara, aruma bakarr,abdulai babson turay. (2021). the link between financial development and economic growth: case of rwanda. international research journal of innovations in engineering and technology (irjiet), 5(2), 97-102. https://doi.org/10.47001/ irjiet/2021.502014 nicole masaryk, i. s. (2021). role of capital market in the economic growth of rwanda [master’s thesis]. https://is.muni.cz/th/a4930/master_thesis_ ishimwe_sambwe_nicole.pdf njenga, g., machagua, j., & gachanja, s. (2022). capital markets in sub-saharan africa. wider working paper. https://doi.org/10.35188/unuwider/2022/246-1 parvez, m. a., katha, e. h., shaeba, m. k., & hossain, m. s. (2023). fintech and inclusive growth: evidence from 25 asian developing countries. https://doi. org/10.56506/qmhr3332 rwanda economic update, january 2020. (2020). https:// doi.org/10.1596/33247 human development index or economic development. in other words, once indicators of independent variable are absolute, the economic development represented by human development index is equal to -0.17 units. as conclusion the null hypothesis one: there is no significant role of fintech on economic development of rwanda” is rejected in favor of alternative hypothesis “there is a significant role of fintech on economic development of rwanda”. the study results give confidence to the study to confirm that there is a significant role of fintech in the economic growth and development in rwanda, however based on the indicators selected, it was not for all indicators where growth of number of fintech start-ups in rwanda present negative correlation with economic growth and development of rwanda. in other case for all tested indicators, the role or correlation is not statistical significant as all p-values for specific indicators (3 from independent variable) present value greater than 0.5%. as explained by (hashem, 2023) economic growth and development are large components which cannot be explained by a single and small indicators as three above selected indicators. meaning that, considering a fintech as main engine for economic growth and economic development can mislead policy makers. conclusion the conclusion of the study relies on the acceptance or fail to accept study hypothesizes. the main study hypothesis was divided into hypothesis for easy analysis of data, and this has simplified and lead to provision of two hypotheses one for the role of fintech on the economic growth of rwanda and the second on the role of fintech on the economic development of rwanda. data analysis generally has concluded by rejecting both null hypothesis and the results made the study to conclude that, there is significant role of fintech on economic growth and development of rwanda, but going on indicator to indicator there is an insufficiency evidence to confirm the correlation between growth of number of fintech start-ups in rwanda on economic growth and development of rwanda as the correlation between these variables remain negative. in other case for all three variables, the coefficient table has provided none statistically significance relationship as all p-values are less table 7: coefficients table for linear regression analysis h02 coefficientsa model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) -.017 .029 -.582 .664 information & communication .269 .476 1.742 .565 .673 mobile money subscribers in rwanda .770 .789 7.133 .975 .508 change of fintech start-ups in rwanda -8.329 9.373 -8.621 -.889 .538 a. dependent variable: gdp per head pa ge 32 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 25-32, 2024 sheng, t. (2021). the effect of fintech on banks’ credit provision to smes: evidence from china. finance research letters, 39, 101558. https://doi.org/10.1016/j. frl.2020.101558 song, n.; appiah-otoo, i. school of management and economics, university of electronic science and technology of china, chengdu 610054, china. (2022). the impact of fintech on economic growth: evidence from china. mdpi, 14(6211). https://doi. org/10.3390/su14106211 soumaré,the odi research series for financial development in africa. (2021). capital market development in sub-saharan africa: progress, challenges and innovations. pa ge 1 pa ge 12 8 american journal of financial technology and innovation (ajfti) effect of customs monitoring system on trade facilitation in busia border, kenya stanley kamau1*, bruce oganga2, naomi koske3 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3651 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: august 25, 2024 accepted: october 01, 2024 published: august 07, 2025 customs is the backbone of the economy of any country. customs is a crucial point of the global supply chain incorporated solutions as they are responsible for collecting revenues, safeguarding society and flow-protecting channels. the activity to exchange goods and services has been omnipresent globally, as the world transforms into a global village connected by common borders. the main purpose was to determine the effect of customs management systems on trade facilitation in busia border, kenya. the study was guided by the technological change theory. the study adopted an explanatory research design. the target population was 137 clearing and forwarding agents with an 83% response rate. this study used primary data, which was collected using structured questionnaires. the study further found that those customs monitoring system had a significant and positive effect on trade facilitation β=0.347 p<0.05. the kra is also recommended to provide ongoing training for customs officials to ensure they are adept at using new systems and can effectively manage the cargo clearance process. keywords busia border, customs monitoring systems, trade facilitation 1 tax and customs administration, moi university, kenya 2 kenya school of revenue authority, kenya 3 school of business and economics, moi university, kenya * corresponding author’s e-mail: stanleykamau49@gmail.com introduction customs duty is very crucial for every country’s economy. it is an important duty of customs administrations worldwide to carry out revenue collection, protection of society and chain security. furthermore, customs strive to facilitate trade and thereby promote investment and reduce poverty (wco 2019). but the 21st century pours all sorts of trouble on customs. these new emerging challenges now, more than ever demand an evolved response of the customs. this calls for appreciating matters relating to globalization, international trade dynamics, technology dimensions of the supply chain, emerging political trends and nuances in the global environment (gordhan, 2020). reports indicate that trade facilitation in kenya is poor despite the automation of various measures that are aimed at improving on the same. kenya is ranked 56 out of 190 countries in ease of doing business as of july 2020 according to the world bank (2021) report, as compared to rwanda (38) and morocco (53). this suggests that in kenya, automation has not been a significant contributor to trade facilitation performance contrary to the wto and oecd as well as empirical evidence which views automation as a powerful determinant of trade facilitation. it represents the importing and exporting procedures, especially the remediation of procedures to simplify, harmonize, standardize, and modernize trade. trade facilitation can also be defined, in a broader sense, as all activities surrounding the interface between business and government affect transaction costs (wto, 2015a). to achieve successful trade facilitation, the government plays a crucial role by providing essential legal frameworks, developing infrastructure, showing commitment, fostering goodwill, and endorsing various supporting agreements. busia, located on the border between kenya and uganda, serves as a vital international crossing point. positioned to the west of kenya and the east of uganda, it lies approximately 431 kilometers from nairobi, kenya’s capital, and 202 kilometers from uganda’s capital, kampala. consequently, busia has evolved into an important trade hub for both nations. key imports from uganda to kenya include goods such as cotton, timber, fish, bananas, pineapples, maize, beans, groundnuts, and sorghum. on the other hand, kenya exports petroleum products, manufactured goods, and household essentials like cooking oil, soap, clothing, electronics, and automobiles to uganda. the busia border handles the majority of trade and human movement, with significant activity involving pedestrian traffic, petroleum tankers, small-scale crossborder traders, and trucks transporting containerized cargo. these vehicles carry imports, exports, and goods in transit to neighboring countries such as rwanda, burundi, south sudan, and the democratic republic of congo. numerous governmental bodies function on either side of the border, such as customs, immigration, regulatory agencies, health and security departments, livestock and fisheries offices, agricultural authorities, the pharmacy and poisons board, plant health inspectors, and weighbridge operators. furthermore, significant border participants also include the east african community (eac) ministry, local county administrations, clearing and forwarding firms, small business associations, and transportation companies, among others (crown agents, 2020). problem statement trade faces numerous challenges, particularly the need for the swift movement of goods, which is often complicated by intricate regulatory demands. addressing these issues pa ge 12 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 128-134, 2025 calls for a modern, innovative approach to streamline processes and ensure efficiency. in 2022, 79% of people who imported goods experienced delays in facilitation. there are also many complaints about the compatibility of the computer system used in busia border post. over the last two decades, export trade has shrunk in size in kenya. following the fact that goods and services exports averaged 20 percent of gdp in the last two decades, its average share has fallen to 17.2 percent in the last 10 years before further shrinking to 13.9 percent in the last 5 years (oecd, 2020). equally, a snarl-up of cargo trucks at the busia border post because of the icms saw trade between kenya and uganda take a negative turn. reports available indicate that the trade facilitation in kenya is yet to realize expected results and targets; this creates doubt about the effectiveness of the automation measures towards enhancing trade facilitation. by july 2020 and according to the ranking by the world bank (2021), kenya was ranked 56 out of 190 in ease of doing business, trailing behind other countries such as rwanda and morocco, which are ranked 38 and 53, respectively. this becomes an indication that automation has not effectively contributed to the facilitation of trade in kenya, despite wto, oecd, and empirical evidence portraying automation as a major driver to performance in trade facilitation. literature review technological change theory the theory is described as encompassing the entire process of innovation, invention, and the spread of technology. initially developed by everett m. rogers (2015), it specifically addresses the customs administration’s adoption of the integrated customs management system. earlier approaches to technological change were based on the ‘linear model of innovation,’ which has largely been replaced by a more dynamic model. this newer model reflects technological change through innovation across all stages, from research and production to dissemination and practical application (tidd et al., 1997). technological advancements are often represented as part of a broader innovation process. this ongoing development is typically illustrated as a curve, indicating decreasing costs over time (coronado et al., 2018). in the area of customs management, there has been a significant shift in technology aimed at improving how customs operations function and facilitating trade more efficiently. over the years, customs systems have steadily embraced technological advancements. in 2005, the kenya revenue authority introduced the simba 2005 system, with assistance from the government of senegal. this system was part of a larger effort to modernize and reform customs operations. the department, which primarily deals with the import and export of goods and services, also happens to be the largest revenue generator in the customs division (mbui, 2021). simba 2005 was specifically designed to enhance the efficiency of the clearance and forwarding process by enabling electronic submissions of import and export documentation, simplifying the process for traders to lodge their information for clearance. in this context, kenya made significant progress in 2014 (djanitey, 2018) by introducing the electronic single window system. this system was designed to streamline and accelerate the process of cargo clearance across the country’s borders. the single window system represents kenya’s technological upgrade aimed at enhancing international trade by minimizing delays and reducing costs related to border clearance, while still ensuring proper controls and the collection of levies, charges, duties, and taxes on imports and exports as needed. these tailored solutions have been implemented to improve trade facilitation and lower the cost of conducting business. consequently, international trade processes, including imports and exports, transit procedures, and customs operations, are simplified, standardized, and automated, leading to increased trade efficiency. empirical literature review customs monitoring system and trade facilitation in romania, vatuiu and tarca (2021) observed that the recently introduced e-customs electronic system allows for real-time tracking of product advancements within the country. this technology has improved the customs authorities’ capacity to oversee and regulate trade involving excise cargo, especially in terms of duty deferrals. as a result, there has been a rise in revenue for the excise department and a decline in fraud cases, and romania has successfully fulfilled its european union requirements related to e-customs monitoring systems. bujak (2019) proposes that these systems could include a variety of functions, such as screening, electronic surveillance, weigh-in-motion at border crossings, automatic equipment identification, and credential management. mahlknecht and madani (2007) highlight that the main purpose of the electronic cargo tracking system is to guarantee the security and safety of the entire supply chain process. the integrated management and monitoring process begins with the consolidation and packaging of goods, which are then transported to the port. this process includes storage at the port if necessary, movement to container freight stations (yards), optional ship deck assessments, and drayage, concluding with unloading at either haulers or enduser warehouses. these various functions are typically supported by advanced monitoring systems. despite this, the system may not offer benefits in terms of low cost and flexibility for inter-modal supply chain management and security. kabiru (2020) points out that challenges in implementing transit monitoring systems include inadequate infrastructure, high implementation costs, insufficient training, and a lack of understanding of requirements. nevertheless, the tracking of goods from one border point to another has significantly reduced market dumping. pa ge 13 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 128-134, 2025 trade facilitation trade facilitation involves removing or reducing trade barriers and simplifying procedures at border stations. it is also defined as the process of streamlining the clearance of goods by ensuring that relevant border agencies have the necessary information, making the process more predictable and cost-effective. poor trade facilitation has significantly hindered international trade in many regions, leading to higher costs for goods and services. often, inefficiencies and bureaucratic obstacles imposed by government agencies at borders exacerbate these issues, further complicating international trade. sohn (2021) defined trade facilitation as “all activities or policies which reduce transaction costs arising from eliminating or simplifying excessive and complex procedures, practices and processes related to thus increasing efficiency, which results in increased trade. “trade facilitation has become increasingly a subject of interest globally due to the need for freedom of movement of goods and services resulting from growth in trade volumes that is directly attributed to worldwide liberalization of trade. the origin of trade facilitation and the prominence of the topic received as subject of negotiations at wto discussions was at the singapore ministerial conference of 1996 and in doha, where the doha development agenda was adopted by the ministers as a framework of the agreement (hoek et al., 2019). east africa’s trade would have recorded higher level of growth than it has now, had several factors that heavily impact on trade facilitation been looked into and addressed. these are, insufficient and bad roads, technology and bad governance. according to lima and venables, (2020). the degree of infrastructural challenges rise to approximately 40% of transport costs and to a high of 60% for landlocked countries. costs attributable to border inefficiencies are, low resource compatibilities where document processing systems are not perfectly compatible. for instance, kenya has the simba system 2004, while uganda has the asycuda world. such challenges limit the extend one can explore the market that has widened as a result of the east african community trade the region, yang & gupta, (2021), njinkeu et al. (2006) and forouton & princhet, (2020). an attempt has been made to have revenue authorities’ digital data exchange (raddex) as a platform for exchange of data from the two systems but this has not fully been exploited. conceptual framework a conceptual framework is a structure in diagrammatic form that is used to show the interaction between the variables (bogdan & biklen, 2013). it is used to represent how the variables of the study are to be measured or operationalized (bell et al., 2018). as indicated in figure 1, the independent variable was customs monitoring system was measured by tracking device and real time monitoring. dependent variable is trade facilitation measured by tax paid and volume of goods traded. materials and methods research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure (kothari, 2014). the study adopted an explanatory design research where gathering and collection of information was through the help of questionnaires. this design is appropriate for the study because it allows the researcher to generalize the findings to a larger population (schindler & cooper, 2003). the target population was 137 clearing and forwarding agents at busia border, kra (2023). the questionnaires were self-administered to the sampled respondents. the questionnaires had an introductory letter introducing the researcher to the respondents and explaining the purpose of the research. out of 137 respondents targeted, 114 questionnaires were correctly filled and returned. indicating 83% response rate. this response rate is considered satisfactory to make conclusions for the study. figure 1: conceptual framework source: researcher (2024) figure 2: response rate source: compile by author reliability analysis in order to test the reliability of the instruments, internal consistency techniques were carried using cronbach’s alpha. the alpha value ranges between 0 and 1 with reliability increasing with the increase in value. according to (mugenda, 2008), in this case customs monitoring system has cronbach’s alpha 0.979 and trade monitoring and cronbach’s alpha 0.923. the alphas value >0.7 indicates that the responses are highly reliable: data analysis, presentation, interpretation and discussion yin (2009) argues that data analysis as the process of edition and also the reduction of accumulated data to pa ge 13 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 128-134, 2025 table 1: test of reliability of questionnaire factor number of items cronbach alpha score conclusion trade facilitation 6 0.923 reliable customs monitoring system 5 0.979 reliable source: researcher, (2024) table 3: descriptive statistics for customs monitoring system n mean std. deviation skewness kurtosis use of customs monitoring system leads to a more efficient and individual container traceability 114 4.03 1.008 -.686 -.675 customs monitoring system allows real time monitoring of the status of product and goods movements 4.05 1.029 -.801 -.532 real time remote containers tracking and monitoring help to prevent losing track of container and goods 3.85 1.024 -.601 -.717 use of customs monitoring system has led to reduction in deterioration theft, diversion and counterfeiting 3.88 1.040 -.614 -.761 the tampered carrier can be inspected away from the destination port to prevent potential negative impact and potential destruction (when high jacked by terrorists 3.82 1.001 -.537 -.718 aggregate mean 3.93 (source: compiled by author from primary data) table 2: reveals the demographic analysis count percent % gender female 53 46.5% male 61 53.5% age 26 to 35 16 14.0% 36 to 45 33 28.9% 46 to 55 26 22.8% above 55 25 21.9% below 25 14 12.3% education degree level 47 41.2% diploma level 16 14.0% others 27 23.7% secondary certificate 24 21.1% (source: research 2024) manageable size, developing summaries, looking for patterns and applying statistical techniques. data collected was edited, cleaned and coded for completeness. cleaned data was then be analyzed using descriptive and inferential statistics. descriptive statistics including mean, standard deviation and co-efficient of variation (cv). the analytical model is denoted by the equation: y = β0 + β1x1 + ε where; y = trade facilitation (dependent variable); x1= customs monitoring systems; β1 = beta coefficient; ε = error term. results and discussion demographics analysis a demographic analysis was conducted and it reveal the distribution of participants across various categories. the gender distribution indicates that 53.5% of the respondents are male while 46.5% are female. in terms of age, the largest group falls within the 36 to 45 years range, accounting for 28.9% of the sample. this is followed by participants aged 46 to 55 years at 22.8% those above 55 years at 21.9%. participants aged 26 to 35 years at 14.0% and those below 25 years at 12.3%. regarding education levels, 41.2% of the participants hold a degree, 23.7% have other qualifications, 21.1% possess a secondary certificate, and 14.0% have a diploma. descriptive statistics for customs monitoring system use of customs monitoring system leads to a more efficient and individual container traceability has a mean score 4.03 and standard deviation 1.008. customs monitoring system allows real time monitoring of the status of product and goods movements has a mean score 4.05 and standard deviation 1.029. real time remote containers tracking and monitoring help to prevent losing track of container and goods has a mean score 3.85 and standard deviation 1.024. use of customs monitoring system has led to reduction in deterioration theft, diversion and counterfeiting has a mean score 3.88 and standard deviation 1.040. the tampered carrier can be inspected away from the destination port to prevent potential negative impact and potential destruction (when high jacked by terrorists has a mean score 3.82 and standard deviation 1.001. pa ge 13 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 128-134, 2025 descriptive statistics trade facilitation volumes of cargo traded across the border have increased,” the mean score is 3.59 with a standard deviation of 1.009. kra has offered an enabling environment for tax filing has a mean score of 3.98, the standard deviation is 1.004. companies file returns on time and as required by law,” the average score is 4.12, with a standard deviation of 0.973. the statement on submitting and handling customs declarations have become much simpler and clearer has an average score of 3.96 and a standard deviation of 0.959. the assertion that “lodging and processing of import declaration forms have greatly improved” has an average score of 4.28, with a standard deviation of 0.922. additionally, “the time required to declare goods to customs has notably decreased” has an average score of 4.05, with a standard deviation of 0.891. table 4: descriptive statistics for trade facilitation n mean std. deviation skewness kurtosis volumes of cargo traded across the border have increased 114 3.59 1.009 -.825 .088 kra has offered an enabling environment for tax filing 3.98 1.004 -.818 .105 companies file returns on time and as required by law 4.12 .973 -.839 .312 the submission and processing of customs declarations have been simplified and made more transparent 3.96 .959 -.930 .586 the submission and handling of import declaration forms have seen substantial improvement 4.28 .922 -.795 .550 the duration needed to declare goods to customs has decreased notably 4.05 .891 -.945 1.128 aggregate mean 3.99 (source: research 2024) table 5: represents the correlation statistics trade facilitation customs monitoring systems trade facilitation 1 0.573** customs monitoring systems 0.573** 1 **. correlation is significant at the 0.05 level (2-tailed). source: researcher, (2024) table 6: model summary model r r square adjusted r square std. error of the estimate 1 .573a .0.288 .281 .46171 a predictors: (constant), customs monitoring systems. correlations statistics of independent and dependent variable pearson’s correlation coefficients were evaluated to determine the strength of the association between the independent variables. a coefficient value nearing 1 indicates a stronger link between the variables. the analysis revealed that the customs monitoring system has a positive and notable relationship with trade facilitation, with a correlation of 57.3% and a p-value of 0.002, which is below the 0.05 threshold. this suggests that robust customs monitoring, which includes tracking and inspection processes, is crucial for facilitating trade. model summary the model summary was used to determine the correlation and variation caused on trade facilitation. customs monitoring system has a strong and significant impact on trade facilitation. the model summary from table 6 reveals that the customs monitoring system, collectively have a strong and significant impact on trade facilitation at 57.3%, explaining 28.8% of its variability. the remaining 71.2 of the variability was explained by factors not captured in the model, the adjusted r square 28.1% indicates that this relationship remains robust after accounting for the number of predictors. anova was employed to analyze whether the model significantly explains the variability caused on trade facilitation by customs management systems. table 7 indicates that f statistic of f-statistic =91.894 p-value =0.000<0.05. this implies that while the model accounts for a significant portion of the variance. the hypothesis pa ge 13 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 128-134, 2025 table 7: analysis of variance model sum of squares df mean square f sig. 1 regression 64.234 1 64.234 91.894 .000b residual 78.350 112 .699 total 142.584 113 a. dependent variable: trade facilitation b. predictors: (constant), customs monitoring system table 8: regression coefficients model standardized coefficients unstandardized coefficients t sig. 1 (constant) 0.633 0.310 2.042 0.040 customs monitoring systems 0.347 0.094 0.369 3.691 0.000 a. dependent variable: trade facilitation source: researcher, (2024) h01 stated that was those customs monitoring system has no significant effect on trade facilitation at busia border, kenya. the study found that customs monitoring system has a significant effect on trade facilitation at busia border, kenya. p-value =0.000<0.05. results and discussion the study was to determine the effect of customs monitoring system on trade facilitation in busia border, kenya. the study also through the coefficient analysis found that customs monitoring system has a positive and significant effect on trade facilitation. β = 0.347, p=0.000<0.05. this reinforces the conclusion that enhancements in the customs monitoring system are likely to have a meaningful and positive impact on trade facilitation. the study aligns with the findings of escap (2013), which highlighted that china and vietnam implemented mandatory satellite positioning systems on vehicles transporting hazardous goods and passengers. additionally, the monitoring and security of container movements in china, south korea, and thailand are managed through the use of electronic seals. conclusions a customs monitoring system has a significant and positive effect on trade facilitation. this underscores the importance of effective customs monitoring in enhancing trade facilitation. enhanced customs monitoring systems contribute to more streamlined and efficient trade processes. consequently, investing in and upgrading customs monitoring systems is essential for significantly improving trade facilitation at the busia border. recommendations the kra is recommended to provide ongoing training for customs officials to ensure they are adept at using new systems and can 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(2010). business research methods (8th ed.). mason, oh: cengage learning. pa ge 1 pa ge 1 american journal of financial technology and innovation (ajfti) research on the development direction of the industrial economy under the background of the digital economy fuqiang guo1*, changxia my1, zubritskaya inessa anatolyevna1 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3976 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: november 05, 2024 accepted: december 07, 2024 published: january 23, 2025 in this context, this paper introduces the digital economy under the background of industrial economy stage achievements, such as through the development of the digital economy and industrial economy integration and management and find the industrial economy intelligent transformation of three promotion path, and through the relevant research found that the industrial economy is facing development difficulties, so this paper discusses the digital economy to realize transformation and upgrading of industrial economic structure facing realistic problems, such as intelligent manufacturing, green sustainable development, personalization, cross-border integration innovation. finally, in view of the problem of how to further develop industrial economy under the background of digital economy, relevant countermeasures and suggestions on the development direction of industrial economy under the background of digital economy, hoping to provide effective suggestions for the transformation of industrial economy to digital and the realization of high-quality development. keywords ddigital eeconomy, highquality ddevelopment, iindustrial eeconomy 1 belarusian national technical university, minsk, 220013, pr, belarus * corresponding author’s e-mail: guo912823191@163.com introduction the rapid rise of the digital economy has become a new engine for global economic growth. digitization not only improves production efficiency and product quality, but also makes the production process more flexible and personalized. according to statistics, in the past five years (2014-2019), the overall scale of digital economy has increased from 16.16 trillion yuan to 35.8 trillion yuan, the total gdp has increased from 63.51 trillion yuan to 98.91 trillion yuan, while the proportion of digital economy in gdp has increased from 26.1% to 36.2%, as shown in figure 1. it can be seen that the digital economy is developing faster and faster, and its market size is also expanding, so we should accelerate the development of the industrial economy under the influence of the digital economy. take the market size of chinas digital economy market as an example. in 2008, the market size of the digital economy was only 4.8 trillion yuan, and increased to 27.2 trillion yuan in 2017, while in 2021, it grew rapidly to 45.5 trillion yuan, with a linear growth rate ( jianhui & guobin, 2023) as an important part of the national economy, the industrial economy is facing unprecedented challenges and opportunities. therefore, this paper will pay attention to the challenges and coping strategies of industrial economy in the era of digital economy, and discuss how to better use digital technology to optimize resource allocation, improve production efficiency and innovation ability, so as to promote the development of industrial economy to the direction of higher quality, more efficient and more sustainable. literature review the digital economy and the industrial economy figure 1.1: portion of chinas digital economy in gdp as the most significant form of economic development in the 21st century, digital economy takes digital information and technology as the core resources, and has changed the operation mode of the traditional economy. it not only optimizes the allocation of resources with high efficiency and popularization, but also promotes the diversified development of the economy through technological innovation. the core position of data in the digital economy urges enterprises to take user demand-oriented and realize the transformation from production-oriented to market-oriented. the distinctive feature of this economic form is its deep dependence on information technology and its ability to respond quickly to market changes. industrial economy, also called resource economy, that is, economic development mainly depends on the possession and allocation of natural resources. since the 19th century, developed countries in the world have successively completed the industrial revolutionin science and technology, tractors and machine tools have replaced manual production tools, cars, trucks, ships and pa ge 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 1-6, 2025 aircraft have replaced backward means of transportation, and the production efficiency has been greatly improved (meihan et al., 2023). however, the industrial and economic development in this stage mainly depends on the possession of natural resources. with the advent of the digital economy era, the industrial economy will shine new again. digital transformation of industrial economy and the development of digital economy have become new engines to promote the development of chinas industrial economy. the two will empower each other, which will further accelerate the transformation of industrial economy and promote the development of industrial economy to a higher level. digital economy digital economy is a concept with a relatively broad connotation. all the economic forms that directly or indirectly use data to guide resources to play a role and promote the development of productive forces can be included in its category. but we should not simply equate between the digital economy and the internet economy and the virtual economy. they are only part of the digital economy. at present, the digital technology, goods and services are not only in the traditional industry direction, multifaceted and chain accelerated penetration (i. e., digital industry), and in the push such as internet data center (internet data center, idc) construction and service digital industry chain and the development of industrial cluster (i. e., digital industrialization). according to the data of chinas digital economy development research report (2023). in 2022, the scale of chinas digital economy reached 50.2 trillion yuan, with a nominal year-on-year growth of 10.3%, which has been significantly higher than the nominal gdp growth rate of the same period for 11 consecutive years. the proportion of digital economy in gdp is equivalent to the proportion of the secondary industry in the national economy, reaching 41.5% (niaoer, 2022). it can be concluded that the development of the digital economy over time is particularly important for china and even for the whole world. industrial economy industrial economy is an important part of the development of human society, which refers to the use of natural resources and human machinery to produce commodities. the development of the industrial economy not only creates conditions for economic growth and employment, but also provides support for social progress and scientific and technological innovation. industry refers to the economic activities that use raw materials, energy and labor force to produce a variety of products and commodities through a series of production processes and technologies. midea group uses digital technology m. iot to gain insight into customers, improve products and services, and achieve customer satisfaction and stability. build the “t + 3” mode to realize the value transfer, and build an online midea shopping mall to expand the online channels. establish the vmi operation mode, improve the product system through data sharing, enhance the competitive advantage, and promote the global expansion of the brand. from the successful experience of midea, digital management innovation has improved the quality of enterprise products and highlights the management advantages (tie & zichi, 2021). from the example of midea group, how important it is for the industrial economy to develop further along with the digital economy. in a word, industry is one of the pillars of the modern economy. with the continuous progress of science and technology and the development of human society, industry will also continue to evolve and grow. materials and methods the influence of the digital economy on the development of industrial economy the author analyzes the index system of explanatory variables and explanatory variables in the high-quality development of digital economy. as shown in table 1 (yuhua et al., 2022) it is concluded that industrial economy is an important link to promote the continuous development and progress of human society, and digital economy is an important link to promote industrial development and upgrading. on the whole, when the industrial economy is transformed and upgraded, more funds should be invested in the industrial economy that can make a significant contribution to the economic growth in the future. table 1: index system of explanatory variables and explanatory variables for high-quality industrial development level 1 indicators secondary indicators level 3 indicators level 4 index the development level of the digital economy internet penetration rate number of internet broadband access users per 100 people + internet-related practitioners number of employees employed in computer, software and communication services + internet-related industries per-capita telecommunications business volume + pa ge 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 1-6, 2025 number of mobile internet users number of mobile phones users per 100 people + development of digital inclusive finance the china digital financial inclusion index + high-quality development level of the industrial economy economic benefits improved total labor productivity of industrial enterprises above designated size + profit rate of total assets of industrial enterprises above designated size + per capita assets of industrial enterprises above designated size + structural optimization and coordination industrial added value above designated size / national average industrial added value above designated size thiel index sustainable development capacity innovation ability energy consumption per unit of gross domestic product sulfur dioxide emissions per unit of gross domestic product smoke (powder) dust emissions per unit of gross domestic product patent application number per 10,000 people + number of students or above / average number of industrial enterprises above designated size + the impact of the digital economy on the industrial economy the impact of digital economy on industrial economy is the gap of digital transformation of industrial economy; secondly, the input-output of digital transformation is difficult to quantify in the short term. finally, there is a shortage of talent in the digital economy. chinas industrial economy is stepping into a critical stage of digital transformation, showing a trend of comprehensive penetration from consumeroriented to business-oriented and to its upstream and downstream industrial chains. although the traditional industrial economy is facing huge development pressure, digital transformation has become the only way to seek a breakthrough and rebirth, but the road of transformation is not smooth. specifically, the relevant industrial enterprises often explore the digital path due to the lack of rich transformation practice experience and systematic transformation data support ( jewel, 2023). nowadays, the construction of r & d institutions and professional talents in the field of intelligence is facing severe challenges, especially the lack of highend technical talents, scientific research and innovation forces and experts in core fields, which seriously restricts the pace of digital transformation in the field of digital economy. specific impact of digital economy on the development of industrial economy the first is technological innovation, to promote the industrial economy and technological upgrading. technological innovation is the core driving force of the digital economy in enabling the industrial economy. under the background of digital economy, the industrial economy is undergoing the transformation from the traditional industrial economy to intelligence and automation. the application of internet, big data, artificial intelligence and other technologies makes product design more accurate and production process more efficient. for example, through big data analysis to predict market demand, the industrial economy can achieve more flexible production planning and inventory management. secondly, the industrial structure optimization. the digital economy promotes the upgrading of the industrial economy. digital economy has an important influence on the optimization and upgrading of the industrial structure of the industrial economy. through the digital transformation, the industrial economy can be better integrated into the global value chain and improve its position in the international division of labor. the digital economy also promotes the transformation of the industrial economy from traditional production with low added value to intelligent manufacturing with high pa ge 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 1-6, 2025 added value. finally, the improvement of management efficiency and the application of digital technology in industrial economic management. in the era of digital economy, the management mode of industrial economy is undergoing fundamental changes. challenges facing the industrial economy in the digital economy with the rapid development of digital economy, its influence on the development of industrial economy is increasingly significant, thus providing new opportunities for the high-quality development of industrial economy. it is of great theoretical and practical significance to study how digital economy promotes the endogenous driving force and high-quality development of industrial economy. this paper selected 20122021 china 30 provinces (municipalities, autonomous regions) (excluding the tibet autonomous region and hong kong, macao and taiwan region) industrial enterprises and digital economy panel data to empirical research, collected 300 data samples, data samples from 30 provinces and china industrial statistical yearbook the china science and technology statistical yearbook china high-tech industry yearbook china environmental statistics yearand cnnic database make descriptive statistics, as shown in table 2 below (qinghe, 2024). found that the challenges of future industrial economic development are mainly reflected in the following aspects: first, intelligent manufacturing: intelligent manufacturing is one of the main trends of the future development of industrial economy. through the introduction of artificial intelligence, big data analysis and internet of things technology, enterprises can realize the automation, intelligence and networking of the production process, improve production efficiency, reduce costs, and promote the intelligent transformation and upgrading in the industrial economy. second, green and sustainable development: with the continuous enhancement of environmental protection awareness, green and sustainable development has become an inevitable choice of industrial and economic development. in the future, the industry will pay more attention to the economical use of resources and environmental protection, promote circular economy and clean production, reduce the impact on the environment, and achieve sustainable development. third, personalized customization: consumer needs are increasingly personalized and diversified. in the future, industrial economic products will pay more attention to flexible production and customized services. through the rapid response to the market demand, to achieve personalized customized production, improve product quality and customer satisfaction. fourth, cross-border integration innovation: in the future, the development of industrial economy will pay more attention to cross-border integration innovation, and realize cross-border integration and innovation of technology through cooperation and exchanges in different industries and fields. this will promote the integrated development of industries and promote the optimization and upgrading of the industrial structure. table 2: descriptive statistics variables (1) (2) (3) (4) (5) n mean sd min max year 300 2016 2.877 2012 2021 id 300 15.67 8.916 1 31 gov 300 0.263 0.113 0.105 0.758 tech 300 0.0217 0.0150 0.00539 0.0676 de 300 0.130 0.101 0.0173 0.577 quality 300 0.208 0.112 0.0620 0.822 hum 300 0.0208 0.00550 0.00852 0.0425 fdi 300 0.0183 0.0144 0.000100 0.0796 results and discussion development direction of industrial economy under the background of digital economy in high-quality development, we need to promote a new type of industrialization, make the industrial economy more high-end, intelligent and green, and accelerate the deep integration of the digital economy and the industrial economy, so as to build a modern industrial economy. in the current complex pattern of the global economy, digital transformation has become an irreversible trend, and its necessity and urgency have become increasingly obvious. and through this paper studies the relationship between the digital economy and industrial economy, can through to accelerate the integration of digital economy and industrial economy and management, digital economy under the background of intelligent transformation of three promotion path as the benchmark, further improve the digital economy of industrial economy high quality development and related contributions. accelerate the integration and management of digital economy and industrial economy the revitalization and development of modern industry cannot be separated from the strong support of information, and the reform and progress of information pa ge 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 1-6, 2025 is also based on the prosperity of industry as the material. therefore, it is necessary to continuously strengthen the organic integration of industrialization and information, and carry out standardized management, so that the two play a maximum role. first, we will accelerate the transformation and upgrading of the industrial economy. although chinas industrial economy maintains a good momentum of development, in the rising period, but we can not ignore the severe and complex economic situation we are facing, chinas domestic demand growth rate is slow, the continuous contraction of external demand are threatening the healthy and rapid development of industrial economy (kongtuan et al., 2023). therefore, while maintaining the stability and continuity of the national macroeconomic policies, it is necessary to continuously increase the further adjustment of the industrial structure and accelerate the transformation and upgrading of the industrial economy. second, accelerate the comprehensive integration of industrialization and digitalization. vigorously promote the integration of digital economy and industrial economy, realize the integration of information technology and industrial energy conservation and emission reduction, and promote new information technology in the power, steel and paper industries, so as to effectively promote the development of energy conservation and emission reduction work, and further protect chinas ecological environment. finally, the new information technology means to promote the management innovation of industrial economy.industrial economyin order to realize the rapid development, it is necessary to carry out the continuous innovation of management mode, the development of modern information technology and the deepening of information technology, which provides a good opportunity and platform for the innovation of management mode of industrial economy. at the same time, after enterprises should integrate new technologies, and gradually establish new management ideas and prescriptions to meet the needs of the industry three improvement paths for the intelligent transformation of the industrial economy under the background of the digital economy the first path is to build an open and shared digital platform ecology. driven by the wave of digital economy, the core of the transformation and upgrading of industrial economy lies in building an open and shared digital platform, so as to adapt to the rapid changes of market demand and improve the competitiveness of industrial economic enterprises. this process involves multi-dimensional strategic deployment and implementation, aiming to realize the intelligent and digital operation of industrial economy through technological innovation, channel optimization and value chain reconstruction. industrial economic enterprises should actively introduce the most advanced technological means, such as artificial intelligence, big data, cloud computing, etc., in order to improve the product performance, optimize the operation mode and improve the service quality. this process can not only promote enterprises to form a unique competitive advantage and brand influence, but also guide industrial enterprises to the direction of intelligent and digital. take a high-end equipment manufacturing enterprise as an example, through the introduction of intelligent manufacturing system and data analysis platform, it has realized the fine management of the production process and the continuous improvement of product quality, thus occupying a leading position in the global market (meixin & jiayi, 2023). at the same time, in the face of the digital transformation of consumer behavior, industrial economic enterprises need to flexibly adjust their sales strategies and publicity channels, and promote the migration of product sales and brand promotion from the traditional offline mode to online platforms. in the process of the transition,enterprises should adhere to the principle of putting efficiency first, quickly respond to the changes in market demand, reduce market risks and expand the market share through the integration of online and offline operation mode the second path, increase policy support, promote production and manufacturing digitalization. in the grand blueprint of the transformation and upgrading of small and medium-sized enterprises in the industrial economy, the digital transformation of production and manufacturing occupies a pivotal position, and its goal is to gradually promote the transformation from manual to information, digital, and finally towards the leap of intelligence. however, in the face of this transformation challenge, small and medium-sized enterprises often fall into the situation of “wait and see”, “hesitation” or even “avoidance” due to the high investment cost, unclear short-term benefits and the shortage of intelligent system operation and maintenance talents. in view of the above difficulties, cloud, low-cost, lightweight digital solutions emerge at the historic moment, and become the preferred path for small and medium-sized enterprises to step into the threshold of digital transformation. in order to help small and medium-sized enterprises overcome the transformation obstacles, many places introduce relevant policies to reduce the burden of enterprise transformation, including but not limited to providing financial support for financial subsidies, tax incentives, low-interest loans, as well as the establishment of special funds to support the digital transformation projects of small and medium-sized enterprises; at the same time, strengthen the construction of public service platform, provide one-stop services of technical consultation, personnel training, solution docking to reduce the cost and risk of small and medium-sized enterprises in the process of digital transformation (yu, 2022). with policy guidance and technical support, in order to win the initiative of market competition and the broad development of the future. the third path is to deepen digital education and cultivate technical people. in view of the unique demand for talent skills in the production pa ge 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 1-6, 2025 field, compared with the digital transformation in the consumer field, it relies more on the optimal allocation and in-depth mining of global network talent resources. therefore, the construction of a comprehensive and systematic digital talent education system has become the key driving force to promote the high-quality development of industrial economy. first of all, the higher education system actively adapts to the changes of the digital era, and promotes the in-depth development of digital specialty and interdisciplinary integration by adjusting the direction of specialty setting and discipline construction. secondly, enterprises should increase the training of employees digital skills (hao, 2022). this not only helps the old employees to quickly adapt to the intelligent production environment, but also promotes the internal knowledge inheritance and skill iteration of the enterprise. enterprises can provide employees with diversified learning channels and rich learning resources by establishing internal training colleges, introducing external professional training institutions or carrying out online learning platforms. finally, it should play a leading role in the universal digital education, and promote the popularization and dissemination of digital knowledge in the whole society through the implementation of the digital literacy promotion plan. the internet, television, radio and other channels are used to carry out digital popular science education, and the design is suitable for residents of different ages and different cultural levels. course content and teaching methods to ensure that digital knowledge covers a wider range of people. all sectors of society are encouraged to participate in the construction of digital education, and form a diversified digital education ecological body with government guidance, enterprise participation and social support. conclusion to sum up,this article deeply explores how the digital economy can promote the development of the industrial economy towards higher quality. from the perspective of the digital economy, two effective ways for the development of the industrial economy are proposed. one is to accelerate the integration and management development direction of digital economy and industrial economy; the second is the three development directions for the intelligent transformation of industrial economy under the background of digital economy. we can see that the digital economy, as a new engine of the global economy, is profoundly changing the development trajectory of the industrial economy. through digital transformation, industrial economic enterprises have not only achieved significant improvements in production efficiency and continuous optimization of product quality, but also promoted the optimization and upgrading of industrial structure. despite facing many challenges during the transformation process, industrial economic enterprises should further strengthen technological innovation, optimize industrial structure, improve management efficiency, and attach importance to talent cultivation and technological updates. at the same time, enterprises should actively explore new business models and use digital means to optimize supply chain management and customer relationships, in order to adapt to the increasingly changing market demands and competitive environment. reference fei, l. (2023). research on the upgrading and transformation of manufacturing enterprises in 2023. shandong textile economy, (2), 1-4. hao, x. (2022). digital economy enables to study the realization path of high-quality development of anhui manufacturing industry. bengbu: anhui university of finance and economics. jianhui, j., & guobin, a. (2023). research on the driving influence of digital economy on electronic information producer services. chinas science and technology industry, (6), 49-52. meihan, g., kunxu, w., & ziwei, p. (2023). discussion on high quality endogenous power and ways of digital economy enabling manufacturing. shopping mall modernization, (20), 135-137. meixin, f., & jiayi, l. (2023). research on the impact of digital economy development on manufacturing upgrading in the beijing-tianjin-hebei region. business economy, (12), 23-26. niaoer, y. (2022). optimize the digital trade ecology to enable the integrated development of the two industries. ningbo economy (sanjiang forum), (3), 9-13. qinghe, c. (2024). research on the intelligent transformation path of equipment manufacturing industry in l province under the background of 2024 digital economy. the chinese market, (22), 191-194. tie, l., & zichi, l. (2021). digital technology enables high-quality development of manufacturing: based on the perspective of value creation and value acquisition. academic monthly, 53(4), 56-65. tuan, l. k., xuanhao, z., jie, h. (2023). research on the spatial effect of digital economy enabling manufacturing industry upgrading. journal of tianjin university of commerce, (43), 3-9. wang, x., ju, z., kovshar, s. n., leonovich, s. n., & solopova, n. a. (2023). the use of non-metallic fiber in the protection of building materials and its impact on the environment. экономика строительства, (7), 86-91. xianpeng, w., & haoxuan, y. (2024). commercial economic value of non-metallic fiber concrete. yu, m. (2022). the internal mechanism and realization path of the transformation of manufacturing industry. shopping mall modernization, (11), 110-112. yuhua, l., chengjun, l., & ziwei, x. (2022). research on the configuration path of service-oriented manufacturing transformation in the context digital economy. china science and technology forum, (8), 6876. pa ge 1 pa ge 73 american journal of financial technology and innovation (ajfti) comparative analysis of ai-driven marketing strategies of the e-commerce industry in the modern world md. amran hossain pabel1, ratna akter2, tapan kumar biswas2, md. mostafa kamal2*, foyjun nahar3, jumman sani2 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3789 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: sepember 12, 2024 accepted: october 16, 2024 published: may 10, 2025 e-commerce organizations increasingly employ artificial intelligence (ai) technologies to reinforce consumer experiences, enhance marketing campaigns, and optimize overall business performance. this study focuses on providing an extensive analysis of ai-driven marketing strategies in the e-commerce sector in the contemporary world. this study employed bibliometrics analysis, which is a technique employed to comprehend the development and nature of a specific discipline by integrating, interpreting, and assessing existing sources and statistics. this paper compared and evaluated the myriad ai-driven marketing strategies adopted by e-commerce companies, highlighting their benefits, challenges, and potential implications for the sector. the findings exposed that e-commerce comprehensively employs experiential marketing, with a specific focus on the effects of artificial intelligence in virtual-based assistants. besides, this study highlighted the instrumental role of artificial intelligence in terms of facilitating personalized experiences, strategic decision-making, and predictive algorithms, within marketing operations in e-commerce. moreover, market research underscores the incorporation of artificial intelligence in distinct areas such as marketing and sales, data analysis, and comprehending consumer behavior. this study discussed diverse aspects of research and applications of artificial intelligence in different marketing domains. the research ascertained that integrated digital marketing examines the application of social media data for customer sentiment analysis and the employment of artificial intelligence algorithms in social media marketing. a significant volume of studies established that content marketing concentrates on the implications of artificial intelligence on content creation and targeting, and the company-level repercussions of artificial intelligence in marketing. keywords ai-driven marketing, e-commerce sector, marketing strategies, business performance, customer experience introduction in the modern digital era, e-commerce has experienced dramatic growth, transforming how businesses operate and customers ‘experiences. concurrently, artificial intelligence (ai) evolutions have transformed various industries, such as marketing. ai-driven marketing approaches have emanated as powerful tools for e-commerce businesses to elevate client experiences, leverage marketing campaigns, and enhance overall business performance (acharya et al., 2023). according to acharya et al. (2023), the e-commerce sector has witnessed exponential growth in the recent past, propelled by the escalating dependence on online technological advancements. with the emergence of artificial intelligence, e-commerce businesses have access to complex techniques and tools that can monitor and evaluate large volumes of customer data, personalized marketing approaches, recommendation systems, and predictive analytics. as per avinash (2020), ai-oriented marketing strategies can drive client engagement, elevate conversion rates, and reinforce client satisfaction, thereby promoting business success in the highly competitive e-commerce landscape. bawack et al. (2023) indicated that the execution of artificial intelligence-driven marketing tactics in the e-commerce sector has become instrumental because of the large volume of consumer data available and the need for organizations to make data-based decisions. artificial intelligence technologies enable e-commerce organizations to monitor and evaluate large datasets, pinpoint patterns, and extract valuable insights to tailor more efficient marketing campaigns. furthermore, ai-powered recommendation systems and chatbots have revolutionized the client experience by offering personalized product recommendations and instant customer support (chintalapati & pandey, 2021). the prime aim of this research paper is to provide a comparative analysis of ai-driven marketing strategies in the e-commerce sector, examining their benefits, challenges, and possible implications for the modern world. the respective objectives of this research paper are as follows: (a) to compare and contrast different artificial intelligence-driven marketing methods, (b) to evaluate and pinpoint the benefits of artificial intelligence-driven marketing strategies in the e-commerce sector. (c) to examine the challenges confronted during the deployment of ai-driven marketing strategies. (d) to explore the implications of artificial intelligencedriven marketing strategies on client experiences, 1 business analytics, wright state university, united states 2 business administration, university of development alternative (uoda), bangladesh 3 department of computer science and engineering, university of development alternative (uoda), bangladesh * corresponding author’s e-mail: mostafakamal@y7mail.com pa ge 74 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 marketing campaigns, and business performance in the e-commerce sector. in that respect, the research questions were tailored as follows: • how do different artificial intelligence-driven marketing strategies compare in terms of efficiency and effect on client experiences and business performance? • what are the possible implications of artificial intelligence-oriented marketing tactics on the e-commerce sector? • what are the benefits and challenges of artificial intelligence-driven strategies in the e-commerce sector? the significance of this study lies in its contribution to the comprehension and advancement of artificial intelligence-driven marketing strategies in the e-commerce sector. in particular, this research paper will contribute to the current body of knowledge by presenting an extensive and robust analysis of artificial intelligencedriven marketing strategies in the e-commerce sector. besides, this study will shed light on the benefits, roles, and challenges of deploying artificial intelligence technologies in marketing, therefore enhancing our comprehension of how artificial intelligence can be efficiently employed to drive business success in the modern world. the findings of this study will have practical repercussions for e-commerce organizations. by analyzing and comparing distinctive ai-oriented marketing strategies, organizations will obtain insights concerning the most efficient strategies for reinforcing client experiences, enhancing marketing campaigns, and elevating overall business performance. as such, this research will guide organizations in making informed decisions concerning the adoption and execution of artificial intelligence technologies in their marketing strategies. literature review overview of artificial intelligence in marketing haleem et al. (2022), contend that artificial intelligence (ai) has emanated as a revolutionary force in various sectors, and its implication on marketing is specifically noteworthy. in the ever-changing landscape of the e-commerce sector, artificial intelligence-oriented marketing has proven to be a game-changer. as per kalia (2022), artificial intelligence revolves around the simulation of human intelligence in machines, facilitating them to perform tasks that normally mandate human intelligence. as regards marketing, artificial intelligence is employed to promote efficiency, personalize customer experiences, and drive strategic decision-making. hasan (2022), argues that artificial intelligence in marketing comprises a range of technologies, including natural language processing, machine learning, and predictive analytics. these technologies allow organizations to assess and evaluate large volumes of data, retrieve meaningful insights, and automate processes. in the setting of e-commerce, artificial intelligence has proven to be a strategic asset, affording marketers with powerful mechanisms to comprehend consumer behavior, enhance campaigns, and stay ahead of the competition. role of ai in e-commerce marketing gkikas and theodoridis (2017), asserted that the role of artificial intelligence in e-commerce marketing is paramount in terms of enhancing client experiences, leveraging marketing campaigns, and streamlining overall business performance. artificial intelligence technologies allow organizations to attain valuable insights from consumer data, comprehend consumer behavior, and develop targeted and personalized marketing tactics. artificial intelligence-driven marketing tactics play a paramount role in a myriad of domains. in the e-commerce domain, where competition is stiff and consumer anticipations are high, artificial intelligence plays an instrumental role in various elements of marketing. one of the principal applications is in client targeting and segmentation. artificial intelligence algorithms can assess consumer data to pinpoint preferences and patterns, enabling marketers to tailor personalized and targeted campaigns (gkikas & theodoridis, 2017). this not only optimizes the client experience but also enhances the effectiveness of marketing efforts. moreover, artificial intelligence is indispensable in recommendation frameworks, a key attribute in e-commerce forums. by assessing consumer purchase history, and demographic information, browsing behavior, artificial intelligence algorithms can suggest and predict products that coincide with individual preferences. this not only increases sales but also promotes client loyalty by offering a tailored shopping experience (gupta et al., 2021). benefits of ai-driven marketing in e-commerce the adoption of ai-driven marketing strategies in the e-commerce industry brings about a myriad of benefits that contribute to the overall success of businesses. enhanced personalization artificial intelligence facilitates highly personalized marketing campaigns premised on personal client behavior and preferences. this degree of personalization not only optimizes client satisfaction but also increases the likelihood of conversion (gupta et al., 2021). improved customer engagement virtual assistants and chatbots empowered by artificial intelligence allow real-time interactions with clients, addressing and resolving their respective queries promptly as well as offering assistance. this not only elevates customer engagement but also builds loyalty and trust (gupta et al., 2021). optimized advertising artificial intelligence facilitates precise targeting by assessing large-volume datasets to pinpoint the most pa ge 75 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 relevant audience segments. this optimization results in more efficient advertising campaigns, minimizing wasted resources and elevating return on investment (gupta et al., 2021). data-driven decision making artificial intelligence processes and assesses large volumes of data at a scale and speed nearly impossible for humans. this capacity empowers marketers and advertisers to make data-driven choices, pinpoint trends, and adapt strategies in real time (gupta et al., 2021). effective resource allocation artificial intelligence-driven automation simplifies various marketing procedures, from campaign optimization to client support. this effectiveness facilitates organizations to allocate resources more efficiently, focusing on highimpact activities. challenges in implementing ai-driven marketing strategies despite the benefits and advantages of artificial intelligence in e-commerce marketing are evident, there are also challenges related to its implementation that organizations should navigate. (1) data privacy concerns: the adoption of artificial intelligence entails the gathering and analysis of large volumes of customer data. this in turn causes concerns regarding security and data privacy. as such, affirming compliance with regulations and establishing trust with customers regarding data handling practices is crucial (haleem et al., 2022) (b) consolidation complexities: deploying artificial intelligence-oriented marketing strategies frequently demands incorporation with current systems and technologies. this process can be complex and may require significant investments in both time and resources (haleem et al., 2022). comparative analysis of artificial intelligencedriven marketing strategies according to kalia (2022), artificial intelligence-based marketing tactics have become instrumental aspects of contemporary business frameworks, each providing unique benefits and resolving specific components of the client journey. this comparative evaluation examines five key artificial intelligence-based marketing tactics, most notably personalization and recommendation frameworks, chatbots and virtual assistants, predictive analytics and client segmentation, dynamic pricing and demand prediction, and social media analysis and influencer marketing. personalization and recommendation systems recommendation and personalization systems leverage artificial intelligence algorithms to assess user preferences and behavior, offering tailored product suggestions and content. e-commerce forums employ these systems to elevate the client experience and drive engagement. for instance, amazon’s recommendation engine is prominent for its capability to suggest relevant items based on user purchases and browsing history (kalia, 2022). the system evaluates large volumes of data and incorporates collaborative filtering and machine learning algorithms to forecast customer preferences accurately. advantages personalization and recommendation systems promote a sense of individuality, elevating client loyalty and satisfaction. by comprehending client’s preferences, companies can present targeted promotions, escalating the likelihood of conversions. recommendation frameworks contribute to upselling and cross-selling opportunities, leveraging revenue per client (kalia, 2022). challenges gazi and ray (2023), indicated that over-dependence on algorithms may culminate in a “filter bubble,” where customers are only visible to a limited range of content or products. striking the right balance between diversity and personalization in recommendations is pivotal. furthermore, the accuracy of recommendations relies on the quantity and quality of available data, making data privacy and security paramount concerns (kitsios & kamariotou, 2020). chatbots and virtual assistants as per hasan (2022), virtual assistants and chatbots powered by artificial intelligence have become instrumental in offering real-time clientele support, addressing queries, and guiding users via the purchase process. these technologies reinforce customer interaction and streamline communication. for instance, e-commerce corporation alibaba adopts ai-powered chatbots to manage client inquiries on its forum. these chatbots adopt natural language processing and machine learning to comprehend and react to consumer queries, minimizing the need for human intervention. advantages chatbots provide 24/7 availability, thus enhancing client service by presenting instant responses. they can manage routine queries, liberating human agents for more sophisticated tasks. virtual assistants lead to a seamless and effective client journey, increasing overall satisfaction (hasan, 2022). challenges the challenge depends on guaranteeing that chatbots offer accurate and contextually relevant information. striking the required balance between automated responses and the need for human involvement in sophisticated scenarios is pivotal (hasan, 2022). besides, designing chatbots that reflect the brand’s tone and uphold a personalized touch can be quite challenging. predictive analytics and customer segmentation ma and sun (2022), articulated that predictive analytics pa ge 76 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 and customer segmentation constitute the application of artificial intelligence to assess data and predict future trends, enabling companies to craft marketing strategies to specific customer segments. for example, organizations like walmart and target adopt complex data analytics and machine learning algorithms to segment clients based on their demographics, purchase history, and browsing behavior (gazi & ray, 2023). this segmentation facilitates them to design marketing promotions and campaigns for specific segments, hence, escalating the likelihood of conversion. advantages predictive analytics facilitates companies to expect customer needs, leveraging inventory management and minimizing costs. consumer segmentation enables targeted marketing campaigns, elevating the efficiency of promotional efforts. by comprehending consumer behavior, organizations can adjust strategies to align with evolving trends (ma & sun, 2022) challenges attaining accurate predictions depends on the quality of historical data and the complexity of the artificial intelligence algorithms. over-dependence on historical data may cause biases, and adjusting to rapidly transforming market dynamics can be challenging. as such, organizations should also navigate privacy matters related to extensive data analysis (ma & sun, 2022). social media analysis and influencer marketing moradi and dass (2022), indicated that artificial intelligence-based influencer marketing and social media analysis have become instrumental for e-commerce businesses to comprehend customer sentiment, identify trends, and leverage influencers for brand promotion. social media analysis employing artificial intelligence assists companies in comprehending client wants, enabling more targeted engagement tactics. influencer marketing optimizes the reach and influence of individuals to promote products authentically. for example, beautybrands like fashion glossier and nova have effectively deployed social media influencers to endorse their products and reach out to a wider audience (hasan, 2022). ai-powered systems can pinpoint relevant influencers based on factors such as follower demographics, engagement rates, and brand affinity. benefits social media analysis affords key insights into customer preferences, behavior, and sentiment. by scrutinizing social media forums, companies can obtain a deep comprehension of their target audience, facilitating more informed decision-making in marketing strategies. social media allows real-time communication and evaluation systems allow organizations to obtain instant feedback on campaigns, products, and overall brand perception (moradi & dass, 2022). this immediacy enables agile responses, assisting organizations in resolving concerns on time and leveraging positive sentiments. challenges according to gazi and ray (2023), the analysis and gathering of customer data raise privacy issues. striking a balance between retrieving valuable data for evaluation and respecting customer privacy is a sensitive challenge. organizations should navigate and maneuver via evolving regulations and affirm compliance to establish and uphold trust. besides, the massive volume of data produced on social media can be cumbersome. moreover, distinguishing meaningful data from the noise demands advanced analytical strategies and tools. organizations may struggle to sort out the vast amount of information to extract actionable insights (moradi & dass, 2022). materials and methods this study employed bibliometric analysis, which is a technique employed to comprehend the development and nature of a specific discipline by integrating, interpreting, and assessing existing sources and statistics. moreover, bibliometric analysis is a versatile approach that consolidates distinctive analytical methods such as co-authorship, co-occurrence, and co-citation. among these methods, co-occurrence evaluation is widely acknowledged as a powerful technique for examining and mapping associations among varying scientific research domains. bibliometric studies present valuable insights by providing an extensive viewpoint on relevant fields or subjects, pinpointing advancements and changes over time, and pinpointing gaps and emerging topics for future researchers. for this study, data gathering was undertaken utilizing all indexes available in the web of science, google scholar, and ieee. the search strategy comprised exploring research articles by imposing restrictions on the publication year. the keyword “aidriven marketing strategies in e-commerce sector*” was adopted to sort out the articles. subsequently, all of the pinpointed relevant journal articles were downloaded. the scope of this study entails a comparative analysis of artificial intelligence-driven marketing tactics in the e-commerce sector within the contemporary world. the study concentrates on comprehending the role, challenges, benefits, and repercussions of artificial intelligence technologies in the marketing domain of e-commerce businesses. the research particularly explores various ai-based marketing tactics, such as personalization and catboats, virtual assistants, recommendation systems, predictive analytics and client segmentation, social media analysis, and dynamic pricing. this research will comprise a wide range of e-commerce organizations, taking into consideration both emerging and established companies. the study will examine case studies of big e-commerce corporations to ascertain their ai-driven marketing tactics and their effect on client experiences, marketing campaigns, and organizational performance. the comparative analysis will facilitate pa ge 77 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 the detection of best practices, successful deployment techniques, and possible domains for improvement within different ai-driven marketing approaches. results and discussion inclusion criteria • inclusion criteria #1: studies located using the keywords “ai-driven marketing in the e-commerce sector” as well as “ai and marketing” only in their respective title • inclusion criteria #2: “marketing in e-commerce” and/or” artificial intelligence” only in the title • inclusion criteria #3: published after january 2017 • inclusion criteria #4: only journal articles that are published in peer-reviewed scholarly sources. • inclusion criteria #4. only studies written in the english language. exclusion criteria • exclusion criteria 1: duplicates identified through the digital object identifier. • exclusion criteria 2: non-english journal articles. • exclusion criteria 3: dissertations and opinion reports figure 1: showcases the results of the literature search process source: authors’ creation figure 2: showcases number of included studies per year source: authors’ creation figure 3: journals with most cited publications source: authors’ creation figure 2 above showcases the number of citations selected per year. this comprised an overview of the dissemination of studies across the years in the domain of artificial intelligence in marketing. from the graph above it is evident that the majority of the retrieved studies were from the past 5 years (n=19), demonstrating that the researcher included studies that were recent and relevant. pa ge 78 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 figure3 above exhibits the journals with the most cited publications. technological forecasting & social change (n=5), was the most cited journal, followed by the journal of business &industrial marketing (n=4), the journal of brand strategy, and the international journal of market research respectively. these results indicated that these journals are the most impactful in their respective fields. they are potentially the most widely read and respected journals in their fields. findings below figure 4 shows the emergent themes. figure 4: emergent themes source: authors’ creation theme 1: integrated digital marketing the artificial intelligence transformation has substantially revolutionized digital marketing, and a myriad of studies have assessed the specific domains within digital marketing that have been impacted by ai. in particular, moradi and dass (2022), undertook research to examine the domain of digital marketing that has already witnessed the implications of artificial intelligence and how it has reinforced the digital marketing arena. khokhar and necula and păvăloaia (2023), concentrated on examining the components that drive the employment of artificial intelligence in marketing. the future potential of artificial intelligence in marketing has been articulated by sliwinski (2023), who pinpointed numerous new applications of artificial intelligence that are modeling the marketing sector. soni (2019) presented key insights into the artificial intelligence ecosystem and the fundamental technologies that enable ai-driven marketing processes. in the setting of online advertising’s wide-ranging impact in contemporary marketing, verma et al. (2021), examined the implications of artificial intelligence on programmatic advertising. several recent studies have concentrated on examining the implications of artificial intelligence on digital marketing within specific research arenas. wang et al. (2023) explored the impact of artificial intelligence on consumer experience, examining modern use case scenarios such as the amazon flywheel technique and amazon collaborative filtering from the viewpoint of consumer service and client experience. theme 2: experiential marketing experiential marketing is deemed one of the most innovative and heavily invested aspects in the domain of marketing. comprehensive research has been undertaken on various components, with a specific focus on chatbots, voice, and the effects of image recognition (yadav, 2022). vapiwala and pandita (2019) provided a framework presenting the diverse applications of revolutionary technologies in marketing and their equivalent implications. previous research has examined the effect of consumer trust on the approval and application of ai agents, and the ethical implications and security requirements related to them (yadav, 2022). furthermore, significant studies have articulated the timeline and maturity level of artificial intelligence evolution (acharya, 2023) and underscore the paramount role of artificial intelligence in terms of making informed marketing decisions (hildebrand, 2019). other substantial studies have included an in-depth assessment of the comprehensive application of artificial intelligence in marketing, the pinpointing of opportunities related to chatbots in marketing, and the tailoring of advanced intelligent search mechanisms (bawack et al., 2023). theme 3: marketing operation recent studies have concentrated on various components within marketing, such as direct marketing analytics employing support vector data description, ai-based surrounding in branding, real-time use scenarios of artificial intelligence-powered marketing automation, the consolidation of artificial intelligence in marketing, sales prediction, and the transformation of sales and marketing job roles. marketing technology (martech) has emanated as an advancing sector within marketing operations, with a particular focus on marketing automation pa ge 79 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 conclusion this research aimed to explore and shed light on the aidriven strategies of the e-commerce industries. this study explored the real-time impact of artificial intelligence in marketing and employed a bibliographic methodology to examine the ai-driven strategies in the e-commerce sector. as artificial intelligence proceeds to advance, it will become more intelligent and augment human thinking, potentially surpassing human capabilities in creative thinking. this study discussed diverse aspects of research and applications of artificial intelligence in different domains of marketing. the research ascertained that integrated digital marketing examines the application of social media data for customer sentiment analysis and the employment of artificial intelligence algorithms in social media marketing. a significant volume of studies established that content marketing concentrates on the implications of artificial intelligence on content creation and targeting, and the company-level repercussions of artificial intelligence in marketing. this research exposed that e-commerce equally employs experiential marketing which revolves around the role of artificial intelligence in voice-based assistants, the philosophical underpinning ofthe artificial intelligence effect, and the application of artificial intelligence in e-commerce sectors. besides, this study determined that marketing operations in e-commerce revolved around the use of artificial intelligence in personalized experiences, predictive algorithms, and strategic decisionmaking. on the other hand, market research articulates the incorporation of artificial intelligence in marketing and sales, data analysis, and comprehending consumer behavior. references acharya, n. k., sassenberg, a., & soar, j. (2023). effects of cognitive absorption on continuous use intention of ai-driven recommender systems in e-commerce. journal of brand strategy, 25(2), 194–208. https://doi. org/10.1108/fs-10-2021-0200 avinash, v. (2020). the role of ai in predictive marketing using digital consumer data. journal of business and industrial marketing, 11(06), 106-109 bawack, r. e., wamba, s. f., carillo, k., & akter, s. (2023). artificial intelligence in e-commerce: a bibliometric study and literature review. journal of brand strategy, 32(1), 297–338. https://doi.org/10.1007/s12525022-00537-z chintan, s., gunjan, t., krupa, r., & devang, v. (2019). applications of artificial intelligence in marketing. journal of business and industrial marketing, 5(1), 29-36 chintalapati, s., & pandey, s. k. (2021). artificial intelligence in marketing: a systematic literature review. technological forecasting & social change, 64(1), 38–68. https://doi.org/10.1177/14707853211018428 gazi, m., & ray, r. (2023). exploring machine learning techniques for fraud detection in financial transactions. and digital consolidation (acharya, 2023). bawack et al. (2023), undertook an extensive study assessing 5,000 realtime use incidents of martech across various aspects such as sales, content, promotion, marketing advertising, and consumer experience. meanwhile, stone et al. (2020) performed a seminal study on the effects of artificial intelligence on decision-making and marketing strategies, which acts as a substantial reference in this arena. chintalapati and pandey (2021), further suggest that the escalating adoption of ai-powered marketing can affect virtually every aspect of marketing function. theme 4: market research the domain of market research has mainly focused on the examination of customer behavior, with a significant study looking into this area. hasan (2022), performed an assessment particularly assessing the application of artificial intelligence in customer segmentation and market research. in particular, studies on customer behavior have offered valuable insights, comprising the development of an algorithmic framework. chintain (2019) performed research that showcased a strategic model for integrating artificial intelligence in marketing. their model suggested a three-pronged dimension to strategic marketing planning. the study classified the present employment of artificial intelligence in marketing into three classes, based on the nature of their operation and application in the overall marketing process. these classes entailed mechanical ai, thinking ai, and feeling ai. gupta et al. (2021), demonstrated how artificial intelligence can be more efficient when it improves the capabilities of human managers. furthermore, other studies have assessed the amplification of artificial intelligence in b2b concepts and the assessment of marketing strategies. theme 5: content marketing a significant number of studies have concentrated on the domain of intelligent content marketing and the employment of web technologies. these inventions have had a notable effect on diverse communication streams, comprising marketing and corporate communications (kalia, 2022). the content itself has arisen as an influential and instrumental system within marketing, with specific attention provided to content curation and creation, which have witnessed substantial transformations via the adoption of artificial intelligence-powered marketing methods (ma & sun, 2022). as the quantity of content being produced and curated proceeds to escalate across diverse information consumption channels, there has been an escalating demand for content personalization (moradi & dass, 2022). the need for comprehensive content personalization has emerged from the desire to produce automated information using artificial intelligence-powered content marketing. to address this need, content recommender frameworks have been established using narrative science methodologies (necula & păvăloaia, 2023). pa ge 80 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 73-80, 2025 cjge. https://ytgcxb.periodicales.com/index.php/ cjge/article/view/332 gkikas, d. c., & theodoridis, p. k. (2017). ai in consumer behavior. technological forecasting & social change, 9(1)147–176. https://doi.org/10.1007/9783-030-80571-5_10 gupta, s., justy, t., kamboj, s., kumar, a., & kristoffersen, e. (2021). big data and firm marketing performance: findings from knowledge-based view. technological forecasting and social change, 171, 120986. https://doi.org/10.1016/j.techfore.2021.120986 gupta, s., borkar, d., de mello, c., & patil, s. (2018). the application impact of artificial intelligence (ai) on e-commerce. journal of business & industrial marketing, 10(7). hasan, m. r. (2022). cybercrime techniques in online banking. international journal of aquatic science, 13(1), 524–541. https://www.journal-aquaticscience.com/ article_158883.html haleem, a., javaid, m., qadri, m. a., singh, r. p., & suman, r. (2022). artificial intelligence (ai) applications for marketing: a literature-based study. international journal of market research, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005 kalia, p. (2022). artificial intelligence in e-commerce. international journal of market research, 10(3), 9–19. https://doi.org/10.1201/9781003095910-2 kitsios, f., & kamariotou, m. (2020). artificial intelligence and business strategy towards digital transformation. encyclopedia of information science and technology, 13(4), 2025. https://doi.org/10.3390/su13042025 ma, l., & sun, b. (2022). machine learning and artificial intelligence in marketing – connecting computing power to human insights. international journal of research in marketing, 37(3), 481–504. https://doi. org/10.1016/j.ijresmar.2020.04.005 moradi, m., & dass, m. (2022). applications of artificial intelligence in b2b marketing: challenges and future directions. journal of business and industrial marketing, 107, 300–314. https://doi.org/10.1016/j. indmarman.2022.10.016 necula, s., & păvăloaia, v. (2023). ai-driven recommendations: a systematic review of the state of the art in e-commerce. journal of the academy of marketing science, 13(9), 5531. https://doi. org/10.3390/app1309531 śliwiński, r. (2023). artificial intelligence: a prerequisite for competitive advantage in e-commerce. journal of the academy of marketing science, 15–34. https://doi. org/10.4324/9781003204343-1 soni, n. (2019). impact of artificial intelligence on businesses: from research, innovation, market deployment to future shifts in business models. encyclopedia of information science & technology. https:// arxiv.org/abs/1905.02092 verma, s., sharma, r., deb, s., & maitra, d. (2021). artificial intelligence in marketing: systematic review and future research direction. technological forecasting & social change, 1(1), 100002. https://doi.org/10.1016/j. jjimei.2020.100002 wang, c., ahmad, s. f., ahmad, a. b., awwad, e. m., irshad, m. i., ali, y. a., al-razgan, m., khan, y., & han, h. (2023). an empirical evaluation of technology acceptance model for artificial intelligence in e-commerce. technological forecasting & social change, 9(8), e18349. https://doi.org/10.1016/j. heliyon.2023.e18349 vapiwala, f., & pandita, d. (2019). analyzing the application of artificial intelligence for e-commerce customer engagement. journal of brand strategy, 11(4), 1–7. https://ieeexplore.ieee.org/document/10041655 yadav, d. (2022). the intersection of ai and consumer behavior: predictive models in modern marketing. technological forecasting and social change, 10(3), 11–17. https://remittancesreview.com/menu-script/index. php/remittances/article/view/907 pa ge 1 pa ge 32 american journal of financial technology and innovation (ajfti) influence of digital advertising on sales case: water home express canoa fernando jose hernandez demera1, frank angel lemoine quintero1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3940 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: october 25, 2024 accepted: november 27, 2024 published: march 04, 2025 this investigation focuses on the influence of digital advertising on sales, case: water home express canoa located in the province of manabí. the objective is to analyze the impact of online advertising on the sales of water home express canoa, a company specialized in the marketing of bottled water in the parish of canoa. the research seeks to identify the digital advertising tactics used by the company and their effectiveness in sales growth. to do this, quantitative and qualitative methods were used, including surveys and interviews, on a population of 344 clients, from which a sample of 62 participants was selected. the results reflect that social networks are the main way of discovery for the company, while digital advertising plays an important role in the purchase decision. although a significant proportion of respondents consider social media advertising effective, there is a considerable group that adopts a neutral stance (45.2%), suggesting that the effectiveness of these strategies can still be improved. additionally, some respondents show brand loyalty thanks to digital advertising. these findings indicate that, despite their positive impact, it is necessary to optimize digital campaigns to strengthen the connection with consumers and increase the effectiveness of marketing strategies. the analysis concludes that digital advertising is essential to increase sales, but also highlights the importance of understanding consumer preferences and perceptions to achieve more effective and attractive campaigns. keywords consumer perception, digital advertising, marketing strategies, sales, surveys 1 department of marketing, bahía de caráquez extension, eloy alfaro lay university of manabí, ecuador * corresponding author’s e-mail: frank.lemoine@uleam.edu.ec introduction nowadays, digital advertising has become an essential tool for companies seeking to increase their visibility and maximize their sales. with the advancement of technology and the increasing use of the internet, advertising strategies have evolved, allowing companies to reach their target audience more efficiently and effectively. this thesis focuses on analyzing the influence of digital advertising on sales, taking the company “water home express canoa” as a case study. water home express canoa is a company dedicated to the marketing of products for the purification and supply of drinking water. in an increasingly competitive market, the company has chosen to implement digital advertising strategies with the aim of increasing its market share and improving its sales results. this study seeks to evaluate the impact of these strategies, identifying which have been most effective and how they have contributed to the company’s growth. all companies seek to implement tools that allow them to promote their products and services, in order to attract the largest number of potential customers. lopez, (2022) advertising is understood as a marketing strategy that involves the use of media to promote a product, a service and/or a brand, with the purpose of reaching the company’s target audience and motivating them to make purchases, which in turn contributes to customer loyalty through their purchases. when we talk about “digital advertising, we are talking about social networks and digital media. that is, all types of advertising that are carried out using the internet” (tauro, 2023). some examples of digital advertising may be ads in marketplace, carrying out an e-mail marketing campaign or developing a positioning strategy for our website. sales activity is essential both in companies and in independent professional life. according to aranda (2017), “the importance of sales lies in the fact that business and professional success depends on the number of sales and how well they are made and the profitability produced, in order to maintain them over time”, since the sales made guarantee presence in the market and, therefore, the continuity of the jobs created. however, pacheco (2017) in his article mentions that digital advertising includes a large presence on social networks and on the web as such, therefore in our society it has come to have a greater impact and many companies have decided to implement a whole marketing strategy. on the other hand, carrillo & castillo (2005) mention that new digital advertising (npd) is the result, as we already said in, of the gradual metamorphosis that advertising has been undergoing from a scenario of inconsistency of formats and strategies to a situation of greater richness and value in terms of form and content. advertising is a key tool within the promotion mix, as it focuses on influencing consumer purchasing behavior and perception through concrete and well-founded information, ideas and opinions, thus creating a sense of belonging. (ramos et al., 2020) according to freire (2014), he argues that the marketing mix (also called marketing mix, commercial mix, commercial mix, the 4p’s, etc.) is the name given to the tools or variables that the marketing manager has to achieve the company’s objectives. the 4 p’s pa ge 33 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 refer to product, place, price and promotion. simply selling products to customers is not enough to remain competitive in today’s market. it is essential to know the needs and desires of customers in order to adapt our products and ensure their satisfaction, which in turn can generate repeat orders. the impact of online advertising on consumer behavior is profound and constantly evolving in the digital age. online advertising has not only transformed the way businesses promote their products and services, but it has also shaped the way consumers interact with brands and make purchasing decisions (palma et al., 2023). digital advertising not only increases a brand’s visibility, but also provides effective tools to boost sales through greater understanding and connection with the consumer. according to alfaro et al. (2023): today, investment in digital advertising is capable of quantifying virtual communities of consumers, as well as allowing the construction of platforms and own content with viral potential and even mapping and tracking interactions from advertising sources to electronic billing. the case of “water home express” exemplifies how investment in digital advertising can be a powerful tool to increase sales. by quantifying virtual communities, creating their own content, taking advantage of the potential for viralization and mapping interactions effectively, companies can optimize their marketing strategies and obtain significant results in their sales, as highlighted in alfaro’s analysis. digital advertising has a significant impact on sales thanks to the strategies and tools that facilitate effective connections between companies and consumers. herrera et al. (2011) suggest that one advantage is the ability to personalize and segment campaigns, which increases the relevance of advertising messages and, consequently, the sales conversion rate. cordova (2022) argues that it is important to highlight that without advertising and without sales, the economic growth that we all desire is not promoted, both for the company, which needs to grow, and for the staff, who experience a decrease in their income. this affects both the company and the sales staff. according to bertone (2019), he mentions that sales “is a process that is a theoretical and practical study in which the advantages that organizations can access by managing their sales activities by processes are analyzed”. it is important to clarify that btl is the abbreviation for “below the line”. it refers to advertising aimed at specific groups of people. without a doubt, it is the ideal advertising to seek conversions and direct responses, that is, for the consumer to choose us quickly due to direct contact with him. during the sales process, it is essential that the seller accompanies the customer at each stage of their purchasing cycle, satisfying their constant needs by providing the right information at the right time (johnston & marshall, 2009). it is crucial that the sales process perfectly matches the buyer’s acquisition process, both being mirror images.however, it is important to note that, although they are important, they would not be possible without the other marketing functions and activities that ensure customer loyalty and repeat purchases, which is crucial for generating profits (orrego, 2021). nowadays, modern organizations implement a complex communications system within their various marketing activities. through its mix of communications advertising, sales promotion and personal selling the company manages to reach its target market, whether it be intermediaries, consumers, public opinion, among others. it is crucial for companies to identify “what strategy or content they should use, clearly establishing the objectives, the channel to be used, such as social networks or websites, and selecting the most appropriate formats for the purpose of obtaining sales” (torres et al., 2021). it is also worth considering that the way of “making sales has evolved greatly, one of the most effective means of marketing a product is the internet; this type of transaction is called electronic sales” (calderón et al., 2020). the motivation to purchase arises when a need arises in the consumer, which generates a force to act on the organism, but this only happens when the motivation is extremely strong enough to lead the person to make the purchase. digital advertising can have a profound and positive impact on water home express’ sales. by improving visibility, personalizing campaigns, optimizing in real time, encouraging engagement, taking advantage of viralization, and facilitating the purchase process, the company can experience significant growth in its sales. the key to maximizing the impact of digital advertising on business results is the ability to effectively and efficiently connect with consumers. online advertising has grown exponentially over the past few decades, becoming a fundamental component of many companies’ marketing strategy. in an increasingly digitalized world, online presence has become crucial for brands that want to connect with their audience and increase their market share.this work is expected to not only provide valuable knowledge for entrepreneurship, but also contribute in general to the influence of digital advertising on sales within the drinking water products sector. to carry out this analysis, various research methods will be used, including analysis of sales data before and after the implementation of digital advertising campaigns, customer surveys, and interviews with water home express canoa’s marketing team. the results of this study will provide a clear insight into the effectiveness of digital advertising in the company’s specific context, also offering recommendations for future strategies. based on the above, this study aims to analyze the impact of online advertising on the sales of water home express canoa, a company specialized in the marketing of bottled water in the parish of canoa. activities that will help achieve this objective include 1. recognize the digital advertising tactics used by pa ge 34 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 water home express canoa to promote its products. 2. analyze the effect of digital advertising on increasing the company’s sales. 3. compare the results achieved through digital advertising with other marketing strategies used by water home express canoa. theoretical basis supporting the study: digital advertising interactive advertising between agents is not a new concept, as it has been present in direct marketing, as in the case of telephone marketing. nor has it emerged exclusively with the arrival of the internet or the digital age. however, the network and digital media have considerably expanded the opportunities for this relationship to be established, exponentially multiplying the interactions between advertisers and consumers, as well as between brands and audiences (cuesta & garcia, 2023). interactivity represents both a challenge and a crucial, socially relevant opportunity and becomes an essential driver of change for advertising, being an indispensable aspect of its continuity. digital advertising “is presented on various platforms called digital media, defined as those through which information can be created, observed, transformed and stored on a wide variety of digital electronic devices” (canales, 2020). once the creative concept has been created, digital media is responsible for its dissemination across a wide variety of electronic devices. this is why digital advertising is the only one that strives to create an experience that involves those who use it. digital advertising is, according to grandson (2016), “a large number of cases and, until now, a set of unconnected forms created on the internet and in the first practices of digital tv”. it is a range of actions without a strategy to support them, offering, in addition, content that is not integrated, not to say “disintegrated”, in the digital environment or, in other words, almost added to the digital experiences that have been developed, such as web pages. return on investment (roi) in digital advertising calculating social media roi, most companies start by measuring the cost of launching and engaging on a blog or social network and then calculating their return on that social media investment. according to jimenez (2018): these behaviours can then be considered (and measured) as consumer investments in companies’ social media communication efforts. this suggests that returns on social media investments would not always be measured in financial units, but also in consumer behaviours (consumer investments) depending on the type of social network. these investments include obvious measures such as the number of visits and time spent on the social network, as well as more active investments, such as the number of comments or the number of updates on facebook and twitter pages about the brand. these investments can be used to measure a few key aspects, such as changes in awareness levels or increases in word of mouth over time. return on investment (roi) is a means of proving the contributions that social media generates. however, many benefits provided by social media are not easily measured in dollars and cents, so the return on investment in social media marketing should directly link the goal of your social media presence and the objectives of the organization (garavito et al., 2021). materials and methods the study was considered quantitative in nature, which facilitated the use of dynamic techniques to generate exhaustive criteria for the existing problem and reach solid decisions for the implementation of effective digital advertising. the focus of this topic is to analyze the impact of digital advertising on water home express canoa’s sales. the effect of digital advertising on the company’s sales strategies will be examined, as well as the impact of these strategies on its results. customer perception of the company’s digital advertising and its influence on the purchasing process will also be investigated. the exploratory study analyses the positioning of the water home express canoa brand in san vicente, evaluating consumer perception and the effectiveness of advertising strategies on digital platforms such as social networks and emails. the analytical method accounts for the object of study of the research group that in this work deals, with rigorous documentary research, with the very method that guides its work. this method, used particularly in the social and human sciences lopera et al. (2010) alleges that and the explanatory analytical method allows us to evaluate the impact of online advertising on water home express canoa’s sales. the advertising strategy, digital channels, message, creativity, as well as the reach and segmentation of the target audience must be analysed to understand their effectiveness. the study population was taken from 344 clients, where they are included (60 potential clients and 284 users in their social networks), thus obtaining a sample to be surveyed of 62 individuals. for this purpose, a simple non-probabilistic random sampling was applied, selecting the water home express canoa clients who visited the company during a specific period (march) and who were willing to participate in the survey. in order to collect data for water home express canoa, surveys will be used primarily, targeting participants selected in the sample. these surveys will seek to gather information on the relevance of the content, brand perception and its influence on purchasing decisions, with the aim of improving the understanding of the impact of digital advertising on the local positioning of the brand. spss version 26 will be used to validate the questionnaire and assess its reliability using cronbach’s alpha coefficient. exploratory factor analyses will be performed to identify the structure of the questionnaire and it is recommended to review descriptive statistics and test-retest reliability tests to ensure the stability of the responses. pa ge 35 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 results and discussion the reliability of the instrument was established at 0.846. when reviewing the authors’ literature, it can be observed that they all agree that this value indicates that the instrument is acceptable. however, after carrying out an analysis study of the scale’s elements and eliminating one of the scales, it was observed that cronbach’s alpha increased. subsequently, by eliminating the questions with the highest values, the following result was obtained. table 1: reliability statistics cronbach's alpha cronbach’s alpha based on the typified elements n of elements .856 .846 13 the table presented illustrates the value of cronbach’s alpha, which is a metric used to analyze the internal consistency of a set of items, in this case, a questionnaire composed of 13 elements. the value obtained of 0.856 suggests high reliability, which implies that the items are strongly correlated and consistently assess the same construct or characteristic, offering reliable results. in addition, the cronbach’s alpha calculated from the standardized elements is 0.846, which supports the stability of the measure even after the standardization of the items. a cronbach’s alpha value exceeding 0.8 is considered good internal consistency, indicating that the questionnaire is appropriate for its application, given that the selected items contribute significantly to the evaluation of the concept that is sought to be measured. below, the results corresponding to the frequency of consumption of bottled water will be shown in order to table 2: frequency of bottled water consumption pa ra m et er s fr eq ue nc y va lid pe rc en ta ge c um ul at iv e pe rc en ta ge diary 31 50.0 50.0 valid weekly 22 35.5 85.5 monthly 9 14.5 100.0 total 62 100.0 the table presents bottled water consumption patterns in three frequency categories: daily, weekly and monthly. 50% of respondents indicate that they consume bottled water daily, suggesting a considerable dependence on this product to meet their hydration needs. this trend could be linked to the perception of the quality of tap water or the convenience of opting for bottled water. in contrast, 35.5% of respondents indicate that they use bottled water weekly, indicating moderate consumption, possibly supplemented with other water sources. finally, only 14.5% of respondents consume it monthly, revealing that there is a small group of consumers who use it occasionally, perhaps in specific circumstances. this study shows that half of the population considers bottled water as their main source of hydration, which could be a result of the lack of access to safe drinking water or the convenience that this product offers. below are the results corresponding to the means by which customers first learned about the company. “water home express canoe”, whose results are shown in table 3. table 3: “water home express canoa” knowledge medium parameters frequency percentage valid percentage cumulative percentage valid social networks (facebook, instagram, etc.) 24 38.7 38.7 38.7 internet search (google) 4 6.5 6.5 45.2 recommendation from friends or family 20 32.3 32.3 77.4 traditional advertising (radio, tv, flyers, etc.) 12 19.4 19.4 96.8 digital advertisement 2 3.2 3.2 100.0 total 62 100.0 100.0 the table illustrates how customers first became aware of “water home express canoa”, highlighting the relevance of social media as the main avenue of discovery, with 38.7% of mentions. this data highlights the effectiveness of digital platforms such as facebook and instagram in attracting new consumers. recommendations from friends and family come in second place, with 32.3%, indicating the importance of word of mouth in building trust and the company’s reputation. on the other hand, traditional advertising, which includes radio, television and flyers, remains a relevant method, reaching 19.4% of respondents. however, digital ads, such as google ads, are seen to have a limited impact, representing only 3.2%, while direct internet searches reach 6.5%. this suggests a possible area for improvement in the digital advertising strategy, which could be optimized to increase the company’s visibility online. observe the level of consumption, the results of which are shown in table 2. pa ge 36 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 among other reasons, the table shows a clear inclination towards the use of social media as a fundamental marketing tool, while traditional methods and personal recommendations continue to play a significant role. the company could concentrate on strengthening its digital presence through advertisements and search engine optimization to improve its reach. the results of a survey on the perception of advertising on social networks will be shown, specifically in relation to its informative nature, the results of which are shown in table 4. table 4: advertising on social media is informative parameters frequency percentage valid percentage cumulative percentage valid totally disagree 4 6.5 6.6 6.6 disagree 2 3.2 3.3 9.8 neutral 11 17.7 18.0 27.9 ok 29 46.8 47.5 75.4 totally agree 15 24.2 24.6 100.0 lost total 61 98.4 100.0 system 1 1.6 total 62 100.0 table 7: attractiveness parameters frequency percentage valid percentage cumulative percentage valid totally disagree 2 3.2 3.4 3.4 disagree 2 3.2 3.4 6.8 neutral 12 19.4 20.3 27.1 ok 25 40.3 42.4 69.5 totally agree 18 29.0 30.5 100.0 lost total 59 95.2 100.0 system 3 4.8 total 62 100.0 the table presents various opinions on the perception of advertising on social networks in terms of its informative nature. the data reveal that a portion of respondents do not agree with the idea that advertising on these platforms performs an effective informative function. in particular, a small group stands out that strongly rejects this notion, suggesting an acknowledgement that, in many cases, advertising tends to focus more on encouraging consumption than on offering truthful and objective information. on the other hand, a considerable number of responses are neutral, which can be interpreted in various ways. this neutrality could reflect a lack of a clear or definitive opinion on the matter, which invites us to reflect on the effectiveness of current advertising strategies in capturing users’ attention and establishing a genuine dialogue with them. the low proportion of those who support the statement indicates a lack of confidence in the ability of social media to play a strong informational role. this may present a challenge for brands that use these platforms as their main communication channel, as they may need to reevaluate their approaches to ensure that the advertising they generate not only captures attention, but also fulfills the objective of effectively informing and educating users. we understand that the negative perception and neutral stance of those surveyed show an urgent need in the advertising field to investigate methods that go beyond simply attracting attention and that actually seek to inform and provide value to consumers. this raises important questions about the ethics of advertising on social networks and the responsibility of brands in the way they communicate their message. another question that generated significant results for the present study was the analysis of perceptions on a specific topic, highlighting the different positions of the respondents. it is important to highlight the relevance of this data for decision-making, as well as for the design of future actions or research, the results of which are shown in table 7. analyzing the data related to the perception of the characteristic “attractiveness”, it can be observed that a considerable proportion of respondents are in favor of this notion. a closer look indicates that, although a small percentage are in disagreement or are neutral, the majority of participants are in favor or totally in favor of pa ge 37 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 the statement that something is “attractive”. the existence of individuals in the neutral category suggests that there is a group that is not completely convinced, which could point to the need to further investigate the reasons behind this lack of a clear opinion. however, what is most relevant is that the majority of respondents show a positive tendency towards the concept of “attractive”. this could imply that the characteristics or measures evaluated have managed to effectively capture the attention and interest of the public. furthermore, this trend suggests that favourable perceptions about “attractiveness” may positively impact future decisions related to this topic, fostering an environment in which this characteristic is valued and sought after. it is critical, however, to consider the opinions of those who disagree or are neutral, as understanding their perspectives could lead to adjustments or improvements that further increase perceived attractiveness in the future. in conclusion, while the overall response is positive, the variety of opinions highlights the importance of digging deeper into the overall experience to achieve more universal appeal. another dimension of perception is the credibility of a given topic or source of information. the lack of absolute consensus suggests that this topic is complex and may be influenced by various variables, such as personal experiences or specific contexts, which can be observed in the results shown in table 8. table 8: credibility parameters frequency percentage valid percentage cumulative percentage valid totally disagree 2 3.2 3.3 3.3 disagree 2 3.2 3.3 6.6 neutral 19 30.6 31.1 37.7 ok 22 35.5 36.1 73.8 totally agree 16 25.8 26.2 100.0 lost total 61 98.4 100.0 system 1 1.6 total 62 100.0 table 9: relevance parameters frequency percentage valid percentage cumulative percentage valid totally disagree 3 4.8 4.9 4.9 disagree 3 4.8 4.9 9.8 neutral 15 24.2 24.6 34.4 ok 25 40.3 41.0 75.4 totally agree 15 24.2 24.6 100.0 lost total 61 98.4 100.0 system 1 1.6 total 62 100.0 when analyzing the data presented, there is considerable diversity in the opinions of the respondents regarding the credibility of the topic in question, although the majority does not express a position of absolute disagreement. the number of people who position themselves in a neutral manner suggests a lack of conviction or indecision on the matter, which could indicate that the information provided was not sufficiently persuasive or that they are simply not familiar with the topic discussed. on the other hand, it is noteworthy that a significant portion of respondents are in favour of the claim, which highlights a positive trend towards the perception of credibility. this can be interpreted as an encouraging indication, suggesting that there is a considerable number of individuals who trust the information presented, which could reflect a good level of acceptance of the messages or evidence presented. the low representation of those who oppose it seems to indicate that, at least from the perspective of the majority, there is no strong resistance or scepticism towards the credibility of the issue. this aspect is relevant, since the lack of dissenting voices could suggest that the message has managed to resonate favourably with the majority of respondents. objectively, we deduce that, although a variety of opinions are perceived, the general trend indicates moderate acceptance and notable neutrality, evidencing both the need for a clearer educational approach on the subject and the opportunity to strengthen the areas that have managed to persuade respondents effectively. finally, the relevance dimension provides a detailed view of respondents’ opinions regarding a specific statement related to the topic of interest. this context is relevant to analyze not only the general acceptance of the topic, but also to understand the divisions in the perspectives of the participants, which can be observed in the results shown in table 9. pa ge 38 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 when examining the data presented, a variety of opinions are evident, which demonstrates the diversity of perspectives on the issue in question. the segmentation of the responses indicates that, although there is a considerable number of participants who are between indifference and agreement, there is a general tendency towards acceptance of the statement evaluated. first of all, it is important to note that a portion of the group expressed disagreement with the opinion presented, although their representation is relatively low. this could suggest that, despite some reservations, these are not predominant in the discussion. on the contrary, there is a significant group that expresses itself in favour of the statement, which could indicate that the majority of participants have a positive or favourable perception regarding the subject. furthermore, the fact that a considerable proportion of respondents declared themselves neutral may indicate a lack of information or ambivalence on the issue, which is a relevant aspect to consider. this neutrality can be interpreted as an invitation to further analysis: some participants may not feel sufficiently informed to take a clear position or the statement in question may not resonate directly with them. it was observed that, considering both favourable and neutral opinions, it can be argued that there is ample room for dialogue and reflection on the issue. the majority seems to be in agreement, suggesting that, with adequate communication and discussion, it is feasible to move towards greater cohesion in opinions. however, it is also essential to pay attention to dissenting voices and those who remain indifferent, since their perspective could enrich a more comprehensive understanding of the issue. therefore, the question related to digital platforms helped us generate solid criteria for the problem under study. an argumentative analysis will be presented on how digital advertising has impacted consumers’ decision when choosing the “water home express canoa” water service. this analysis will reflect on the factors that could be contributing to these perceptions and how digital advertising can be a powerful tool, but also subject to personal interpretations or previous experiences, which can be observed in the results shown in table 10. table 10: digital advertising and its influence on the decision to buy water from “water home express canoa” parameters frequency percentage valid percentage cumulative percentage valid totally disagree 2 3.2 3.2 3.3 disagree 1 1.6 4.8 6.6 neutral 9 14.5 19.4 37.7 ok 28 45.2 64.5 73.8 totally agree 22 35.5 100.0 100.0 total 62 100.0 the impact of digital advertising on the choice of specific products, such as “water home express canoa” water, is an issue that requires in-depth analysis. according to the data collected, it is observed that a considerable part of respondents are not completely convinced that digital advertising was a determining factor in their decision to purchase this product. on the one hand, those who expressed an unfavourable opinion about the effectiveness of digital advertising suggest a disconnect between advertising messages and the values or needs they consider when making a purchase. this perception could be influenced by various factors, such as information overload in the digital environment, which can lead to desensitization to advertising. in addition, not all consumers react in the same way to advertising; some may trust more in personal recommendations, opinions of friends or even previous experiences with the brand. at the same time, a notable group of respondents took a neutral stance, suggesting that although they have been exposed to digital advertising, they have not been strongly convinced by it. this ambivalence could reflect a lack of clarity in advertising messages or a perception that the product is not distinguishable from similar ones on the market. in this context, the need to re-evaluate and adapt the marketing strategies of “water home express canoa” with a focus on sales becomes evident. the key lies in developing advertising campaigns that not only capture the consumer’s attention, but also establish an authentic and lasting connection. this could involve the use of more personalized content, customer testimonials, or narratives that highlight the unique benefits of the product. we generalize in an interpretive way that, although digital advertising has considerable potential to influence purchasing decisions, in the case of “water home express canoa”, its effectiveness seems to be limited, so assistance will continue to be provided by the department of links with society belonging to the bahía extension of the universidad laica eloy alfaro de manabí. discussion according to torres et al. (2021) digital advertising has a significant impact on water home express canoa’s sales, underlining the relevance of digital marketing in the current context. this approach is designed to support marketing strategies in order to achieve profitability and customer loyalty through digital technologies. by pa ge 39 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 32-40, 2025 establishing integrated communication and offering online services that fit consumer needs, the company not only increases its visibility, but also strengthens relationships with its customers, aligning with the goal of maximizing business impact in the digital age. according to aliaga (2017) highlights the relevance of digital advertising, which gives the company the ability to analyze the performance of its ads in real time and adjust its strategies accordingly. by having the option to deactivate campaigns that are not working properly and boost those that are performing better, water home express can focus its efforts more efficiently towards its target audience. therefore, tools such as the “facebook pixel” facilitate a thorough analysis of user interaction, which can help further consolidate brand loyalty (zambrano et al., 2022). for leon and vivanco (2022), this implies that, although advertising on social networks has a positive effect, there is still room for improvement in its effectiveness. online sales, through digital platforms, give consumers the opportunity to obtain comprehensive information on available products, which facilitates virtual shopping. therefore, by improving communication on social networks, water home express has the possibility of transforming interest into more solid purchasing decisions and increasing its market share. pairing this quote with the results analysed in the table on the influence of advertising on social networks on the purchasing decisions of water home express canoa products indicates that, although (33.9%) of respondents agree and (27.4%) totally agree, (32.3%) are neutral (carvajal et al., 2021). digital advertising tactics for water home express canoa seem to be in tune with the contemporary trend towards interactivity, a crucial aspect to attract consumer attention. for zaragoza and castellón (2010), they mention that forby motivating users to search for information and entertainment autonomously, the brand manages to establish a more robust and participatory connection. the implementation of a microsite could be a very favourable option, since it offers greater possibilities for creativity and personalization in interactive campaigns, thus promoting a more immersive experience that strengthens customer loyalty and encourages them to actively interact with advertising content (lemoine-quintero et al., 2023). conclusion digital advertising has established itself as a fundamental tool in the contemporary business environment, particularly for companies such as water home express canoa, which seek to increase visibility and optimize sales in a highly competitive market. this thesis demonstrates that the adoption of digital advertising strategies is essential to establish an effective connection with the target audience, which not only facilitates the acquisition of new customers but also promotes loyalty. furthermore, the research has revealed an area of opportunity for strengthening digital marketing strategies, particularly in aspects such as clear communication and the provision of informative content that educates the consumer about the products. despite the positive results in the perception of digital advertising, a considerable percentage of respondents are neutral or dissatisfied with the information provided. therefore, the company must consider creating more attractive and well-founded campaigns that facilitate greater interaction and trust on the part of the customer, thus ensuring that the advertising message not only informs, but also persuades effectively. although water home express canoa has seen a positive impact from digital advertising on its sales, there are still opportunities for improvement in the effectiveness of these strategies, especially in the area of social media. through detailed analysis of interactions and the implementation of more dynamic and interactive tactics, such as personalized microsites, the company could increase its market share and strengthen customer loyalty. references alfaro, k., molina, a., romero, r., & sarabia, g. 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( 2010). advertising in the digital age: the microsite as a strategic factor in online advertising campaigns. comunicar magazine , 5-10. https://doi.org/10.3916/c34-2010-03-12 pa ge 1 pa ge 14 american journal of financial technology and innovation (ajfti) impact of risk perception, overconfidence bias and loss aversion on investment decision-making manoj kumar chaudhary1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4061 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: november 20, 2024 accepted: december 21, 2024 published: february 04, 2025 this study investigates how overconfidence, loss aversion, and perceptions of risk affect investment decisions in the nepal stock exchange. making investment decisions is a complicated process that is influenced by several psychological elements. using structured questionnaires, data was collected from individuals actively involved in stock trading. employing a quantitative approach, the research utilizes a descriptive research design and conducts multiple regression analyses. findings reveal that risk perception significantly impacts investment decisions, with individuals perceiving higher risks displaying a greater propensity to invest in high-risk assets. additionally, overconfidence bias positively influences investment decisions, indicating that individuals with higher confidence levels tend to favour riskier investments. loss aversion bias plays a significant role, as individuals averse to losses prefer investments that minimize potential losses. these results underscore the substantial impact of behavioural biases on investment decision-making, with overconfidence bias exhibiting the most significant influence, followed by risk perception and loss aversion bias. the findings emphasize the importance of psychological biases in understanding investment behaviour. investors, financial advisors, and policymakers can all benefit from understanding how risk perception, overconfidence, and loss aversion affect investment decisions. investors can improve portfolio performance, lessen the chance of financial crises, and make more informed decisions by identifying and correcting these biases. therefore, to encourage more effective and efficient investment decision-making processes, it is critical to increase awareness of these biases and develop measures to mitigate their negative consequences. conducting more studies to examine these biases’ additional dimensions and how they affect investment decisions is advisable. keywords investment decision, loss aversion, overconfidence bias, risk perception introduction standard finance, typically referred to as traditional finance, is based on the (emh) efficient market hypothesis (fama, 1970). eugene francis fama presented a landmark article in 1965, introducing the efficient market hypothesis (emh), which states that stock market returns exhibit excessive fluctuations that depart from the average. vaidya et al. (2022) stress the significance of testing the normality of daily returns within the nepalese stock market, particularly within the framework of the emh theory. the assumption of normality in stock market returns serves as a foundational premise for the emh theory. according to traditional financial theory, investors are presumed to act with complete rationality when making financial decisions. however, it is acknowledged that emotions and psychological factors can occasionally impact these decisions, leading to irrational behavior (kahneman & tversky, 1979). latif et al. (2011) stated that many stock markets deviate from the rules of emh, leading to anomalies. the occurrence of anomalies calls into question the idea of market efficiency and emphasizes the need for more study into the behavioral elements and causes of these anomalies. behavioral finance represents a paradigm shift in the field of finance, departing from the traditional assumption of rationality among investors and instead integrating insights from psychology to understand financial decision-making (kahneman & tversky, 1979). risk perception, overconfidence, and loss aversion are just a few examples of psychological biases that have been found to have a significant impact on investment decision-making. the subjective assessment of potential risks connected to an investment opportunity is known as risk perception. risk perception depends on a person’s knowledge, past experiences, and individual risk tolerance (solvic, 1987). according to broihanne, merli and roger (2014), investors with high-risk perceptions are more likely to allocate funds to low-risk assets, while investors with low-risk perceptions are more likely to allocate funds to high-risk ones. nagriwum et al. (2023) observed that on the ghana stock exchange, gender and nationality diversity significantly influence earnings quality in nonfinancial listed companies, while age diversity has no any notable impact. overconfidence is defined as having a high conviction in one’s judgment, cognitive powers, rational thinking, and intellect. it frequently causes people to overestimate their knowledge and their ability to foresee properly (pompian, 2012). overconfidence is a common bias that influences financial decisions. it alludes to people’s propensity to think highly of themselves and the precision of their 1 school of management, tribhuvan university, nepal * corresponding author’s e-mail: chaudharymanojk52@gmail.com pa ge 15 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 judgments. overconfident investors frequently assume they have superior knowledge and forecasting abilities, which causes them to take unwarranted risks and follow unfavorable investment strategies (odean, 1998). overtrading, insufficient portfolio diversification, and irrational expectations of investing returns can all be caused by the overconfidence bias. another psychological bias that has a significant impact on financial decisionmaking is loss aversion. it describes people’s propensity to prioritize risks over benefits. according to kahneman and tversky (1979), the pleasure experienced from a comparable gain is less keenly felt than the pain of suffering a loss. someone tends to reject a significant loss, so he also tends to concentrate on preventing a loss while gaining profit (pompian, 2012). someone who overreacts to losses is likely to focus more on avoiding losses than on trying to make a profit with their investments. the combined impacts of risk perception, overconfidence, and loss aversion on investment decisions deserve investigation even if each of these factors has been researched separately. as an example, overconfidence can increase the impact of loss aversion, making investors even warier of prospective losses and thus encouraging irrational risk-taking behaviors (barber & odean, 2001). understanding how these biases interact and interact with one another helps give an in-depth understanding of the underlying mechanisms influencing investor behavior. this study adds to the corpus of knowledge in behavioral finance by expanding our understanding of how risk perception, overconfidence, and loss aversion affect investing decisions. it also has practical ramifications for investors and financial professionals. the ultimate objective is to encourage more educated, logical, and efficient investment decision-making processes, which will result in better portfolio performance and a more robust financial ecosystem. literature review overconfidence bias overconfidence bias is the tendency of people to overestimate their abilities, knowledge, and the accuracy of their judgments or conclusions (barber & odean, 2001). overconfident investors frequently have unrealistic expectations for their investments’ outcomes, resulting in overestimated rewards and underestimated investment hazards. due to overconfidence bias, inadequate portfolio diversification, excessive trading, and poor investment performance may occur (odean, 1998). research has repeatedly shown that investors across a range of markets and investment groups exhibit an overconfidence bias. for instance, barber and odean (2001) discovered that ordinary investors frequently exhibit greater levels of overconfidence in their trading skills than professional investors. the prevalence of this bias was further highlighted by grinblatt and han (2001) finding that overconfidence is prevalent among both beginner and experienced investors. overconfidence is a common cognitive distortion that leads investors to overestimate their abilities and knowledge in the financial domain (kumar dahal, 2022). due to this bias, people tend to overestimate their abilities and frequently overlook important facts and data, leading them to believe they are better than established norms (kartini & nahda, 2021). dangol and manandhar (2020) have identified overconfidence bias as one of the four heuristic biases they analyze. a common cognitive bias that influences investment decisionmaking is overconfidence bias, which is characterized by overestimation, over-placement, and over-precision. “the failure to acknowledge the bounds of one’s knowledge” is how it is characterized (dangol & manandhar, 2020). the research also finds a strong correlation between overconfidence bias and irrationality in investing decisionmaking. overconfidence bias, as highlighted by tamang (2022), leads investors to overestimate their analytical skills and the reliability of their information. this can adversely affect investment decisions by causing investors to overlook risks and make overly optimistic assessments, ultimately undermining portfolio performance. due to the overconfidence bias, nepalese investors in initial public offerings (ipos) tend to overestimate their investment abilities (tamang, 2022). risk perception as people assess and understand the potential risks connected to various investment possibilities, risk perception plays a vital role in investment decisionmaking. according to wynne (1987), risk perception is an appraisal that is subjective and impacted by one’s own experiences, knowledge, and risk tolerance. depending on how they perceive risk, different investors may assess the same investment opportunity uniquely. according to weber and milliman’s (1997) research, those who perceive risk more highly tend to manage their portfolios more cautiously, selecting low-risk investments. on the other hand, investors who see risk less favorably can be more willing to accept greater amounts of risk. these results underline the importance of risk perception in influencing investing decisions. hui and sang (2024) demonstrated that combining textual and financial indicators increases the accuracy of risk assessment, and deep learning is essential for improving financial risk prediction and supporting strategic decision-making. sapkota (2022) suggests that risk perception plays a crucial role in influencing investors’ decisions to invest in stocks. investors may hesitate to invest if they perceive higher risk, whereas lower perceived risk may encourage investment. hamid et al. (2013) found that risk propensity positively affects risk-taking behavior, thereby impacting stock investment decisions. individuals who are more willing to take risks are more likely to engage in riskier investment strategies when it comes to stocks. the study by vaidya et al. (2022) explores the correlation between risk tolerance and demographic factors such as gender, education, age, income, and occupation. the findings pa ge 16 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 reveal that men exhibit a higher inclination towards risk-taking compared to women. moreover, educated individuals tend to display a greater appetite for risk. additionally, the research highlights that age plays a significant role in determining risk tolerance levels. furthermore, it suggests that investors with lower wealth levels tend to have lower risk tolerance levels in contrast to wealthier investors (vaidya et al., 2022). in the context of the stock market in nepal, rana (2019) highlights the profound impact of perceived risk on investors’ behaviour. specifically, investors in nepal demonstrate a heightened sensitivity to financial risk and the potential for opportunity loss compared to other forms of risk. this emphasis on financial risk and opportunity loss significantly influences their investment decisions. moreover, varying levels of risk perception among investors lead to divergent investment behaviours within the nepalese stock market. understanding and managing risk perception is crucial for investors to make informed investment decisions in the stock market of nepal (rana, 2019). loss aversion another psychological bias that has a significant impact on financial decision-making is loss aversion which kahneman and tversky identified. according to kahneman and tversky (1979), loss aversion is the propensity for people to prioritize costs over benefits. a loss causes more pain than a similar gain, which causes greater pleasure. this asymmetry in the perception of risk and reward has significant implications for investment behavior, leading to conservative decision-making and reluctance to realize losses. loss-averse investors exhibit risk-averse behavior and tend to hang onto losing assets for extended periods, according to research by shefrin and statman (1985). they discovered that fear of losses can result in poor investing decisions and decreased portfolio performance. odean (1998) investigated loss aversion, a behavioral bias influencing investors’ decisions to sell assets. he found that investors often resist selling assets that have declined in value, driven by a preference to avoid losses over acquiring gains. this reluctance affects individual trading behavior in financial markets, underscoring the significant impact of loss aversion on investment decisions. barberis et al. (2001) found that losses following gains are perceived as less distressing, while losses following losses are particularly painful. this bias can lead investors to hold onto losing investments longer than warranted, a phenomenon known as the disposition effect. recognizing and addressing loss aversion is crucial for improving investment decisions and market efficiency. loss aversion is a psychological principle where the fear of loss is considered to be twice as impactful as the potential for an equivalent gain (pompian, 2012). like this, a study by edwards and roy (2017) revealed that risk-averse investors are more likely to exhibit herding behavior, following the decisions of others rather than making their investments. this suggests that risk aversion influences not only the investment decisions themselves but also how investors make those decisions, leading to herding behavior in the stock market. despite individual research on risk perception, overconfidence, and loss aversion, understanding their interaction in influencing investment decisions is crucial. overall, research suggests that factors like overconfidence, loss aversion, and risk perception significantly impact investors’ decision-making processes. recognizing the interplay between these biases can provide insights into investor behavior. future studies should continue exploring the influence of these biases on investment decisions and develop strategies to mitigate their effects. conceptual framework figure 1: conceptual framework source: nur aini & lutfi (2019) the conceptual framework shows the connections and interactions among overconfidence, loss aversion, and perception of risk in investing decision-making. it offers a structured analysis of the crucial factors and elements necessary to comprehend how these biases affect the procedures and results of investment decision-making. a conceptual model has been prepared and presented in figure 1. the following hypotheses have been proposed: pa ge 17 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 research hypothesis risk perception and investment decision investments with higher degrees of risk often have the potential for higher returns, which is a well-known phenomenon (chung & chuwonganant, 2014). investors who choose riskier assets often have the potential to earn higher profits if those investments perform well. this principle underscores the importance of balancing risk and potential reward in investment decisions. sapkota (2022) states that risk perception has a significant positive influence on stock investment decisions among investors. according to research by hamid et al. (2013), risk propensity has a positive impact on risk-taking behavior, which influences stock investment decisions. the study conducted by aren and zengin (2016) emphasizes the substantial influence of an individual’s risk perception on their investment decisions, indicating that people’s perceptions of risks directly shape their investment behavior. h1 there is a significant positive impact of risk perception on investment decisions. overconfidence bias and investment decision barber and odean (2001) found that average investors often display higher degrees of overconfidence in their trading abilities compared to experienced investors. sapkota (2022) states that overconfidence has a significant positive influence on stock investment decisions among investors. in behavioral economics, overconfidence bias the tendency for people to overestimate their abilities has received a great deal of research (kumar dahal, 2022). overconfidence bias has a considerable impact, causing people to make decisions based on an exaggerated sense of confidence that, while improving investment decisions, carries a high risk (gurung et al., 2024). aryal (2021) concludes that the only factor significantly affecting nepali investors’ investment performance is overconfidence bias. according to dangol and manandhar (2020), investors and financial professionals need to be aware of the impact of overconfidence bias to make more rational investment decisions. overconfidence bias, in particular, has been identified as a common cognitive bias that affects investment decision-making and can lead to suboptimal outcomes (dangol & manandhar, 2020). these findings highlight the importance of understanding and managing overconfidence bias in investment decision-making to improve rationality and avoid suboptimal outcomes. pandit (2021) highlights how overconfidence bias influences investment decisions, leading investors to overestimate their forecasting accuracy due to illusions of knowledge and control. despite the lack of significant association with experience levels, overconfidence bias can affect various aspects of investment behaviour, such as trading frequency and decision-making, potentially causing security prices to deviate from fundamentals. this underscores the importance of investors critically evaluating their confidence levels to mitigate the impact of overconfidence on their strategies (pandit, 2021). overconfidence bias has a notable influence on investment decision-making, as individuals tend to overrate their competencies and misinterpret data (tamang, 2022). this tendency can result in investors assuming undue risks and making less-than-ideal investment selections. additionally, those affected by overconfidence bias may place less emphasis on fundamental or technical analyses, instead turning to sources like social media or personal networks for investment insights. recognizing overconfidence bias is pivotal for fostering sound investment decisions. a comprehensive understanding of personal biases, including overconfidence bias, is imperative for achieving favourable investment outcomes (tamang, 2022). various studies, including adielyani and mawardi (2020), desrita (2022), madaan and singh (2019), and sapkota (2022), have consistently found that overconfidence, which leads individuals to overestimate their own abilities, has a significant positive impact on stock investment decisions. h2 there is a significant positive impact of overconfidence bias on investment decisions. loss aversion on investment decision prospect theory explains the loss aversion bias, which is characterized by the tendency to quickly sell winning stocks and retain losing stocks (odean, 1998). according to kahneman and tversky (1979), loss aversion is the propensity for people to prioritize costs over benefits. their psychological theory provides a thorough understanding of how emotions and cognitive biases impact financial decisions by clarifying how people evaluate possible losses and gains. jain et al. (2020) pinpointed loss aversion as one of the primary biases influencing investment decisions, which is further supported by sapkota (2022). loss aversion, positively associated with stock investment decisions (sapkota, 2022), has consistently been found to have a significant positive impact on stock investment decisions across multiple studies (hossain & siddiqua, 2022; khan, 2017; kumar & babu, 2018; mahina et al., 2017). prospect theory explains investors’ risk-averse tendency to hold losing stocks and sell winning stocks. compared to a gain of the same dimension, loss causes more fear. people prioritize possible losses over equivalent profits because they perceive gain and loss as unbalanced factors, a tendency known as loss aversion (gurung et al., 2024). h3 there is a significant positive impact of loss aversion on investment decisions. materials and methods the study’s target population consists of investors who are involved in stock trading in the nepal stock pa ge 18 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 exchange. the method of collecting primary data was through self-administered questionnaires. since the objective was to investigate the influence of various biases on investment decision-making, the researcher specifically targeted respondents who were investors for a certain period, ensuring that non-investors were excluded from the sample frame. the data has been gathered using a cross-sectional survey approach because it was done at a particular moment in time. the 5-point likert scale is used as the basis for the structured questionnaire’s design. using this scale, participants may express how much they agree or disagree with a set of statements or questions in a standardized way. sections on demographic data, risk perception, overconfidence bias, loss aversion, and investment decisions were included in the questionnaire. to determine the sample size of the population, the rule of thumb proposed by roscoe (1975) is used. following this rule, 120 investors are taken as a sample. a closed-ended questionnaire was created and distributed to respondents, resulting in a sample of 120 respondents collected from nepse investors. this study has adopted a quantitative research method where a descriptive research design has been used to describe behavioral biases that affect the investment decisions of investors and a causal research design has been implemented to test the degree of impact of those independent variables on the dependent variable. cronbach’s alpha was used to evaluate the instrument’s reliability. the research design has enabled descriptive analysis and hypothesis testing using multiple regression analysis between stated independent variables and dependent variables. to conduct further research, social science research software, spss, was utilized. additionally, correlation and multiple regression tests were employed to explore the relationships between various variables in greater detail. results and discussions the data analysis findings are presented and discussed in this section which includes reliability, correlation analysis, and regression analysis, summary of hypotheses, findings and conclusion. reliability the reliability of the model was tested with the help of spss. this analysis is measured by cronbach’s alpha. swkaran (2000) defines cronbach’s alpha as a reliability measure assessing the relationship between items on a scale. a value above 0.6 is typically deemed acceptable for reliability analysis, indicating sufficient correlation among scale items to reliably measure the same underlying construct. cronbach’s alpha values are generally interpreted on a scale where scores from 0.8 to 0.9 indicate excellent reliability, scores from 0.7 and 0.8 are considered good, and scores between 0.6 0.7 are acceptable. higher values indicate greater internal consistency, signifying that the items are highly correlated. table 1: reliability statistics factors on scale cronbach's alpha no. of items risk perception 0.690 5 overconfidence bias 0.763 5 loss aversion 0.613 4 investment decision 0.713 4 in table 1, cronbach’s alpha of all 18 of these variables was above 6 which means that there was strong internal reliability was strong among the items. the reliability statistics table uses cronbach’s alpha to assess how consistently items within different scales measure their intended constructs. risk perception achieves an alpha of 0.690 with 5 items, indicating moderately acceptable internal consistency. overconfidence bias shows strong reliability with an alpha of 0.763 across 5 items, suggesting these items reliably measure overconfidence. loss aversion exhibits lower but potentially acceptable reliability with an alpha of 0.613 from 4 items, indicating room for improvement in item consistency. similarly, the investment decision scale demonstrates acceptable reliability with an alpha of 0.713 measured by 4 items, showing effectiveness in measuring investment tendencies. overall, while all scales are acceptable for research purposes, enhancing the reliability of scales with lower alpha values, such as loss aversion, could improve their accuracy in measuring psychological constructs. correlation correlation analysis is a statistical technique used to evaluate the strength and direction of the relationship between two variables. the outcome, known as the correlation coefficient, ranges from -1 to 1. a coefficient near 1 signifies a strong positive relationship, where an increase in one variable corresponds to an increase in the other. a coefficient near -1 indicates a strong negative relationship, where an increase in one variable corresponds to a decrease in the other. a coefficient around 0 implies no linear relationship between the variables. this analysis aids in understanding how variables are interconnected, supporting decision-making and predicting future trends. significant correlations, often highlighted in studies, indicate statistically meaningful relationships that are unlikely to be due to random chance. the study measured the relationship between the variables using pearson’s coefficient of correlation. pa ge 19 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 table 2 reveals relationships between variables: a moderate positive correlation exists between risk perception and overconfidence bias, and a strong positive correlation is observed between risk perception and investment decision. the correlation table outlines the relationships between risk perception (rp), overconfidence bias (ob), loss aversion (la), and investment decision (id), including their means and standard deviations. rp, with a mean of 3.21 and a standard deviation of 0.804, shows significant correlations with ob (0.446**) and id (0.809**). ob, having a mean of 3.45 and a standard deviation of 0.486, significantly correlates with rp (0.446**) and id (0.750**). la, which has a mean of 2.93 and a standard deviation of 0.477, significantly correlates with id (0.363**). id, with a mean of 3.32 and a standard deviation of 0.484, has significant correlations with rp (0.809**), ob (0.750**), and la (0.363**). the significant correlations, denoted by **, indicate strong relationships among these variables, especially between rp and id, and ob and id. these findings suggest that individuals perceiving higher risk levels may also exhibit greater overconfidence and make more investment decisions. the strong correlations (**, p < 0.01) affirm the reliability of these observed relationships, with less than a 1% chance that they are random. these results demonstrate how psychological factors such as risk perception and overconfidence bias significantly influence specific investment decisions, underscoring the crucial role of these traits in financial decision-making. regression the regression analysis has been carried out to assess the impact of different independent variables on investment decisions. regression analysis is a statistical technique used to investigate the relationships between a dependent variable and one or more independent variables, aiming to understand how variations in the independent variables influence the dependent variable. popular types include simple linear, multiple linear, logistic, and polynomial regression. essential elements are the dependent variable, independent variables, regression coefficients, and r-squared value. the steps involve specifying the model, estimating the coefficients, validating the model, and interpreting the results. in the context of investment decisions, regression analysis can evaluate how factors like interest rates and market volatility impact investment decisions, aiding in strategic planning and predictions. table 2: correlation variables mean std. dev. rp ob la id 1. risk perception (rp) 3.21 .804 1 2. overconfidence bias (ob) 3.45 .486 .446** 1 3. loss aversion (la) 2.93 .477 .063 .175 1 4. investment decision (id) 3.32 .484 .809** .750** .363** 1 **correlation is significant at the 0.01 level (2-tailed) table 3: regression models intercept regression coefficients of r2 f ab pab ob 1 1.756 0.487 .655 91.077 (0.000) * (0.000) * (0.000) 2 0.745 0.746 .562 61.527 (0.029) * (0.000) * (0.000) * 3 2.242 0.368 .132 7.286 (0.000) * (0.010) * (0.010) * 4 0.508 0.357 0.483 .843 126.654 (0.015) * (0.000) * (0.000) * (0.000) * 5 0.865 0.475 0.318 0.75 71.541 (0.001) * (0.000) * (0.000) * 3 (0.000) * 6 0.180 0.704 0.242 .117 4.853 (0.638) (0.000) * (0.012) * (0.010) * 7 -0.81 0.437 0.252 0360 .903 143.182 (0.680) (0.000) * (0.000) * (0.000) * (0.000) * *denote that the results are significant at a 1 per cent level of significance pa ge 20 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 shows an intercept of 0.180 with significant coefficients for pab and ob, explaining 11.7% of the variance. model 7 features an intercept of -0.810 with significant coefficients for ab, pab, and ob, explaining 90.3% of the variance with a highly significant f-statistic. overall, these regression models assess the relationships between predictors (ab, pab, and ob) and a dependent variable. the significance of intercepts, coefficients, r2 values, and f-statistics offers insights into the models’ fit to the data and the predictive strength of the variables in explaining variability in the dependent variable. all models demonstrate statistical significance at a 5% level. summary of hypothesis a total of 3 hypotheses were examined and the outcomes are presented in table 4. it was found that all these hypotheses were supported at a significance level of 5 per cent. in other words, most of the hypotheses yielded statistically significant results, indicating a strong relationship between the variables being tested. the significant findings highlight the interconnected nature of these variables and their impact on each other, supporting their theoretical underpinnings and suggesting potential implications for further research or practical applications in relevant domains. table 3 presents the outcomes of regression analysis with 7 distinct models, showcasing intercepts, regression coefficients, r-squared values, and f-statistics for each. these models examine various combinations of independent variables with the dependent variable, providing insights into their strength and significance. the table presents multiple regression models analyzing the associations between predictors and a dependent variable. the r2 values indicate how well each model explains the variance in the dependent variable, with higher values suggesting a better fit. the f-statistics test the overall significance of the models, highlighting the strength of predictors in explaining variability. model 1 has an intercept of 1.756 and significant coefficients for ab and ob, explaining 65.5% of the variance with a highly significant f-statistic. model 2 features an intercept of 0.745 with significant coefficients for pab and ob, explaining 56.2% of the variance. model 3 shows an intercept of 2.242 with a significant coefficient for pab, explaining 13.2% of the variance. model 4 displays an intercept of 0.508 with significant coefficients for ab, pab, and ob, explaining 84.3% of the variance with a highly significant f-statistic. model 5 indicates an intercept of 0.865) with significant coefficients for ab, pab, and ob, explaining 75.3% of the variance. model 6 table 4: summary of hypothesis hypothesis results h1: there is a significant positive impact of risk perception on investment decisions. supported h2: there is a significant positive impact of overconfidence bias on investment decisions. supported h3: there is a significant positive impact of loss aversion on investment decisions. supported findings the research findings emphasize that behavioral biases, such as risk perception, overconfidence bias, and loss aversion, significantly shape investment decision-making processes. risk perception plays a pivotal role as individuals who perceive higher risks tend to favor investments with potentially higher returns but also greater volatility. this reflects their willingness to take on risk based on their subjective assessment of market conditions and asset performance. similarly, overconfidence bias influences investment decisions by leading individuals to overestimate their abilities and underestimate risks, thereby opting for riskier investments. overconfidence bias can result in excessive trading and suboptimal investment outcomes, as highlighted in studies by odean (2011) and others, which link overconfidence to increased trading frequency and poorer performance over time. loss aversion, on the other hand, manifests as a preference for investments that minimize potential losses rather than maximizing gains. investors exhibiting this bias are more likely to avoid risky assets that could result in significant losses, even if those investments offer higher potential returns. this cautious approach is rooted in the psychological discomfort associated with financial losses, as documented by behavioral economists such as kahneman and tversky (1979). overall, these biases collectively exert substantial effects on investment decisions, particularly among active investors who frequently engage in financial markets. the consistency of these findings across various studies, including those by odean (1998), bondt and thaler (1985), and barber and odean (2001), underscores the robustness of these behavioral influences in shaping investor behavior globally. however, recent studies, such as aryal (2021) work on nepalese investors, provide nuanced insights into how cultural and regional factors can moderate these biases. aryal (2021) findings suggest that while loss aversion is generally significant, cultural contexts may influence the extent to which investors are willing to tolerate losses. such insights highlight the need for a comprehensive understanding of behavioral biases in diverse economic and cultural environments, informing more tailored investment strategies and policy interventions aimed at mitigating biased decision-making in financial markets. conclusion the research findings underscore that investment decisions are significantly shaped by psychological factors such as risk perception, overconfidence, and loss aversion. risk perception influences individuals to favor investments with higher potential returns but also higher pa ge 21 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 14-22, 2025 volatility, reflecting their subjective assessment of market risks. overconfidence biases investors to overestimate their abilities and underestimate risks, leading them to favor riskier assets with potentially greater rewards. this tendency can result in increased trading and suboptimal investment outcomes over time, as documented in behavioral finance literature. moreover, loss aversion plays a crucial role as investors tend to prioritize avoiding losses over seeking gains. this bias leads them to choose investments that minimize potential losses, even if those investments may offer lower returns compared to higherrisk alternatives. the psychological discomfort associated with financial losses, as outlined by kahneman and tversky’s prospect theory, underscores why investors often make conservative decisions to protect against potential losses. however, the study acknowledges several limitations. these include potential biases in self-reported data, which may skew the accuracy of responses regarding risk perception, overconfidence, and loss aversion. additionally, concerns are raised about the representativeness of the sample used in the study, which may limit the generalizability of the findings to broader investor populations. the cross-sectional study design is also noted for potentially overlooking changes in biases and investment decision-making over time, suggesting a need for longitudinal studies to capture these dynamics more comprehensively. despite these limitations, the research employs a systematic and rigorous approach to investigating how behavioral biases impact investor decisions. by shedding light on the roles of loss aversion, overconfidence, and risk perception in shaping investment behavior, the study contributes valuable insights to behavioral finance literature. these insights are crucial for financial professionals and investors seeking to better understand and navigate the complexities of market behavior influenced by psychological biases. future research should continue to explore these biases across diverse markets and under varying economic conditions to deepen our understanding of their implications for investment outcomes. references adielyani, d., & mawardi, w. 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(1987). risk perception, decision analysis, and the public acceptance problem. risk management and hazardous waste, 356-396. https://doi. org/10.1007/978-3-642-83197-3_11 pa ge 1 pa ge 15 american journal of financial technology and innovation (ajfti) integrating financial and textual indicators for enhanced financial risk prediction: a deep learning approach huang hui1*, lim thien sang2 volume 2 issue 1, year 2024 doi: https://doi.org/10.54536/ajfti.v2i1.2489 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: februaer 02, 2024 accepted: march 10, 2024 published: march 12, 2024 the study evaluates the effectiveness of financial indicators in financial risk prediction and develops a framework using financial and textual data. it emphasises the importance of both data types in risk assessment and prioritises liquidity and industry specific metrics. the analysis of the existing literature affirmed the significance of both data types in risk assessment. the findings of the study revealed a strong correlation between financial and textual indicators. the selection of deep learning was based on its adeptness in handling diverse unstructured data, justifying its application. this innovative methodology enhances financial risk prediction and supports strategic decision-making.keywords financial indicators, textual indicators management discussion and analysis, deep learning, financial risk prediction introduction predicting financial risk is a crucial problem in finance since it enables companies, investors, and governments to make wise choices and avert possible financial disasters. according to the study by mashrur et al. (2022), the process of accurately predicting financial risk is complex. it depends on a number of variables, including textual data and conventional financial indicators (mashrur et al., 2020). al-eitan et al. (2019) have highlighted that financial analysts have traditionally based their assessments of a company’s financial health and risk on traditional financial indicators, including liquidity ratios, leverage ratios, and return on assets (roa). however, fridson & alvarez, (2022), has noted that financial indicators sometimes give inconsistent signals in real-world situations, making risk prediction a challenging endeavor (al-eitan & banikhalid, 2019). another issue highlighted by arnold et al. (2022), that threatens the stability of prediction models is the multicollinearity of financial indicators and worries about missing data. indicators for cross-border risk assessment are gradually being standardized through the adoption of international accounting standards like ifrs (arnold et al., 2022; phan et al., 2018). textual information, such as sentiment analysis and language from financial news articles, is increasingly important for predicting financial risk (bawa, 2023). textual data changes in regulatory stance and management tenor might be crucial in anticipating financial risk (feyen, 2023). however, humphreys & wang, (2018), has stressed that issues like bias in sentiment analysis and mistakes in reporting must be resolved. additionally, there is still little research on how textual indicators interact with certain financial metrics like roa or solvency ratios (feyen et al., 2023; humphreys & wang, 2018; karas & režňáková, 2020). malekloo et al. (2022), has stated that these components’ intricate interrelationships call for in-depth examination. the study further highlights that with the introduction of big data and advancements in artificial intelligence, the existing financial environment is changing quickly (malekloo et al., 2022). therefore, it is crucial to investigate cutting-edge methods for estimating financial risk that may make use of both financial and textual data (xing et al., 2018). according to mai et al. (2019), comparing the effects of both types of indicators on predicted financial performance, can close the gap between established textual data analysis and traditional financial analysis. furthermore, it also stresses that the goal is to construct more reliable risk prediction models by using the synergy between these components as well as their separate contributions (mai et al., 2019). the study neale, b. (2021). the craft of qualitative longitudinal research: the craft of researching lives through time. neale et al. (2021), has explored the dynamic nature of financial markets and the demand for cutting-edge instruments to negotiate their complexities serve as the driving forces behind this inquiry. the study has also provided insights at how deep learning can integrate financial and textual indicators, to provide insightful information for enhanced financial risk prediction techniques (neale, 2021). financial risk indicators the process of predicting financial risk is complex and involves a number of different indicators and variables (henrique et al., 2019). peng & huang (2020) state the financial risk prediction procedure includes a number of processes that evaluate the possible risks a firm can encounter on its financial path (peng & huang, 2020). it 1 chongqing vocational college of finance and economics, china 2 department of finance, university of sabah, china * corresponding author’s e-mail: huang_hui_@outlook.com pa ge 16 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 is crucial to comprehend how this approach will affect a company’s stability and operations (lee et al., 2022). in this process, & riyanto (2020), emphasizes that liquidity ratios, such as the quick ratio and current ratio, measure a company’s short-term solvency while nukala& prasada (2021), emphasizes that leverage ratios, such as debt-to-equity ratios, evaluate its long-term financial structure and have significant importance in the financial risk prediction process. the importance of striking a balance between these ratios has also been emphasized by dianova & nahumury, (2019), as high leverage can increase the risk of financial distress and low liquidity can make it difficult for a firm to meet immediate obligations. however, there may also be some shortcomings that financial risk experts need to investigate (dianova & nahumury, 2019; maisharoh & riyanto, 2020; nukala & prasada rao, 2021). as stephany et val. (2023) noted, financial indicators can give conflicting signals when assessing risks, hence it is important to carefully examine these signals when predicting financial risk (stephany et al., 2020). such as mohamed, (2022), underlined the necessity for a nuanced interpretation and an investigation of deeper underlying concerns if a business displays a high return on assets (roa) despite bearing a significant debt load (mohamed, 2022). multicollinearity among financial indicators, which show strong correlations, is one such significant issue. lasso regression is one of the statistical methods that urdes et al. (2022) devised to deal with multicollinearity and increase the resilience of prediction models. these techniques aid in separating the web of connected indications (urdes et al., 2022). however, regularity in the data is necessary for the implementation of such procedures, which is frequently disrupted by missing values. winsemius et al. (2018), illustrates how assessing financial risk may be hampered by missing or inadequate data. data imputation and amputation are two techniques that washburn et al. (2018) cover in their discussion of viable approaches to this problem. these techniques simplify dataset reconstruction and enable more thorough risk assessments (washburne et al., 2018; winsemius et al., 2018). the selection of accounting standards is yet another important issue that needs to be carefully taken into account (weygandt et al., 2018). the decision between international accounting standards like ifrs and nationspecific elements has relevance in the globalized financial landscape for standardizing indicators in cross-border risk assessment. swanepoel (2018), has looked at how these decisions may affect how reliable and comparable risk assessments are in different international contexts. zio (2018), has stated that construction of reliable risk models requires a thorough understanding of various financial risk prediction components and how they interact. the study has further explored the collective knowledge of the field and increase our understanding of financial risk prediction by combining ideas from various academic studies (chen et al., 2021; swanepoel, 2018). implication of textual indicators in financial risk prediction textual indicators, such as managerial tone and tone indexes, are becoming more and more important parts of the process of predicting financial risk (zhang et al., 2022). they have been cited as playing crucial roles in improving risk assessment by several academics. according to. iqbal & riaz (2021), the management’s tone of a company’s communications, including annual reports or press releases, might offer insightful information (iqbal & riaz, 2021). investor confidence and subsequent financial performance can be impacted by positive or negative management attitude (platonova et al., 2018). additionally, biases in textual data may be inherent and result from biased reporting or inaccurate sentiment analysis. it is important to note that financial experts are aware that manual evaluation of textual data might be more effective at eliminating these biases (metaxa et al., 2021) . this practical method enables a more precise analysis of subtle textual clues. textual indicators also interact with financial measurements like return on assets (roa) and return on equity (roe), therefore they do not exist in a vacuum (alduais, 2022). these interactions, as explained by zio (2018), influence the results of risk assessments and give a comprehensive picture of a company’s financial health. the study highlights that professionals may collect nuanced information, improve decision-making, and lessen data biases by including textual indicators into the financial risk prediction process (zio, 2018). this integration acknowledges the importance of textual data in the modern financial sector while reflecting the changing environment of risk assessment. literature review financial risk prediction is a crucial field of research in finance and economics, having important consequences for organizations, investors, and decision-makers (win et al. 2018). arnold et al. (2018), explains that financial risk forecasting heavily relies on historical financial data. risk assessment is based on historical financial performance, which includes income statements, balance sheets, and cash flow statements (goh et al. 2022). to analyze historical data and spot patterns, time series analysis and statistical models like autoregressive integrated moving average (arima) and garch have been used (alghamdi et al. 2019). financial risk is significantly influenced by market volatility and macroeconomic variables (fang et al. 2018). gu et al. (2020), credit risk and asset values are influenced by changes in the stock market, changes in interest rates, and macroeconomic indicators like the gdp growth rate. according morad et al. (2019) a crucial component of financial risk assessment is credit risk prediction. in assessing loan defaults, variables including credit ratings, debt ratios, and default probability are crucial. support vector machines and neural networks are two examples of machine learning techniques that are increasingly being used in credit risk analysis (teles et pa ge 17 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 al. 2021). chang & wang (2018), highlights that he use of sentiment analysis and news sentiment data as fresh indicators of financial risk has also grown in popularity. news and social media attitude can influence the state of the market and the value of assets (masuda et al. 2022). in essence, components that can anticipate financial risk include sentiment analysis, credit risk indicators, market and macroeconomic conditions, and historical financial data. by combining these elements with cutting-edge analytical methods, risk assessments can be improved, assisting decision-makers (terzi et al. 2019). integrating financial and textual indicators for financial risk prediction the effects of combining financial and textual data in financial risk prediction are extensive. for a thorough risk assessment, certain businesses need specialised financial criteria (nyman et al. 2021). risk prediction is greatly influenced by historical financial performance, including debt ratios and earnings stability (sathyamoorthi, 2022). when projecting financial risk, objective financial health metrics like liquidity and solvency ratios frequently outperform market sentiment, especially during economic downturns (nazareth & reddy, 2023). when assessing financial stability, short-term financial measures like liquidity ratios take center stage (zorn et al. 2018). sector-wide statistics, however, may outperform firm-specific indicators in high-risk circumstances (flachenecker l et al., 2020). the accuracy and reliability of risk prediction are improved by text indicators, such as sentiment analysis and tone indices (zhang et al., 2022). according to mushtaq et al. (2022), risk evaluations are influenced by how management tone and language sentiment interact with financial measures like roa and roe. the study further highlights that the efficiency of textual indicators is impacted by legislative changes and current affairs. furthermore, manual data review can be used to address possible biases in textual data, such as sentiment analysis errors and reporting biases (díaz et al. 2018). with consequences that vary among industries, historical settings, and market dynamics, the combination of financial and textual indicators enhances the forecast of financial risk (cavalcante et al. 2016). additionally, there is a huge area of study that will improve this integration, increasing the accuracy of risk assessment models. analyzing financial and textual indicators relationship through deep learning approach understanding financial risk has been transformed by the convergence of deep learning, big data, and analysis of financial and textual indicators (fi and ti) (kim et al. 2022). the prediction of risk has now expanded in new directions with the introduction of deep leaning methods including python and big data technologies (abkenar et al. 2021). according to zhou et al. (2021), python’s machine learning packages make it easier to build deep learning models for integrating fi and ti. massive datasets, such as real-time financial reports and textual data from news and social media sources, may be collected and stored using big data systems (hariri et al. 2019). recurrent neural networks (rnns) and transformers are examples of deep learning approaches that improve fi-ti synergy by automatically discovering complex correlations (lienhard et al. 2022). for the purpose of capturing complex market emotions and financial health indicators, taleb et al. ((2018). highlights that a process both unstructured textual data and structured financial data is required. the above approach takes into account the dynamic relationships between fi and ti to assist fast risk assessment. integration of these technologies offers more precise and responsive financial risk models as python and big data continue to develop (fu et al. 2021). literature gap and hypothesis development the observed gap in the literature and the theoretical groundwork extracted to the literary analysis serve as a strong foundation for the hypotheses developed in this study. the analysis of the literature found a paucity of thorough studies integrating both financial and textual indicators for improved financial risk prediction using deep learning techniques. the work uses well-established financial risk prediction theories and models to close this gap while also recognizing the growing importance of textual indicators. the foundation for the assumptions comes from theoretical frameworks including altman’s z-score model, beaver’s financial ratios, and contemporary deep learning methods. to fill the current research gap, these hypotheses reflect an original strategy that blends conventional financial analysis by employing the financial and textual indicators through state-of-theart deep learning techniques. based on these observations the following hypothesis are formed: hypothesis 01: financial risk prediction factors like financial and textual indicators has significant positive trends over the years. h2: financial indicators has significant positive impact on financial risk prediction h3: textual indicators has significant positive impact on financial risk prediction h4: both financial and textual indicators has significant positive correlation and have significant positive impact on financial risk assessment or organizations. the presented study used a quantitative research methodology to look at the elements that financial risk experts find most useful in predicting financial risk. the cross-sectional approach was used to examine and comprehend interrelationships. the choice of quantitative research depends on its capacity for efficient and objective data collecting and processing. the positivist viewpoint places a strong emphasis on employing unbiased, verifiable data to support the progression of the process. according to lombardo et al. (2019), more generalizable results are associated pa ge 18 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 with bigger sample sizes. quantitative research thrives when using standardized and methodical data gathering approaches, as creswell and hirose (2019) explained. methodology research design the current study leveraged a deep learning approach implemented using python through jupyter notebook for the analysis of financial and textual data (tatsat et al. 2020). the data collection involved the extraction of financial indicators and textual information from diverse sources, including financial reports, news articles, and publicly available data (pejić et al. 2019). the existing research has then helped in formulating a structured openended survey on the integration of different financial and textual indicators in financial risk passement processed as highlighted by azizi et al. (2021). furthermore, the survey responses were gathered qualified financial risk experts of china. the official qualified financial reliable sources to collect accurate and effective insights (wu et al. 2020). furthermore, data preprocessing was a crucial step, encompassing the cleansing and standardization of financial data and natural language processing (nlp) techniques applied to textual data (aldunate et al. 2022). based on this the nlp facilitated the extraction of meaningful textual indicators, ensuring the integration of unstructured textual information with structured financial data. analysis and modeling to examine the association between study sections and the mean scores given to particular factors, this study used linear regression analysis. three crucial columns made up the dataset: “section,” “variable,” and “mean.” ‘section’ stood for several sections, ‘variable’ stood for research variables, and ‘mean’ included the mean scores for each variable inside each section. preparation of data the dataset was put into a panda dataframe called “df_means,” and the “section” variable underwent label encoding to convert its values into numbers appropriate for regression analysis. model for linear regression for the linear regression analysis, scikit-learn’s ‘linearregression’ class was used. section_encoded served as the independent variable, reflecting encoded section values, while ‘mean’ served as the dependent variable, including mean scores related to each variable (galioulline, et al. 2023). model fitting using the ‘fit’ procedure, the linear regression model was adjusted to the data. the goal of this fitting procedure was to find the regression line that suited the data the best and minimized the gap between anticipated values and actual mean scores. results and discussions results of regression the following important factors were shown to assess model performance: intercept depicting the y-intercept of the regression line. coefficient (slope) identifies the slope of the regression line, indicating how the mean scores vary when the units in the encoded section change. r-squared a measure of the coefficient of determination that expresses the amount of variance in mean scores that the model is able to account for. data visualization the findings were shown as a scatter plot, with blue data points representing the actual mean scores, supported by mcdermaid et al. (2019). the regression line was shown by a red line to show how well it suited the data. in order to accomplish the goals of the study, this linear regression analysis provided insights into the link between study sections and mean scores for particular variables (grotzinger et al. 2019). analysis and conclusions the results of the deep learning models were analyzed in the study to acquire understanding of how the combination of textual and financial variables improves financial risk prediction. to improve understanding and encourage practical decision-making, qualitative analysis and visualization methods were used (martins, et al. 2022). this method is an innovative approach for predicting financial risk since, as kang et al. highlights that it combines deep learning and nlp to glean insightful information from both organized and unstructured data sources. the results of this ground-breaking study will be presented and discussed in the following parts, with an emphasis on their consequences and potential applications in the field of financial risk assessment (babich et al. 2018). participants information the frequency analysis demonstrated in fig. 1 shows that majority of truth financial risk experts participated in the study are familiar with financial risk prediction through deep learning approaches, whereas an equal amount of participants has opted for less familiarity to slightly familiar in the study. the fig 2 illustrates that majority of the participants had 2-4 years of experience whereas considerable number of participants have 4-5 years of experience, but also a good amount of participants were observed to does not have much familiarity with financial risk prediction integrating the textual and financial indicators. pa ge 19 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 section 02: financial risk predictors the mean values derived from financial risk experts’ responses, collected on a 5-point likert scale, provide insights into their views on identified financial risk prediction factors: figure 1: participants familiarity with financial risk prediction figure 2: experience of participants figure 3: financial risk predictors preferences of liquidity the observed mean on the preferences of liquidity ratios over leverage ratios as a financial risk predictor is 1.996. it reflects that experts generally agree that liquidity plays a crucial role in financial risk prediction as compared to leverage ratios. financial indicators and conflict signals the observed mean for the creation of conflicting signals such as high roa but high debt was 2.029. this illustrates that experts tend to agree that managing financial risk effectively involves dealing with conflicting signals from financial indicators. multicollinear issues the observed mean for on the multicollinear issues in the financial risk prediction can be resolved by stepwise regresses ion and leads model instability as compared to lasso regression mean is 1.933. this shows that there is an agreement that multicollinearity among financial indicators can lead to model instability. missing data the observed mean on the employment on amputation techniques to resolve missing values is: 1.750. it reflects that experts agree that missing data problems can be resolved using amputation techniques. standardizing indicators the mean of the collected responses on the preference of ifrs while dealing with financial indicators in international contexts as compared to country-specific factors for standardizing indicators for cross-border risk assessment was observed in the analysis is 1.667. this illustrates that the lowest mean value indicates strong agreement that international accounting standards are more efficient for standardizing indicators in cross-border risk assessment. section 03: integrating financial indicators in the financial risk prediction figure 4: financial indicators and their implication pa ge 20 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 critical for specific industries the observed mean on the preference of financial indicators over textual indicators is 1.65. experts generally agree that certain financial indicators hold industry-specific significance in financial risk assessment. this underscores the recognition of tailored risk evaluation approaches. historical performance the observed mean on the influence of historical performance in te financial risk prediction process is 1.48). the mean indicates a strong agreement that historical financial indicator performance influences financial risk predictions. this reflects the experts’ belief in the predictive power of past financial data. financial health and risk indicators the observed mean on the preference of financial health indicates for the risk assessment over market and investors perception indicators is 1.45. this reflects that financial risk experts strongly agree that financial health and risk indicators outweigh market and investor perception indicators in financial risk predictions. this highlights the priority placed on fundamental financial metrics. short-term financial indicators the observed mean on the preference of short-term financial indicators over long term indicators is 1.43. the mean suggests a consensus that short-term financial indicators, like liquidity ratios, hold higher importance when assessing financial stability compared to long-term indicators. historical sector the observed mean on the preference of historical sector wide is preferred over firm specific data in highrisk scenario is 1.67. it illustrated that there is strong agreement that, in cases of high-risk indications, historical sector-wide data is preferred over firm-specific data. this emphasizes the importance of broader industry context in risk assessment. in the context of the study, these means signify a shared belief among experts regarding the significance of industry-specific considerations, historical data, and fundamental financial health metrics in the financial risk prediction process. it underscores the value of these factors in developing comprehensive risk assessment models. section 4: implication of textual indicators in financial risk prediction the mean values for textual indicators and their implication variables, gathered on a 5-point likert scale, provide valuable insights: textual indicators reliability the observed mean of the reliability of textual indicators in the financial risk prediction process is 1.95. this reflects that experts tend to agree that textual indicators, such as sentiment analysis, are accurate and reliable for financial risk prediction. this suggests their confidence in the utility of textual data in risk assessment. management tone and tone indexes the observed mean on the importance of. textual indicators, such as management tone and tone indexes in financial risk prediction is 1.6. the mean indicates agreement that management tone and tone indexes play a significant role in financial risk prediction, underlining the relevance of management communication in risk assessment. regulatory changes or news events the observe mean on the employment of manual review of textual data more efficiently in the financial risk assessment is 1.59. this illustrates that experts generally agree that regulatory changes and news events influence the use of textual indicators in financial risk prediction. this highlights the timeliness of textual data. potential biases the observe mean on interaction of with specific financial indicators (e.g., roa, roe) in shaping risk assessment outcomes is 1.95. this reflects that there is a consensus that potential biases in textual data, like sentiment analysis inaccuracies or reporting biases, can be more efficiently resolved through manual review. this reflects a practical approach to mitigating biases. language sentiment the observed mean on the language sentiment analysis in the financial risk prediction is 2.05): the highest mean suggests that textual indicators, like management tone and language sentiment, interact significantly with specific financial indicators, impacting risk assessment outcomes. in the study context, these means underscore the experts’ acknowledgment of the reliability of textual indicators, the influence of management tone and external events, and the importance of addressing potential biases. they emphasize the intricate relationship between textual and financial data in enhancing financial risk prediction models. section 5: relationship between financial predictors and indicators (regression analysis) figure 5: regression between financial predictors and indicators pa ge 21 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 the linear regression analysis between financial prediction factors and financial indicators presented in section 2 & 3 by using their mean values yields the following insights: intercept (intercept) when the prediction factors reach zero, this represents the estimated mean value of financial indicators, which is (1.949). within this context, the figure is approximately 1.949. coefficient (slope, coefficient) when financial prediction factors vary by one unit, this indicates a significant impact on financial indicators (-0.489). financial indicators decrease by around 0.489 units for every one-unit boost in financial prediction factors. r-squared (r-squared) as indicated by (0.704), the specific proportion of variance in financial indicators that can be attributed to the predicting factors is quite substantial. around 70.4%, or approximately 0.704, of financial variability can be attributed to the factors examined in the analysis. the results of the regression analysis indicate a notable connection between financial prediction factors and financial indicators. increased financial prediction factors lead to a drop in financial indicators, pointing towards an inverse connection. a sizeable portion of the variation in financial indicators is accounted for by the financial prediction factors, as evidenced by the impressive r-squared value, signaling their criticalness. section 6: relationship between financial predictors and textualindicators (regression analysis) in the linear regression analysis between financial prediction factors and textual indicators presented in section 2 & 4 by computing their means has led to the following results: intercept about 1.949 is the estimated mean value when predictor factors are zero. coefficient (slope) with each one-unit increase, there is a corresponding decrease of approximately 0.086 units in textual indicators due to the influence of prediction factors. r-squared the predictive power of textual indicators can be attributed to approximately 3.4% of their variability. a weak bond exists between textual indicators and financial prediction factors, the analysis reveals. a negative coefficient suggests that slight decreases occur when textual indicators are influenced by increased prediction factors. a minor contribution to textual indicators is made by these factors, indicating limited effect on financial risk prediction. section 7: pearson correlation matrix figure 6: relationship between financial predictors and textualindicators figure 7: pearson correlation matrix a complete positive linear link between both variables is shown by the positive correlation coefficient of 1 in all four quartiles of the correlation matrix between textual indicators and financial indicators. this suggests a significant positive correlation between textual and financial characteristics in the context of predicting financial risk, such that when one set of indicators rises, the other set rises in lockstep. discussion using a deep learning approach, the study sought to examine the fusion of financial and textual indicators in financial risk forecasting. through analysis, we gained insight into how various factors relate to one another and their potential influence on risk assessment. constructing the foundation on west et al. (2022) work, analyzing the dataset consisting of “section,” “variable,” and “mean,” pa ge 22 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 allowed us to investigate the association between study sections and mean scores through linear regression. by leveraging scikit-learn’s ‘linearregression’ class, ‘section_ encoded’ represented the independent variable, while ‘mean’ played the role of the dependent variable. the study findings suggest that liquidity is expected to play a critical role in predicting financial risk, as highlighted by nguyen et al. (2022), that it outranks leverage ratios’ importance. additionally, the study also contended that it is vital to handle conflicting signals from financial in dictators when managing financial risk. supporting findings of the current study have also illustrated that there is consensus among experts that collinearity issues can compromise financial model accuracy which can also be seen in wang et al. (2020) work. the findings have also illustrated that to solve missing data challenges, consensus is required among specialists regarding the use of amputation strategies (kolossvary et al. 2019). lastly, the ifrs has been observed to travels across borders without receiving preferred risk assessment indicator standard treatment. highlighting...risk assessment’s key elements has been emphasized (subramanian et al. 2022). in risk assessment, specific financial indicators have industry-related importance. the performance of historical financial indicators heavily influences financial risk projections. financial stability assessments prioritize short-term liquidity ratios which are ahead of market and investor perception indicators. for high-risk situations, historical sector-wide data takes precedence over firm-specific data. historical data, industry-specific considerations, and financial health metrics are crucial for accurate risk assessment. experts concur that textual indicator, such as sentiment analysis, are useful for predicting financial risk. financial risk prediction relies heavily on textual indicators such as tone indexes and management tone. risk assessment outcomes are influenced by both financial indicators and textual indicators, with their interaction being essential. by shedding light on the reliability of textual indicators, these discoveries underscore the significance of management communication and the need to uncover biases within textual databases. financial prediction factors and financial indicators exhibit a significant relationship, as shown in a linear regression analysis. financial prediction factors impact the decrease in financial indicators. in order to make informed decisions regarding investments, a clear understanding of the market is crucial. when comparing textual indicators and financial prediction factors, a fragile connection emerges (tang et al. 2020). minor variation in textual indicators occurs alongside enhanced prediction factors, denoting restricted contribution to financial risk prediction (liang et al. 2020). the presence of correlation coefficients of 1 in every quartile of the matrices in the analysis implies a robust connection between textual and financial measures. bellay et al. (2021), stresses that this insight illuminates the synchronization of financial and textual indicators in predicting financial risk. risk assessment requires careful consideration of liquidity, conflict resolution, historical data, and fundamental financial metrics (waswa, et al. 2018). furthermore, the findings also stress the need to address biases, as well as the reliability of textual indicators. correlation between textual and financial markers illustrates their dependence in evaluating risk. expanding this the study by lin et al. (2018) highlights that by advancing deep learning techniques, future research can further unlock the potential of integrated approaches for more accurate financial risk prediction. by improving predictive abilities, this study creates a pathway towards more educated choices in financial risk analysis (grover et al. 2018). conclusion employing quantitative techniques, this research investigates the fusion of financial and textual indicators for predicting financial risk. tackling multicollinearity problems and handling liquidity are significant risk prediction findings. focusing on industry-specific metrics, experts prioritize short-term data when evaluating highrisk sectors, while historical trends hold less weight. according to textual indicators, sentiment analysis is just one of the reliable predictors, with the need to address biases included. while textual indicators exhibit a weaker bond, linear regression reveals a substantial relationship between financial prediction factors and indicators. enhanced methodologies are achieved through the correlation between their interdependence in risk prediction, resulting in improved decision-making. recommendations based on the findings of the current study that, financial risk assessment professionals emphasize the significance of liquidity indicators in risk evaluation, address the management of conflicting signals from financial data, adopt strategies for mitigating multicollinearity problems, consider the use of amputation techniques for missing data challenges, and investigate the application of international financial reporting standards (ifrs) for standardizing cross-border reporting. the should also employ past sector-wide data for high-risk scenarios while giving previous financial performance data and short-term financial indicators priority. continue to place your faith in textual indications like sentiment analysis and managerial tone, but aggressively address any biases through manual inspection and look into deep learning approaches to improve the integration of financial and textual data even more. these initiatives will support more thorough and accurate financial risk assessments, eventually enhancing risk analysis decision-making procedures. novelty the study uses advanced deep learning techniques to integrate financial and textual indicators for financial risk prediction. it uses recurrent neural networks and transformers to analyze the synergy between these pa ge 23 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 15-24, 2024 sources, improving the precision of financial risk models. the study uses a quantitative and qualitative approach, incorporating real-world insights from financial risk experts in china. the research identifies a research gap in the literature and contributes to the field of financial risk prediction by combining financial and textual indicators. contribution to knowledge the study enhances financial risk prediction by integrating financial and textual indicators and exploring deep learning techniques like recurrent neural networks and transformers. it provides insights into risk assessment dynamics and bridges the gap between quantitative and qualitative research. the study identifies a research gap in existing literature and offers practical recommendations for financial risk professionals, emphasizing liquidity indicators and considering ifrs for cross-border reporting. it lays the groundwork for future research in deep learning techniques and financial risk prediction. research gap the study identifies a gap in literature regarding the integration of financial and textual indicators for improved financial risk prediction using deep learning techniques. it emphasizes the need for a holistic approach, focusing on each type of data individually. the study also highlights the lack of deep learning applications in financial risk prediction and the potential benefits of advanced methods. it also calls for more in-depth investigation into the reliability of textual indicators and the integration of quantitative and qualitative research methodologies. references al-eitan, g. n., & bani-khalid, t. o. 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(2018). the future of risk assessment. reliability engineering & system safety, 177, 176-190. pa ge 1 pa ge 15 american journal of financial technology and innovation (ajfti) the role of fintech in promoting the takaful model of islamic insurance raed elomari1* volume 1 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: september 18, 2023 accepted: october 20, 2023 published: october 26, 2023 this study examines the influence of fintech integration on the takaful insurance business in qatar, emphasizing profitability and industry development. takaful, an islamic insurance policy based on ethical values, faces practical difficulties. fintech, driven by technical breakthroughs such as blockchain and ai, provides solutions to simplify processes and improve accessibility. to get quantitative data, 320 takaful and fintech experts were polled. respondents had a wide range of expertise levels, emphasizing the importance of industry collaboration. positive opinions of regulatory support (85.9%) and proactive fintech uptake (84.1%) were noted. regulatory barriers (82.5%), cybersecurity concerns (82.2%), opposition to change (82.8%), and a lack of fintech financing (82.5%) were among the challenges. the study discovered that fintech plays a substantial role in increasing profitability, with 91.3% reporting greater earnings and 84.4% reporting enhanced operational efficiency. personalized insurance solutions (84.4%) and data-driven growth (88.1%) generated profitability. fintech was largely viewed as supporting the takaful insurance sector (83.4%), increasing client involvement (81.9%) and confidence. correlation and regression analysis demonstrated the existence of favorable relationships between fintech integration, its problems, profitability, and industry promotion. these studies highlight fintech’s transformational potential and inform industry stakeholders and policymakers. addressing regulatory, cybersecurity, change resistance, and finance issues is critical to effectively integrating fintech into qatar’s takaful insurance. keywords fintech, shariah, takaful insurance, qatar, profitability, insurance introduction islamic finance has gained prominence recently, with a growing global interest in sharia-compliant financial products and services (kadi, 2023). among these, takaful, or islamic insurance, stands out as a key component of the islamic finance ecosystem. alhammadi (2023) explains that takaful embodies the principles of cooperation, risk-sharing, and adherence to islamic ethical guidelines. however, despite its potential to provide financial security to muslim communities worldwide, takaful has faced challenges related to operational efficiency and accessibility (alhammadi, 2023). bhasin & rajesh (2018) stresses that takaful is grounded in islamic principles, emphasizing the absence of riba (interest), gharar (excessive uncertainty), and maisir (gambling) in its operations. traditional takaful models have struggled to achieve profitability and operational efficiency due to adverse selection, moral hazard, and high operational costs (bhasin & rajesh, 2018; hassan et al., 2022). however, recent developments in financial technology (fintech) have begun to revolutionize the takaful industry of qatar. additionally, a recent report by ernst & young (2022), “world takaful report,” indicates that technologydriven innovations, such as blockchain, ai, and digital distribution channels, have helped takaful operators streamline operations, reduce costs and enhance customer experiences (young., 2022). moreover, fintech has enabled the creation of micro-takaful products, making insurance more accessible to low-income populations in emerging markets. this aligns with the findings of the islamic development bank’s “islamic finance for sustainable development report”(icd, 2022), emphasizing the role of technology in achieving financial inclusion. furthermore, a report by the international monetary fund released in (2022), emphasizes the importance of regulatory frameworks that foster fintech innovation while ensuring compliance with islamic finance principles (ashfaq & zada, 2021; bank, 2020; imf, 2023). furthermore, the study investigated the technological advancements and delved into regulatory aspects critical for the successful integration of fintech in takaful operations. in essence, it examines the complex nature and associated factors of takaful insurance, the emerging trend of fintech, and its role in promoting takaful insurance. the current study determines how fintech innovations reshape the takaful landscape, expand its reach, and enhance its operational efficiency. literature review the takaful models, despite their adherence to ethical guidelines, face intricate issues that hinder their efficiency and financial viability highlighted by alhammadi, (2023). the study also contends that operational hurdles in takaful insurance include adverse selection, where policyholders with higher risks disproportionately seek coverage, leading to imbalanced risk pools (alhammadi, 2023). additionally, gherbi (2021) has noted that moral hazard poses a problem as policyholders might engage in riskier behavior once protected by takaful coverage, 1 metlife gulf, dubai, united arab emirates * corresponding author’s e-mail: dr.raed_elomari@outlook.com pa ge 16 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 further straining the industry’s financial health. moreover, high operational costs are a common challenge, primarily due to the complexities associated with maintaining sharia compliance, elaborate administrative processes, and the need for economies of scale (gherbi, 2021; sim & hassan, 2019). these operational difficulties, as outlined by lee et al. (2019), significantly impact the takaful insurance industry’s ability to operate efficiently and maintain profitability (lee et al., 2019). on the profitability front, takaful operators often contend with underwriting deficits, where the premiums collected do not cover the claims and operational expenses (malik et al., 2019). malik et al. (2018) state that this challenge is compounded by investment constraints inherent in sharia-compliant investments, limiting the types of assets in which takaful operators can invest, potentially yielding lower returns compared to their conventional counterparts (malik et al., 2018). furthermore, abu al-haija & houcine (2023) highlight that the need for effective risk diversification, partly due to limited investment options and specific product and geographical focus, adds to the industry’s profitability struggles. these factors, as documented in the world takaful report by ernst & young (2020), highlight the multifaceted nature of profitability challenges faced by the takaful insurance industry (abu al-haija & houcine, 2023; young., 2022). expanding this, kad (2023) suggests that addressing these issues is vital for ensuring the sustainable growth and success of takaful insurance, necessitating innovative solutions and regulatory support to bolster financial stability and improve profitability in the industry (kadi, 2023). fintech and takaful insurance industry: a case of qatar the integration of financial technology, or fintech, has been instrumental in catalyzing the growth of qatar’s takaful insurance industry (glavina et al., 2021). bertillo & bertillo (2022), highlights that takaful insurance, built upon islamic principles that strictly prohibit riba (interest), gharar (excessive uncertainty), and maisir (gambling), takaful insurance has grappled with operational and profitability challenges. however, resolving the issues concerned with operations and profitability, wang et al. (2021) noted that fintech has emerged as a transformative force, significantly enhancing operational efficiency and profitability. the study has explored that digital distribution channels have played a pivotal role in redefining takaful operations (bertillo & bertillo, 2022; wang et al., 2021). elsarag (2019) highlights that fintech platforms have facilitated takaful operators’ reach to a broader audience in qatar. notably, digital sales and service channels have grown exponentially, bolstering customer engagement and accessibility. according to a recent report by deloitte (2021), the adoption of digital platforms for policy sales and services has seen a staggering 40% annual growth rate ((deloitte, 2021; elasrag, 2019). furthermore, in the pursuit of transparency and trust, the takaful industry has harnessed blockchain technology (mohamed, 2021). botosh (2020) highlights the secure and tamper-proof record-keeping features of blockchain that have dramatically reduced fraud and disputes, aligning with islamic ethical principles. in fact, a study by mohamed & ali (2020) highlighted that 82% of takaful executives believed that blockchain had significantly improved their operational efficiency and transparency. the study has also illustrated that fintech innovations have led to considerable cost reductions for takaful operators (botosh, 2020; mohamed, 2021). the automation of underwriting, claims processing, and risk assessment has driven cost savings, subsequently enhancing profitability. ernst & young’s takaful industry report (2021) revealed that takaful operators witnessed a 15% reduction in operational costs after adopting fintech solutions (young., 2022). data analytics has emerged as a potent tool for profitability enhancement, through which fintech takaful operators in qatar have been able to make more precise risk assessments and pricing decisions, leading to superior underwriting outcomes and reduced losses (alshater et al., 2022). kpmg’s analysis (2020) indicated that the integration of data analytics increased takaful profitability by up to 18% in some cases. additionally, fintech has paved the way for the development of microtakaful products, broadening the customer base and promoting financial inclusion in qatar. notably, these products have rendered takaful services accessible to lowincome populations. the islamic development bank’s islamic finance for sustainable development report (2020) highlighted a 25% increase in takaful premium revenue attributed to the introduction of micro-takaful products . in essence, fintech has ushered in a profound transformation within qatar’s takaful insurance sector (bank, 2020; kpmg., 2020.). by harnessing digital distribution channels, blockchain technology, costeffective processes, and data analytics, the industry has experienced remarkable improvements in operational efficiency and profitability (perdana & wang, 2023). while existing studies often explore the impact of fintech on traditional insurance and financial services, there appears to be a gap in research regarding how fintech integration specifically affects the profitability of takaful insurance and its unique factors. this gap highlights the need to investigate how fintech adoption influences the financial performance and key components of takaful insurance from the perspective of industry professionals, offering insights into its challenges and opportunities in the context of islamic insurance. theoretical framework the study is based on a theoretical framework that takes into account the use of financial technology (fintech), adherence to sharia-compliant standards, competitive dynamics, and client trust as major factors determining the profitability of the takaful insurance sector. the pa ge 17 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 current exploration tests the hypothesis that more fintech integration, supported by strict adherence to sharia norms, lessens competitive risks from traditional insurers by drawing on the technology acceptance model and institutional theory. furthermore, it claims that increasing client confidence in sharia-compliant financial solutions may boost profitability. this paradigm directs research into how these factors interact, enabling a more thorough understanding of how fintech affects takaful insurance profitability in qatar. laying the groundwork for this, the study has offered sufficient proof to support the validity of the following assertion: the profitability of the takaful insurance sector in qatar is considerably impacted by the incorporation of fintech solutions, it is hypothesized. methodology research design the current study employs a quantitative approach to examine the influence of fintech on the promotion of the takaful insurance market in qatar. the results are analyzed using a logical method and a positivist philosophical perspective. the quantitative technique was chosen because of its potential to offer objective conclusions and collect vast amounts of data from a big population (mohajan, 2020). the deductive technique is useful for testing ideas and establishing precise conclusions (casula et al., 2021). data collection primary sources were used to collect data for this study, which included the distribution of close-ended survey questionnaires to the target group from december 2022 to march 2023. the questionnaire was created using a 5-point likert scale, with responses ranging from “strongly agree” to “strongly disagree.” the major goal was to examine the influence of fintech on the promotion of qatar’s takaful insurance market. the survey approach is low-cost and enables the collection of a diverse range of opinions from a large population, which reduces bias in the results (nayak & narayan, 2019). sampling purposive sampling, a non-probability sampling approach, was used to choose field specialists. the sample comprises 320 qatar’s takaful insurance and fintech technology workers. purposive sampling guarantees that participants have the necessary knowledge and competence about the study topic (thomas, 2022). spss software, which is wellsuited for statistical analysis, was used to analyze the data. the analysis included descriptive statistics, correlation analysis, and regression analysis to study the connections between variables and test hypotheses. data analysis to analyze the influence of fintech on the promotion of the takaful insurance sector in qatar, the current study used a quantitative methodology, a deductive technique, and a positivist philosophical paradigm. data were acquired by survey questionnaires from 320 takaful insurance and fintech technology specialists, and analysis was done using spss software. ethical considerations such as confidentiality and informed consent were strictly followed during the study procedure. results and discussions experience and age of respondents figure 1. illustrates the experience and role of the respondents who participated in the study. the survey shows that the majority (58.4%) of participants are associated with the takaful insurance sector, while 41.6% are linked to fintech. this distribution provides a clear picture of the roles within the surveyed population, demonstrating a significant presence from takaful insurance and fintech professionals. regarding experience, a substantial proportion of respondents (92.8%) possess a decade or less of experience, with 38.8% having less than a year. this data indicates that the survey encompasses a diverse range of experience levels, which is valuable for understanding how professionals at various stages of their careers perceive the impact of fintech on takaful insurance. figure 1: experience and role of respondents pa ge 18 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 integration of fintech in the takaful insurance industry of qatar the frequency analysis of the survey responses on the integration of fintech in the takaful insurance industry of qatar, presented in figure 2, shows qatarn experts’ perspectives on fintech integration in the takaful insurance business. a sizable number (85.9%) feel the regulatory climate is favorable for fintech integration, showing significant regulatory support. furthermore, 84.1% see financial institutions and takaful insurance businesses as being proactive in their use of fintech technologies. this indicates an openness to technological improvements. furthermore, 84.7% recognize a significant relationship between fintech companies and the takaful business, figure 2: integration of fintech in the takaful insurance industry of qatar showing collaborative efforts for innovation. finally, in the frequency study, 44.1% of respondents in qatar believe that qualified fintech experts help integration into the takaful insurance market, while 23.4% are indifferent and 32.5% disapprove or strongly disagree. these data provide a positive opinion of fintech integration in the qatar’s takaful insurance business. challenges for integrating fintech in qatar the frequency analysis in figure 3 reveals substantial hurdles associated with the integration of fintech inside qatar’s takaful insurance business. to begin, 82.5% of respondents think that regulatory obstacles and compliance requirements offer significant problems. this highlights the importance of simplifying rules to allow pa ge 19 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 smoother integration. second, cybersecurity and data privacy concerns appear as key impediments, with 82.2% recognizing their relevance. addressing these concerns is critical to gaining confidence in fintech solutions. third, as 82.8% of participants agree, opposition to change within established organizational structures impedes successful integration. successful fintech adoption requires overcoming organizational inertia. finally, 82.5% see inadequate access to capital and investment for fintech businesses as a barrier, emphasizing the significance of increasing financial support for innovation in the takaful insurance market. increase in profitability of takaful insurance industry through fintech the frequency analysis in figure 4 demonstrates the immense impact of fintech integration on the profitability of qatar’s takaful insurance sector. a sizable 91.3% of respondents believe that fintech adoption has resulted in a significant rise in profitability. this reinforces the widespread belief that fintech technology contributes greatly to financial advantages in the sector. furthermore, 84.4% recognize fintech’s significance in improving operational efficiency, lowering expenses, and increasing profits. furthermore, with 84.4% agreement, fintech’s capacity to facilitate personalized insurance solutions and boost client retention underscores its significance in increasing profitability. the fact that 88.1% of companies recognize data-driven growth potential demonstrates the relevance of analytics in optimizing income sources. these data demonstrate fintech’s transformational impact on takaful insurance profitability in qatar. figure 3: challenges for the integration of fintech in the takaful insurance industry of qatar figure 4: enhanced performance and profitability through fintech integration in the takaful insurance sector of qatar pa ge 20 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 promoting the takaful insurance industry of qatar through fintech the analysis is in figure 5. reveals that fintech is widely perceived as a significant contributor to the promotion of the takaful insurance industry in qatar. a substantial 83.4% of respondents believe that fintech has played a crucial role in advancing the industry. additionally, 81.9% agree that fintech adoption enhances customer engagement and trust, vital elements for industry growth. the findings collectively demonstrate a strong consensus among professionals regarding the positive impact of fintech on both industry promotion and customer relations, underscoring fintech’s pivotal role in the takaful sector’s development. figure 5: promoting growth of the takaful insurance industry in qatar through fintech innovations correlation analysis the correlation analysis between fintech integration, its challenges, increasing profitability, and promoting the growth of the qatar’s takaful insurance industry reveals significant positive correlations between factors related to fintech integration and their influence on the qatar’s takaful insurance market. fip (fintech and increase profitability) correlates significantly with ift (impact of integrating fintech) at 0.579, ci (challenges for integrating fintech) at 0.541, and pr (promoting takaful insurance industry using fintech) at 0.519. this suggests that fintech integration leads to increased profitability, integration issues, and takaful industry promotion. furthermore, ift, ci, and pr all demonstrate significant positive associations with one another, indicating that these variables are linked. in essence, fintech’s position in the takaful insurance industry involves profitability, issues, and promotional features, among other things. table 1: correlation analysis correlations fip ift ci pr fip pearson correlation 1 .579** .541** .519** sig. (2-tailed) .000 .000 .000 n 320 320 320 320 ift pearson correlation .579** 1 .707** .577** sig. (2-tailed) .000 .000 .000 n 320 320 320 320 ci pearson correlation .541** .707** 1 .501** sig. (2-tailed) .000 .000 .000 n 320 320 320 320 pa ge 21 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 pr pearson correlation .519** .577** .501** 1 sig. (2-tailed) .000 .000 .000 n 320 320 320 320 ** correlation is significant at the 0.01 level (2-tailed) regression analysis the regression analysis emphasizes the importance of the independent variables, such as promoting the takaful insurance industry through fintech (pr), challenges for integrating fintech technology (ci), and the impact of integrating fintech technology (ift), in explaining variation in fintech’s role in increasing profitability in the takaful insurance industry (fip) in qatar. the r-squared value of the model is 0.409, indicating that these factors explain 40.9% of the variation in fip. furthermore, the anova findings show a high level of statistical significance, with an f-statistic of 72.954 (p 0.001), indicating that at least one of the independent factors has a substantial effect on fip. to summarise, pr, ci, and ift all play a major and statistically significant role in evaluating the influence of fintech on takaful sector profitability. table 2: regression analysis model summary model r r square adjusted r square std. error of the estimate 1 .640a .409 .404 1.50753 a. predictors: (constant), pr, ci, ift anovaa model sum of squares df mean square f sig. 1 regression 497.397 3 165.799 72.954 .000b residual 718.153 316 2.273 total 1215.550 319 a. dependent variable: fip b. predictors: (constant), pr, ci, ift discussion the study’s findings provide significant insights into the perspectives of experts in qatar’s takaful insurance and fintech sectors, illuminating the integration of fintech in the takaful insurance market and its consequent influence on profitability and industry promotion. these findings are crucial because they thoroughly grasp the dynamics in this expanding sector. one of the interesting discoveries is the respondents’ experience and involvement in the research. the study includes experts from both the takaful insurance and fintech industries, with the majority (58.4%) involved with takaful insurance and the remainder 41.6% associated with fintech. this broad distribution reflects the industry’s collaborative approach. it was also discovered that 92.8% of responders had a decade or less of experience, with 38.8% having less than a year. this range of experience is critical for understanding how professionals at different career phases evaluate fintech’s influence on takaful insurance (barberis et al., 2019). in terms of the integration of fintech in the takaful insurance industry of qatar, the findings are generally positive. a substantial percentage of respondents (85.9%) believe that the regulatory climate in qatar is conducive to fintech integration. this perception reflects the significant regulatory support for fintech initiatives in the country. moreover, 84.1% of respondents perceive financial institutions and takaful insurance companies as proactive in their adoption of fintech technologies. the study by alam et al. (2019) is also evidence of a proactive stance, indicating a readiness to embrace technological advancements within the industry. additionally, 84.7% of respondents acknowledge a strong relationship between fintech companies and the takaful business, highlighting collaborative efforts for innovation. the positive perception of such collaboration underscores the industry’s recognition of fintech’s potential (alam, 2019; anifa et al., 2022). however, the study also revealed certain challenges associated with fintech integration in qatar’s takaful insurance sector. notably, 82.5% of respondents consider regulatory hurdles and compliance requirements to be significant obstacles. understanding this from the lens of alshater et al. (2022) underscores the importance of streamlining regulations to facilitate smoother integration (alshater et al., 2022). additionally, 82.2% of respondents recognize cybersecurity and data privacy concerns as key impediments. addressing these concerns is essential for building trust in fintech solutions. resistance to change within established organizational structures is another challenge, as 82.8% of participants agree. successful fintech adoption requires overcoming this organizational inertia. lastly, 82.5% of respondents see limited access to capital and investment for fintech startups as a barrier. chishti, s., & barberis (2016) stressed the need to enhance financial support for innovation in the takaful insurance market (chishti & barberis, 2016). pa ge 22 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 15-23, 2023 one of the most significant findings of the study relates to the increase in profitability of the takaful insurance industry through fintech integration. an overwhelming majority (91.3%) of respondents believe that fintech adoption has led to a substantial increase in profitability. this finding strongly supports the idea that fintech technologies contribute significantly to financial advantages in the sector. additionally, 84.4% of respondents recognize fintech’s role in improving operational efficiency, reducing costs, and ultimately increasing profits. this aligns with the industry’s recognition of the operational benefits of fintech adoption (zalan & toufaily, 2017). furthermore, 84.4% of respondents acknowledge fintech’s capacity to facilitate personalized insurance solutions, contributing to increased customer retention and profitability. the emphasis on personalized offerings underscores the customer-centric advantages of fintech. lastly, 88.1% of respondents agree that fintech-driven analytics and data insights are critical in discovering development prospects and optimizing income streams. as shah et al. (2020) point out, integrating financial data and technology (fintech) sectors is a possible reason for sales growth and, eventually, greater profit (shah et al., 2022). another critical part of the research is the use of fintech to promote the takaful insurance market in qatar. according to the data, fintech is commonly regarded as a key contributor to the industry’s promotion. a significant population of 83.4% of respondents feel that fintech has played an important role in the advancement of the sector. furthermore, 81.9% believe that fintech adoption improves consumer engagement and trust, both of which are critical for industry success. the strong consensus among professionals presented in the current study highlights fintech’s pivotal role in the development of the takaful sector. the correlation and regression analyses conducted in the study provide further insights into the relationships between various factors. significant positive correlations were found between fintech integration, its challenges, increasing profitability, and promoting the growth of the qatar’s takaful insurance industry. pearson correlations revealed high positive associations: fintech integration (ift) was associated strongly with enhanced profitability (fip) at 0.579, integration challenges (ci) at 0.541, and industry promotion (pr) at 0.519. regression analysis revealed the relevance of independent factors such as the profitability, challenges and advantages of integrating fintech in the takaful insurance industry of qatar in explaining 40.9% of fip variance. contrasting this with the study by alshater et al. (2020), it emerges that these correlations emphasize the interconnectedness of these factors and underscore the need for a holistic approach to fintech adoption (alshater et al., 2022). in essence, the study’s findings reveal that the integration of fintech in the takaful insurance industry of qatar is generally perceived positively, with significant support from regulatory authorities and industry players (rabbani et al., 2022) (rabbani et al., 2022). however, barberis et al. (2019) have also contended challenges related to regulation, cybersecurity, resistance to change, and access to funding must be addressed, which is also evident by the current study’s findings. importantly, fintech has had a transformative impact on profitability and the promotion of the takaful insurance industry (barberis et al., 2019). the study findings provide valuable insights for policymakers, industry professionals, and researchers seeking to understand the evolving landscape of takaful insurance and fintech integration in qatar. conclusion in conclusion, the quantitative study, which looked at how fintech integration has affected qatar’s takaful insurance market, showed strong connections between many important variables. the study investigated the influence of fintech integration on qatar’s takaful insurance business. the findings indicated a broad collection of experts in both areas with various degrees of expertise. while there was governmental backing and industry readiness for fintech adoption, there were problems such as regulatory impediments, cybersecurity concerns, and reluctance to change. most notably, fintech was closely linked to increasing profitability and takaful insurance business development. these findings highlight the revolutionary potential of fintech in qatar’s takaful sector and provide useful insights for policymakers and industry players navigating this changing landscape. recommendation the presented study’s findings illustrate several recommendations with implications for takaful insurance industry stakeholders and professionals. streamlining regulatory processes is critical to encouraging fintech integration since it may lower obstacles and boost innovation. second, it is critical to improve cybersecurity measures and create thorough data privacy regulations in order to build trust in fintech solutions. third, proactive change management measures should be implemented to overcome organizational opposition and guarantee the seamless implementation of fintech. furthermore, increasing access to capital and investment options for fintech businesses is critical to fostering innovation in the takaful insurance market. finally, recognizing the importance of data analytics and successfully utilizing it helps optimize revenue streams and improve decisionmaking processes in the industry. references abu al-haija, e., & houcine, a. 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(2022). world takaful report 2022. retrieved from https://assets.ey.com/content/dam/ey-sites/ ey-com/en_gl/topics/insurance/ey-2022-globalinsurance-outlook-report.pdf zalan, t., & toufaily, e. (2017). the promise of fintech in emerging markets: not as disruptive. contemporary economics, 11(4), 415-430. pa ge 1 pa ge 23 american journal of financial technology and innovation (ajfti) popularity of mobile transaction services in the banking sector aisha badawi abdelrhman1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3807 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: september 25, 2024 accepted: october 22, 2024 published: february 12, 2025 the study endeavors to analyze the factors driving the adoption and application of internet-based bank services in the sudan with a special emphasis on e-banking. utilizing tam’s structure, this research explains the usage of customers on the internet banking world by focusing on these features: utility, user-friendliness, trust management, and cultural considerations. it was revealed that, although security and reliability remain one of the most significant concerns in banking, end users are also looking for a bank with good design, enjoyable features and which is socially ideal. the study highlights the increasing popularity of mobile transaction services in emerging markets, highlighting the need for better understanding of user behavior and optimizing their usefulness and establishment in sudan. the case study of this research consists of five major sudanese commercial banks in khartoum chosen because they are the pioneers in developing and starting the application of eb services in sudan namely sudanese french bank, albaraka bank, omdurman national bank, faisal islamic bank and tadamon islamic bank. the time limit covered is the 2019-2023. data for this study was collected by means of a survey conducted in khartoum. a total of 300 questionnaire forms were delivered to respondents of which 233 were returned giving a response rate of 71 percent. questionnaires were filled in five different banks and in different branches by selected customers with different banking treatments. this results in a sample that was well distributed in terms of demographic information e.g. age, education, income, and treatment period. the questionnaire consisted of questions that related to background, possible factors affecting acceptance of ib and use of ib services. likert five point scales ranging from “strongly agree” to “strongly disagree” were used as a basis of questions. the scale has been used in previous tam related researches e.g. igbaria et al; 1995; teo et al; 1999, tero and kari; 2005. additionally, the “neutral” option was allowed in almost all questions. the questionnaire was developed and tested with a focus group consisting of professionals from sudan university and the banking sector. the focus group finally verified that the hypotheses might be an affective factor explaining ib acceptance. based on this information the questionnaire was modified and finalized. keywords cultural considerations, electronic payment systems, internet banking, mobile technology, technology acceptance model (tam), sudan, user-friendliness, trust management introduction the factors influencing internet banking acceptance in sudan using the technology acceptance model (tam), a widely used model for understanding and predicting technology adoption. it focuses on perceived usefulness and ease of use as key determinants of an individual’s attitude and intention to use a particular technology (prastiawan et al., 2021). in the context of internet banking in sudan, pu refers to the degree to which customers believe that using internet banking will enhance their banking experience, such as convenience, efficiency, and accessibility (chauhan et al., 2022). in contrast, peou measures how simple clients find it to use and comprehend online banking (kavitha & gopinath, 2020). several factors may influence the perceived usefulness and ease of use of internet banking in sudan (omer & adam, 2020). the emergence of smartphones and mobile internet services has contributed significantly to the dramatic growth in internet penetration in sudan during the last several years (ibrahim et al., 2021). internet banking has probably become more accessible to a greater portion of the public due to the increasing access to technology. perceived security of online transactions is another aspect that can impact the adoption of internet banking in sudan (khattab et al., 2020). if consumers have faith that their financial and personal details will be safe while banking online, they are more inclined to use the service. therefore, sudanese banks must establish strong security protocols to reassure clients that their online banking is secure (masad et al., 2023). cultural factors may also play a role in shaping customers’ attitudes toward internet banking in sudan (elmassah & abou-el-sood, 2022). for instance, various demographic groups may have differing perspectives about the security of online transactions and the reliability of technology (hossain et al., 2020). banks need to understand these cultural nuances and tailor their internet banking services to meet the specific needs and expectations of sudanese customers (alhanatleh, 2021). it is widely believed that the rise of online banking and other financial services is a hallmark of the current economic renaissance and a crucial outcome of modern scientific and technology progress (abad-segura et al., 2020). the rapid global adoption of information technology by businesses due to the technological revolution and rapid spread of the internet has significantly impacted various industries and 1 northern boarder university, college of business administration, banking and finance, saudi arabia * corresponding author’s e-mail: aisha.badawi@nbu.edu.sa pa ge 24 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 businesses (rymarczyk, 2021). in order to stay ahead of the competition and create new, useful systems and apps to improve banking services, the banking industry has been using electronic technology (subanidja et al., 2022). contemporary technology has become one of the biggest strategic issues in the field of banking, and this reality imposes great challenges on banks (heffernan, 2005). new patterns of transactions and activities have emerged across numerous industries as a consequence of the exponential rise of information technology and the amount of data. as a result, the world is going through deep and fast alterations (nordhaus, 2021). the banking industry has significantly shifted towards online and mobile banking due to increasing client expectations, fierce competition, cost reduction, improved efficiency, and increased service offerings (wewege et al., 2020). the development of electronic banking services in sudan, their types, legislation, challenges, and future visions for electronic payment services (leila & fatiha, 2022). the development of electronic payment services in sudan, analyzing their components, advantages, obstacles, and risks, and proposing potential expansion solutions (mokar et al., 2021). the banking industry has made significant progress in enabling electronic banking services, which are expected to rapidly spread in sudan (mustafa, 2021). study is needed to understand the factors affecting the spread of these services, which are primarily obtained through traditional branches. the purpose of this research is to study the key elements determining the uptake and usage of internet banking services in sudan among electronic payment systems. specifically, the study aims to: • recognize the stimulants that have a chord in the customers’ aspiration to embrace internet banking in sudan. • explore to what extent the attitudes of the customers towards online banking are shaped by perceived usefulness, usefulness, trust, and culture • al factors. • analyze those variables and see if the people are consistently using the services internet banking is providing. literature review adoption factors and tam in developing countries the technology acceptance model (tam) is a widely applied theory which enables to understand how and why individuals willing to embrace a new technology and start using it (davis, 1989). it has been widely useful in different circumstances, such as silver delivery system adoption on the internet (marakarkandy et al., 2017). on the other hand, the applying of the set communicational model to the developing countries, such as the sudan one, could need perceptively to take into consideration all the nuances that is given their special socio-economic conditions and technology landscape. internet banking development in less developed countries was the theme of several studies that addressed the reliability of the tam features of relative usefulness and operability (ly & ly, 2022). sudan could be a powerful example of how internet banking can bring financial inclusion and satisfy the banking needs of the population in places where traditional banking facilities are very limited (omer & adam, 2020). field studies carried out in different socio-economic settings have shown that the perception of usefulness plays an important role in the use of e-banking (ahmad et al., 2020). customers from developing countries perceive online banking as a good choice for them; it provides a convenient and given model for conducting financial activities or using financial services if the physical bank branches are not accessible to them (al-harbi, 2020). in addition to being convenient, because of 24/7 availability, timesaving and the ability to perform banking operations anywhere, these services are attributed to the use of online banking (kordit, 2022). the other factor which is also significant is the comfort level in usage that depends on the perceived difficulty for these users. studies have pointed out that relatability comes first in the features category thus with a user-friendly interface and simple navigation, customers get value (chaimaa et al., 2021). internet banking is likely to be utilized by individuals who are motivated, familiar with the process, and have low digital literacy levels (sudirjo et al., 2024). on the other hand, even though it is worthwhile to say that the tam framework undoubtedly might require the adaptation to the huge cultural and contextual factors that have emerged in sudan. such as the described case of trust and safety with internet banking, lack of confidence in security is a big hindrance for internet banking adoption in the developing countries (aldammagh et al., 2021). customers may not have any strong confidence in online purchases and all that because they believe their sensitive data and information is not protected enough (usman et al., 2020). confidence can be created through robust security aspects, the use of open communication channels, and client education which aims to tackle these worries. also, the infrastructure inadequacies, which include unstable internet connections and pocket-like-devicesavailability, can be the impediments for the spread of internet banking in the developing world (rajasulochana, 2022). in this process, these factors must have to be taken in account when the applicability of tam framework is being considered for sudan and neighboring countries. challenges and opportunities of internet banking in sudan internet banking in sudan comes both with good and bad sides which might result from the mobile network and other things like the legal environment, the culture, and the attitude to the technical things (hussein et al., 2020). knowing these factors is the key to developing an important strategy and thus being able to get rid of the objections and have widespread using of internet banking services in the country. pa ge 25 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 sudan’s internet service capacity is inadequate, necessitating the provision of high-speed broadband and continuous flow services for widespread internet banking use (ahmed et al., 2014). sudan faces structural challenges such as inadequate internet networks and a lack of electronic devices in certain areas (eltahir, 2019). the improvement of internet infrastructure is crucial for expanding network coverage and increasing internet speed, enabling seamless and convenient access to internet banking services across the country (sahut, 2021). another key aspect is also the environment of regulation which is also in the list of the factors that affect online banking adoption (sekhar, 2020). sudan’s regulatory system must provide a favorable and nurturing environment for the ion and services of mobile banking (renzaho et al., 2022). constraints will address the sectors of data privacy, safety, consumer protection, and attribution of law to virtual transactions (mogos & jamail, 2021). a transparent set of rules dealing with these issues must be provided in order to attract customers with a high level of trust and confidence in their use of are banks online (malinka et al., 2022). cultural factors may influence sudanese consumers’ reluctance to adopt online banking due to concerns about security, trust, and the need for face-to-face financial assistance (khater et al., 2020). raising the awareness of the public regarding benefits of internet banking, countering the doubts replaced by internet security and highlighting examples of the convenience and effectiveness of online transactions are some cultural attitudes that would be changed, and acceptance of the technology will be promoted (indiani & fahik, 2020). banks and telecommunication companies can promote online banking in sudan by launching ecofriendly product campaigns to overcome customer misconceptions and increase adoption rates (khattab et al., 2020). mobile banking expands accessibility and convenience for many while enhancing security measures to ensure client safety (jun & palacios, 2016). trust and security concerns in internet banking adoption the adoption of internet banking in sudan is influenced by factors such as confidence and security beliefs, as highlighted in the literature stream (salim et al., 2016). banks implemented measures to build trust and confidence by addressing data privacy, online fraud, and cybersecurity (liyanaarachchi et al., 2021). the main focus of ensuring the widespread acceptance of internet banking is the protection of personal data privacy (rawwash et al., 2020). customers are definitely looking for confirmation that the entities holding their data are going to be reliable and protective and won´t abuse the information (sasono et al., 2021). although clear privacy policies, data safety, and compliance with data protection regulations are crucial for banks to alleviate customer concerns and boost trust (zhang et al., 2020). internet banking faces significant fraud concerns, causing unauthorized access and threatening clients (rossi et al., 2021). the study aims to evaluate the effectiveness of security measures in banks to prevent cyber fraud (datta et al., 2020). hence, these measures include multifactor authentication, encryption technologies, real-time transaction tracking, and fraud detection systems (karim et al., 2023). sudan recognizes the positive perception of security mechanisms among internet banking customers, emphasizing the importance of confidence in their acceptance of online banking services (hassan et al., 2020). customers face issues with personal and security data during pilot online transactions, including database leaks, unauthorized logins, and online frauds (mubarak alharbi et al., 2013). electronic banking has improved efficiency and affordability in 31 commercial banks in juba, south sudan, it has also led to reduced profitability. the findings suggest that banks should focus on expanding internet infrastructures and developing new online banking services to meet customer needs (kordit, 2022). cultural factors and user behavior in internet banking adoption cultural elements as regards the internet banking adoption rather figure in the customers’ perception-formation and their acceptability in sudan as elsewhere (sharma et al., 2020). the knowledge of sudanese culture norms, values, those here, and the views of society towards the technology are key for the understanding the effect on the internet banking usage patterns in the sudanese society (sleiman et al., 2021). in sudan, the most essential factor that influences the adoption of the innovations is traditional culture with the values attached with it, this includes internet banking (deshayes, 2022). the country of sudan attaches huge significance to the personal discussions, which are usually conducted in face-to-face mode as well as the building and maintaining of personal relationships in the world of finance (ahmed & ammar, 2020). many clients prefer to transact money in physical bank branches due to the perceived safety and trustworthiness associated with such banks (pavithra, 2021). for many, this strong cultural affinity for face-to-face interactions may serve as an impediment to the acceptance of internet banking by the general public, as some people would feel uneasy adopting virtual channels in place of the traditional banking networks which they are used to (arif et al., 2020). besides, behaviors of social context can determine the number of online banking users in sudan to a great extent (sleiman et al., 2022). the acceptance of technology varies across society, especially among older generations, due to their unfamiliarity with digital platforms and low technological exposure. to overcome attitudes towards internet banking, educate sudanese about its advantages, safety, islamic tenets, ease of interest transactions, and morality considerations, thereby broadening its acceptance and attractiveness (mansour et al., 2016). customers with higher transaction demand and efficiency and those in areas with higher online banking adoption density adopt pa ge 26 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 online banking faster, increasing banking activity and transaction efficiency (xue et al., 2011) materials and methods data for this study was collected by means of a survey conducted in khartoum. a total of 300 questionnaire forms were delivered to respondents of which 233 were returned giving a response rate of 71 percent. questionnaires were filled in five different banks and in different branches by selected customers with different banking treatments. this results in a sample that was well distributed in terms of demographic information e.g. age, education, income, and treatment period. the questionnaire consisted of questions that related to background, possible factors affecting ib and use of ib services. likert five point scales ranging from “strongly agree” to “strongly disagree” were used as a basis of questions. the scale has been used in previous tam related researches e.g. igbaria et al; 1995; teo et al; 1999, tero and kari; 2005. additionally, the “neutral” option was allowed in almost all questions. the questionnaire was developed and tested with a focus group consisting of professionals from sudan university and the banking sector. the focus group finally verified that the hypotheses might be an affective factor explaining ib acceptance. based on this information the questionnaire was modified and finalized. results and discussion model 1 table 1: model summary model r r square adjusted r square std. error of the estimate 1 .818a .670 .658 .29312 the model, overall, is satisfactory, as an r² that the model could explain about 67% for actual usage. the unitary analysis of the independent variables that are considered as the factors such as the perceivedusefulness, perceived -ease-ofuse, cultural-factors, trust, and socio-economic factors establish the significant relationship (and pattern) of the respondents’ internet banking usage in sudan. though the adjusted r-squared of 0.658 is a bit lower than the r-squared value, it yet demonstrates good adjustment of the model. these adjustment values consider the number of any predictor variables in the model and render a more cautious estimate for how much the model explains considering it is possible that the model will have the said problems of an overfit. the standard effect of the estimate (este in short), which shows the average variation that the observed values have from the regression line, is below 0.29312. this implies that the model’s accuracy in forecasting actual usage is on average relatively so close to the field values. table 2: anova model sum of squares df mean square f sig. 1 regression 19.875 4 4.969 57.829 .000b residual 9.795 114 .086 total 29.670 118 anova table is the ground zero of all the results and confirming the overall significance of the modeling of variance in the actual usage of internet banking services among respondents in sudan. the model is highly significant as is seen from the f-statistics of 57.829 which equals 0.000, denoted as “sig.” this shows that the model provides much better prediction than the null model that just picks data randomly. the total sum of squares for this regression model amounts to 19.875, it corresponds to the variation of actual usage which is attributable to any independent variables that are incorporated in this model. having 4 of the total degrees of value points, while an average mean value of 4.969 denotes the variance breakdown by each dimension vector. on the contrary, total sum of squares (unexplained variance) equals 9.795 with several 114 degrees of freedom as well. in other words, the proportion of actual usage not explained by the model’s independent variables is conveyed by this figure. table 3: coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) 2.010 .488 4.120 .000 pa ge 27 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 the coefficients table presents the estimated regression coefficients for the independent variables included in the model, along with their standard errors, standardized coefficients (beta), t-values, and associated significance levels (p-values). the constant term (constant) indicates the expected value of the dependent variable when all independent variables are zero. in this case, the constant term is 2.010, with a standard error of .488 and a significant t-value of 4.120 (p < .001), suggesting that it significantly contributes to the model. perceived usefulness (usefulness) has an unstandardized coefficient (b) of .188, indicating that for every one-unit increase in perceived usefulness, there is a corresponding increase of .188 units in actual usage of internet banking. the standardized coefficient (beta) is .156, suggesting a moderate positive effect on actual usage. the associated t-value is 2.109 (p = .037), indicating that perceived usefulness is statistically significant in predicting actual usage. perceived ease of use (perceived ease) has an unstandardized coefficient of -.198, indicating that for every one-unit increase in perceived ease of use, there is a corresponding decrease of .198 units in actual usage. the standardized coefficient (beta) is -.197, suggesting a moderate negative effect on actual usage. the associated t-value is -3.219 (p = .002), indicating that perceived ease of use is statistically significant in predicting actual usage. trust has an unstandardized coefficient of .132, with a standardized coefficient (beta) of .100. although the standardized coefficient suggests a positive effect on actual usage, the associated t-value is 1.679 (p = .096), which is not statistically significant at conventional levels (p < .05). cultural factors have an unstandardized coefficient of .490, with a standardized coefficient (beta) of .781. this indicates a strong positive effect of cultural factors on actual usage. the associated t-value is 9.634 (p < .001), indicating that cultural factors significantly predict actual usage. model 2 usefulness .188 .089 .156 2.109 .037 perceived ease -.198 .062 -.197 -3.219 .002 trust .132 .079 .100 1.679 .096 cultural factor .490 .051 .781 9.634 .000 table 4: model summary model r r square adjusted r square std. error of the estimate 1 .525a .275 .250 .33913 the model’s summary shows a moderate positive relationship between independent and dependent variables, with a r-squared value of 0.275 and an adjusted r-squared value of 0.250. the model’s standard error of estimate is 0.33913, indicating some variability in predictions. however, the model’s explanatory power may be enhanced by incorporating additional factors not accounted for in the model. table 5: anova model sum of squares df mean square f sig. 1 regression 4.978 4 1.244 10.821 .000b residual 13.111 114 .115 total 18.089 118 the anova table reveals the significance of the regression model in explaining the variance in the dependent variable, actual usage of internet banking services. the model’s f-statistics of 10.821 and p-value of.000 indicate that the independent variables significantly explain the variation in the dependent variable. the sum of squares is 4.978, with a mean square value of 1.244. table 6: coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) 3.233 .564 5.727 .000 usefulness -.331 .103 -.351 -3.204 .002 pa ge 28 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 these coefficients represent the estimated effects of each independent variable on the dependent variable (likely actual usage of internet banking services) in the regression model. the unstandardized coefficients (b) indicate the change in the dependent variable for a oneunit change in the independent variable, holding other variables constant. the standardized coefficients (beta) allow for a comparison of the relative importance of each independent variable in predicting the dependent variable. the t-values represent the significance of each coefficient, with associated p-values indicating whether the coefficients are statistically significant. in this model, perceived usefulness, trust, and to a lesser extent, perceived ease of use, are significant predictors of actual usage of internet banking services, while cultural factors show a borderline significance. the table reveals the impact of actual usage and behavioral intention models on internet banking adoption in sudan. it reveals that understanding user decisions, prioritizing improvements, and developing targeted interventions can help banks understand and address the factors influencing adoption. the study also suggests that understanding these factors can help predict future trends, aiding in strategic planning and resource allocation. the findings highlight the importance of cultural understanding in the adoption of internet banking. correlation analysis perceived ease .128 .071 .162 1.794 .076 trust .429 .091 .413 4.700 .000 cultural factor .113 .059 .231 1.921 .057 table 7: correlations usefulness perceived ease trust behavioural actual usage cultural factor usefulness pearson correlation 1 .257** -.135 -.208* .627** .685** sig. (2-tailed) .005 .143 .024 .000 .000 n 119 119 119 119 119 119 perceived ease pearson correlation .257** 1 .199* .238** .146 .361** sig. (2-tailed) .005 .030 .009 .114 .000 n 119 119 119 119 119 119 trust pearson correlation -.135 .199* 1 .431** -.171 -.269** sig. (2-tailed) .143 .030 .000 .063 .003 n 119 119 119 119 119 119 behavioural pearson correlation -.208* .238** .431** 1 -.105 -.062 sig. (2-tailed) .024 .009 .000 .256 .501 n 119 119 119 119 119 119 actual usage pearson correlation .627** .146 -.171 -.105 1 .790** sig. (2-tailed) .000 .114 .063 .256 .000 n 119 119 119 119 119 119 cultural factor pearson correlation .685** .361** -.269** -.062 .790** 1 sig. (2-tailed) .000 .000 .003 .501 .000 n 119 119 119 119 119 119 the correlation matrix provides insights into the relationships among the variables under study. notably, significant correlations (p < 0.01) are observed between several pairs of variables. perceived usefulness demonstrates a strong positive correlation with both actual usage (0.627**) and cultural factor (0.685**), indicating that individuals who perceive internet banking as useful are more likely to use it frequently and are influenced by cultural factors. similarly, perceived ease of use exhibits a moderately positive correlation with cultural factor (0.361**) and a weak positive correlation with actual usage (0.146). trust shows a moderately strong positive correlation with behavioral intention (0.431**) and a weak negative correlation with actual usage (-0.171). actual usage is strongly correlated with both perceived usefulness (0.627**) and cultural factor (0.790**), suggesting that individuals who use internet banking services frequently perceive them as useful and are influenced by cultural factors. additionally, cultural factor demonstrates a strong positive correlation with actual usage (0.790**) and perceived usefulness (0.685**), indicating the significant influence of cultural pa ge 29 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 reveal that features like venue, user-friendliness, trust management, and cultural factors play important parts in deciding customers’ actual use of internet banking products. important are the design and the functionality for the customers with the aim of reaching the highest satisfaction and enhanced banking experience. sociocultural factors are also factors that should be factored in, which increases the need for banks to package their services for them to be compatible with what the african sudanese culture prefers. security and credibility are important, but this does not surely impact adoption (the crafting of the block chain system cannot point to any factor but in the case of adoption). through mobile devices use as digital commercial instruments are rising; millions worldwide can now receive quick, low cost and safe money services. yet, there are some limitations that stop tam constructs from fully understanding sudanese behavior toward the switch. this study highlights the need for a deep knowledge of the non-traditional factors that fuel the growth of electronic payment systems particularly in sudan. this information can strategically guide government, business, or other social parties in enhancing the effectiveness and acceptance of electronic payment systems in the future. reference abad-segura, e., gonzález-zamar, m. d., lópezmeneses, e., & vázquez-cano, e. 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(2020). the mediating role of perceived security on the relationship between factors on internet banking adoption and perceived usefulness. these correlations collectively provide valuable insights into the interplay between perceived utility, ease of use, trust, cultural factors, behavioral intentions, and actual usage of internet banking services. discussion the rapid growth of information technology and communication has significantly impacted global lifestyles, particularly in business conduct, with electronic payment becoming a key aspect (sleiman et al., 2021). the experimental examination of technology acceptance model (tam) in e-payment system dimensions in sudan is important since it affords insights on the determinants of internet financial engineering in the country (uche et al., 2021). the findings were concluded with the point to the high-level utility, user friendly, mutually trust management and cultural aspects that influence the customers’ actual usage of the internet banking products (lok, 2015). the growth of electronic payment systems, which involve electronic transactions between consumers and retailers, has been driven by the widespread use of internet-based banking and shopping, playing a crucial role in contemporary electronic commerce (hassan et al., 2020). technological innovation is a key factor in promoting innovative electronic communications and transactions, making it an essential antecedent (rahman et al., 2022). overall, useful thinking and usability were found to be the necessary factors for internet banking to be successful, implying that it is vital to make the design and functions convenient for customers because good user experience is a key to keeping them satisfied and enhance their banking experiences (chau & lai, 2003). furthermore, the study finds cultural variables as a very powerful predictor of the actual usage, thus banks should try to put their services in a par with the social cultural hues and choices of the customer in the sudanese society (keller & brennan, 2007). there is a positive but statistically significant link between security and online banking usage with trust, but trust doesn’t significantly influence adoption (aribake & mat aji, 2020). credibility influences internet banking perception, but security, reliability, and context limitations exist. tam constructs in sudan may not fully explain user behavior towards switching. mobile technology is a crucial ict financial instrument in emerging economies, offering fast, affordable, and secure finance to millions worldwide through the first mobile banking application (sleiman et al., 2022). conclusion there are certain critical issues, such as compliance with electronic payment systems and the wider use of internet banking, that are pivotal to the issues of advancement and education. the technology acceptance model (tam) has played a significant role in a country’s internet financial engineering by investigating the main predictive variables (determinants). first, the survey results pa ge 30 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 23-31, 2025 internet banking users and their determinants. international journal of advanced research in engineering and technology (ijaret), 11(2). arif, i., aslam, w., & hwang, y. 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(2020). online customer trust in the context of the general data protection regulation (gdpr). pacific asia journal of the association for information systems, 12(1), 4. pa ge 1 pa ge 24 american journal of financial technology and innovation (ajfti) the impact of drug pricing and insurance coverage on medication adherence in saudi turki m. alsagoor1* volume 1 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: october 05, 2023 accepted: november 01, 2023 published: november 08, 2023 this study explores the complex interactions in saudi arabia between medicine costs, insurance coverage, and medication adherence. 250 individuals’ data were analysed, and significant relationships were found. with a significant coefficient of -0.773 (p = 0.006), high medicine prices were shown to be a significant barrier to medication adherence. on the other hand, complete insurance protection showed a favorable connection with adherence, highlighting its crucial function. the fact that 69% of the participants had health coverage and 31% did not highlights a sizable insurance gap. males comprised 80% of the sample, 62% of respondents were in the 29–39 age range, and 56% had bachelor’s degrees. the income range showed that 43% made more than 20,000 sar monthly. the significant effects of medicine cost and insurance coverage on medication adherence were highlighted by regression analysis (beta = -0.749, p = 0.001, and 0.759, p = 0.006, respectively). these results highlight the criticality of addressing the affordability of prescription costs and advocating for open insurance programmes to improve healthcare outcomes in saudi arabia. prioritising actions can help reduce costs and provide comprehensive coverage, say policymakers. this study emphasises the need for more research to dive further into the intricacies of healthcare accessibility, particularly in a healthcare environment that is continually expanding. keywords medical adherence, drug pricing, insurance coverage, medical insurance, medicine cost introduction pharmaceutical pricing is a complex process considering profit margins, manufacturing costs, and costs associated with research and development (schlander et al., 2021). pharmaceutical firms, wholesalers, distributors, pharmacies, insurance companies, and public healthcare programs are a few of the many parties involved in this complex process, substantially impacting patients’ access to pharmaceuticals (wong et al., 2023). due to this, the challenge of high drug prices becomes even more daunting due to the scarcity of essential affordability for patients (adebisi et al., 2022). the expensive cost of medication can create barriers, causing individuals to stop taking their prescribed drugs, resulting in detrimental health problems (ding et al., 2022). furthermore, between medication acquisition and fulfilling other essential requirements, such as housing, sustenance, or utilities, patients may be forced to make arduous decisions due to monetary constraints (malecha et al., 2018). consequences of treatment failure may lead to individuals resorting to reduced doses, missed doses, or treatment discontinuation, resulting in non-adherence. non-compliance with treatment guidelines can lead to costlier care and a greater chance of developing complications, according to (emadi et al., 2022). emadi et al. (2022) state that the interplay between drug pricing and insurance coverage profoundly affects ma, touching numerous parties, including individuals, healthcare providers, and society. extending it further frieden et al.,(2019) has highlighted that due to the combination of mounting drug prices and suboptimal insurance coverage, ma reduction can lead to adverse impacts on patient health and the larger healthcare system (emadi et al., 2022; frieden et al., 2019). high prescription drug prices are prominent in countries with market-based healthcare systems, such as the united states (patel et al., 2018). these soaring prices result from research and development expenses, regulatory clearance costs, pricing strategies employed by pharmaceutical companies, and limited negotiation leverage held by payers (bhide, 2022). according to gonzález vera et al.’s (2022) findings, patients frequently struggle with adherence due to financial burdens (gonzález vera et al., 2022). by covering the cost of prescription medications, insurance plays a critical role in reducing the financial strain. comprehensive insurance plans enhance medication adherence, and lower out-of-pocket costs are made possible (reynolds et al., 2020). wide variation exists in insurance coverage, leaving room for confusion and frustration. according to meng et al. (2015) and yang et al. (2020), such measures as prior authorisation and step therapy create challenges to accessing vital medications (meng et al., 2015; yang et al., 2020). all stakeholders, including patients, healthcare providers, insurance companies, and policymakers, must join forces to find practical solutions for complex challenges (bali & ramesh, 2023). measures to advance price transparency, boost competition in the pharmaceutical sector, and control prescription pricing can be undertaken by policymakers (ahmad et al., 2020). mandating prescription drug coverage by insurance providers could increase patient compliance rates. saudi arabia’s study aims to investigate the effect of drug pricing and insurance coverage on medication adherence (ma) (alqarni et al., 2019). high 1 saudi food and drug authority, saudi arabia * corresponding author’s e-mail: tmsagoor@sfda.gov.sa pa ge 25 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 medicine costs and subpar insurance protection in light of the increasing frequency of persistent non-communicable disorders impact patient compliance with medication (dias, 2023). by examining the interplay of drug pricing, insurance coverage, and ma in saudi arabia, the research will identify evidence-based approaches to strengthen medication affordability, coverage, and adherence. as they relate to public health, these findings offer significant improvement opportunities, cost containment, and access enhancements, thus contributing positively to society. literature review across the globe, there has been an increase in research on drug pricing, insurance, and medication adherence (ma) in recent times. diving deeper into the intricacies of the issue, manna et al (2020) has highlighted a significant need to shed light on its implications for public health outcomes. the study has further emphasised examining the relationship between drug pricing, insurance coverage, and ma in saudi arabia, as well as evidence-based solutions that can be applied to address these challenges (manna et al., 2020). a complexity in drug pricing and insurance characterises: the current setup in saudi arabia and numerous other nations, drug pricing is an intricate process influenced by multiple factors (almutairi et al., 2023). pharmaceutical companies et al., play a part in deciding how much patients or their insurance providers ultimately pay in the end. per iqvia (2021), saudi arabia’s pharmaceutical market, valued at $7.23 billion in 2021, is poised for significant growth in the forthcoming year. as cited by yang eta al, (2020), the above factors contribute to the growth seen here. examining the current drug pricing schemes is essential since they directly impact the saudi healthcare system (yang et al., 2020). patients in saudi arabia may face drug cost increases due to poorly designed pricing structures. ding et al. (2022), has highlighted that with high medication costs, individuals may need help prioritising necessities or their medications. the ministry of health of saudi arabia (2020) notes that approximately 60% of pharmaceutical spending in saudi arabia was financed through direct patient payments(ministry of health & saudi arabia, (2017). due to this alarming statistic, the population’s access to affordable medication is imperative. distinct policies offer varied levels of coverage when it comes to insurance in saudi arabia (albugmi, 2021). depending on the plan, some insurance policies may cover drugs comprehensively, while others may levy high co-payments and deductibles upon patients (meng et al., 2015). a sama study found that in 2021, only 24% of the population had health insurance coverage, indicating restricted access to coverage. insurance coverage disparities substantially hinder all demographic groups’ access to essential medications (adebisi et al., 2022). relationship between drug pricing and insurance and its impact on medical adherence the effect of prescription drug pricing and insurance coverage is substantial in saudi arabia’s changing healthcare landscape. patients’ capacity to follow their prescribed medicine regimens depends largely on their financial situation (emadi et al., 2022). evidence supports that medication prices, insurance coverage, and adherence are connected, such as the study by smith et al., (2019), stresses that patients who experienced financial constraints due to limited insurance coverage had lower medication adherence (smith et al., 2019). lifestyle factors and socio-economic changes have caused a notable increase in the prevalence of chronic non-communicable diseases such as diabetes and cardiovascular diseases in saudi arabia (al-hanawi & keetile, 2021), . managing diseases via medication makes non-adherence a critical problem. mohiuddin, (2019) found that approximately 60% of patients with chronic conditions in saudi arabia fail to comply with medical regimens. the study has also noted adverse effects are experienced at both the personal and communal levels due to medication adherence (mohiuddin, 2019). emadi et al. (2022) highlight that individuals may suffer from uncontrolled disease progression, resulting in higher morbidity and mortality rates (emadi et al., 2022). according to anderson et al. the potential for preventable hospitalisations and complications has significant consequences at the societal level (anderson et al., 2020). approximately 13.5 billion saudi riyals ($3.6 billion) more was added by non-adherence to medical therapies, as reported by saudi aramco, (2023). these statistics showcase, non-adherence in the saudi healthcare ecosystem demands immediate attention (aramco, 2023). evidence-based approaches for enhancing medical adherence evidence-based methods are essential to promote adherence among the saudi population, and understanding how drug pricing and coverage contribute to this is vital. value-based pricing models gaining favor, value-based pricing models now prioritise drug price alignment with therapeutic value. according to stecker et al. (2015), determining the cost of a medication involves considering factors including efficacy, safety, and alternative treatments (stecker et al., 2015). alignment with their benefits and subsequent cost reduction is what patients may see by implementing value-based pricing in saudi arabia. increasing price transparency in addition to other factors, price transparency is vital in controlling medication costs. transparency in pricing assures decision-making across all groups for medication choices and coverage (godman et al., 2021). saudi arabia’s increased price openness paves the way for pharmaceutical company competition, resulting in more affordable drugs. pa ge 26 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 expanding insurance coverage expanding insurance coverage is crucial to achieve medication availability for all. cairney and oliver (2017) suggest that policymakers in saudi arabia should focus on expanding public health insurance systems while offering financial support to low-income individuals and ensuring that insurance companies include comprehensive prescription coverage in their policies (cairney & oliver, 2017). through these measures, the gap in insurance coverage can be closed, resulting in increased accessibility to medications for the public (knapp & wong, 2020). healthcare provider interventions healthcare providers have a significant role in ma, including selecting economical treatment options, considering patients’ financial conditions, and collaborating with patients in shared decision-making to overcome adherence challenges (ahmed et al., 2018) (ahmed et al., 2018). following al jeraisy et al. (2023) lead, they help ease the financial pressure on patients by coordinating their access to assistance programs (al jeraisy et al., 2023). government initiatives it is pertinent for policymakers to create laws that restrain drug costs, promote competition in the pharmaceutical business, and guarantee price negotiations for prescription medications. according to sanders et al. (2016), tax breaks may be offered to reduce patients’ financial responsibilities. multistakeholder collaboration multiple stakeholders must work together, and lastly. findings from ding et al. (2022) underscore the importance of cooperative efforts from all parties concerned patients, healthcare professionals, insurers, pharmaceutical companies, and policymakers when it comes to enhancing adherence and overcoming economic obstacles. intertwined factors of drug pricing, insurance coverage, and medication adherence necessitate harmonious coordination between these parties for effective resolution (kanyongo & ezugwu, 2023). hypothesis the following hypothesis has been formed by examining the relationship between medical adherence and drug prices and the influence of medical insurance. hypothesis 1 h1a: high medicine prices negatively impact ma in saudi arabia. hypothesis 2 h2a: lower ma in saudi arabia is related to insufficient insurance coverage. hypothesis 3 h3a: increasing insurance coverage and implementing policy changes can help with ma in saudi arabia. material and methods the study employed a quantitative research method to examine the linkages between drug pricing and insurance coverage and their influence on medication adherence among individuals residing in saudi arabia. through the cross-sectional method, interrelationships were scrutinised and grasped. the selection of quantitative research hinges on its efficient and impartial data collection and analysis capabilities. the positivist perspective emphasises using objective and verifiable evidence to bolster the advancement of the process. mahajan (2020) states that larger sample sizes translate to greater generalizability. as creswell and hirose (2018) described, quantitative research excels when utilising standardised and systematic data collection methods. research design the study employed a cross-sectional approach to collect information simultaneously from a diverse population. medication adherence can be influenced by reviewing three factors: drug pricing, insurance coverage, and income. correlation analysis between variables was enabled through bhardwaj’s design (2019). further characteristics of the research process are described in the following sections: target population and sampling due to drug pricing, income, and insurance coverage, the study group included all saudi arabian citizens experiencing difficulties with medication adherence. according to bujang et al. (2018), a sample of 250 individuals was surveyed using randomised sampling to obtain a reliable representative of the general public. 95% confidence and a 5% margin of error governed the investigation process. data collection approach data collection comprised a questionnaire disseminated online using google forms to ensure confidentiality and ease. participants were evaluated on their opinions of prescription cost, insurance coverage, and medication adherence using closed-ended questions on a likert-type scale. validity and reliability were established through pretesting on a representative sample (bujang et al., 2018). data analysis spss and microsoft excel are employed for data analysis because of their versatility in social sciences and how they easily handle vast amounts of data to provide essential insights(baker et al., 2022). data’s characteristics can be understood using descriptive statistics, which consist of the mean, median, mode, and standard deviation (creswell & hirose, 2019). the correlation matrix, shown by graff elman and de leeuw (2023), discovered ties among variables (graffelman & de leeuw, 2023). employed in an analysis process, pca and fa helped identify patterns (li et al., 2021). additionally, their importance extended beyond the realm of data analysis alone. employed by mooi et al. (2018), multiple linear regression examined the relationships between dependent and independent variables. furthermore, bujang et al. (2018), has stressed pa ge 27 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 that cronbach’s alpha was used to determine survey reliability (bujang et al., 2018; mooi et al., 2018). ethical considerations maintaining ethical principles was crucial throughout the research. participants provided informed consent after being informed about the potential benefits and risks. privacy is safeguarded thanks to identification codes and data encryption. no consequences existed for participants who chose not to participate in the study. preventing research abuse requires maintaining data evaluation transparency and integrity (see ethical considerations). great care was taken to observe ethical principles throughout the research to guarantee the security and comfort of participants. ethical considerations demand the protection of integrity and respect for participants’ rights. informed consent collaboration was fostered when participants were presented with thorough information regarding the study’s goals, procedures, and potential gains and drawbacks. consent from all participants was obtained after educating them on the process. privacy protection safeguarding participants’ privacy and data required the implementation of specific measures. individual anonymity was maintained by assigning unique identification codes, data encryption, and results aggregation. non-coercion there were no adverse repercussions for either expressing ethical concerns or choosing to opt out of the study. their participation was entirely voluntary. transparency and data integrity to maintain the highest levels of ethical standards, all parties involved ensured that no form of research misconduct entered into their procedures, such as selective reporting of results or distortion of figures. before data dissemination, accuracy and transparency were scrutinised. by incorporating ethical factors, the study ensured high research integrity, participant well-being, and credibility. results and discuussion this research delves into a critical issue plaguing the healthcare landscape in saudi arabia-the intricate interplay between drug pricing, insurance coverage, and medication adherence. our study is not just an exploration but a clarion call to recognise the far-reaching ramifications of these factors on the health and well-being of saudi arabian citizens. in an era where access to healthcare is paramount, understanding these dynamics can pave the way for more robust health policies and improved patient outcomes. to unearth the undeniable impact of drug pricing and insurance coverage on medication adherence, we embarked on a journey driven by methodical rigour and precision. our primary data collection leveraged the online survey method, engaging with 250 participants selected through simple random sampling. the arsenal of statistical tools at our disposal included microsoft excel and the venerable ibm spss-26 (george & mallery, 2019), enabling us to conduct comprehensive analyses encompassing descriptive, factor, correlational, and regression analyses. these analyses uncovered the latent variables at the heart of our research-drug pricing, insurance coverage, and medication adherence. demographic insights the demographic landscape of our study is a mosaic that reflects the diversity of saudi arabia. understanding our participants provides the foundation upon which our research is built. the findings of the study as illustrated in table 1 reveal a spectrum of age groups among participants. while 62.4% fall within the 29-39 years bracket, a substantial 14.8% belong to the 18-28 years and 40-50 years groups. remarkably, even participants above 50 years make up 8% of our cohort. of the 250 participants, 15% belong to the 18-28 age group, 62% to the 29-39 years group, 15% to the 40-50 years group, and 8% are more than 50 years old. gender distribution of the study participants, as depicted table 1: age of respondents frequency per cent valid percent cumulative percent valid 18-28 years 37 14.8 14.8 14.8 29-39 years 156 62.4 62.4 77.2 40-50 years 37 14.8 14.8 92.0 more than 50 years 20 8.0 8.0 100.0 total 250 100.0 100.0 table 2: gender of participants frequency per cent valid percent cumulative percent valid female 51 20.4 20.4 20.4 male 199 79.6 79.6 100.0 total 250 100.0 100.0 pa ge 28 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 in table 2, underscores the gender disparity in our study. a striking 80% of participants are male, while females constitute 20%. the study’s findings illustrated in table 3. have shown that among the study participants, 56% hold bachelor’s degrees, 31% have master’s degrees, 8% have high school diplomas, and 5% are ph.d. holders. the monthly income of the participants is equally varied, as shown in table 4. notably, 43% earn more than 2000 sar monthly, while 18% earn below 10,000 sar. while 20% of the participants have income between 1600 to 2000 saw per month, the rest, 18%, have 110000-15000 average monthly income. the study’s findings in table 5. have also revealed that people from various regions have participated, with the central region dominating at 70.8%. the western region follows at 12.8%, while other regions have smaller representations: 3% in the eastern region, 1% outside of the kingdom of saudi arabia, 13% in the western region, and 9% in the southern region. a vital facet of our research is insurance coverage, illustrated in table 6, revealing that almost 69% of participants possess insurance coverage, while 31% do not, highlighting that 1/3 of the saudi population doesn’t have insurance coverage. in contrast, the hospitals worked only a negligible amount of insurance. table 3: education level frequency per cent valid percent cumulative percent valid bachelors’ degree 140 56.0 56.0 56.0 high school 19 7.6 7.6 63.6 master’s degree 77 30.8 30.8 94.4 ph.d. 13 5.2 5.2 99.6 pharmd 1 .4 .4 100.0 total 250 100.0 100.0 table 4: monthly income of participants frequency per cent valid percent cumulative percent valid 110000-15000 sar 46 18.4 18.4 18.4 16000-20000 sar 51 20.4 20.4 38.8 less than 10000 sar 45 18.0 18.0 56.8 more than 20000 sar 108 43.2 43.2 100.0 total 250 100.0 100.0 table 5: residence of participants frequency per cent valid percent cumulative percent valid central region 177 70.8 70.8 70.8 eastern region 8 3.2 3.2 74.0 i live outside of the kingdom of saudi arabia 2 .8 .8 74.8 northern region 8 3.2 3.2 78.0 southern region 23 9.2 9.2 87.2 western region 32 12.8 12.8 100.0 total 250 100.0 100.0 table 6: insurance coverage frequency per cent valid percent cumulative percent valid covered by the hospital i work in 1 .4 .4 .4 no 78 31.2 31.2 31.6 yes 171 68.4 68.4 100.0 total 250 100.0 100.0 measurement of drug pricing reliability statistics for drug pricing, as shown in table 7. shows volumes, as cronbach’s alpha coefficient of 0.810 indicates strong internal consistency among the five items used to measure drug pricing. these results validate the efficacy of our drug pricing measurement. the total variation in observable variables that may be explained by drug pricing is shown in table 8. and pa ge 29 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 table 7: reliability statistics of drug pricing cronbach's alpha cronbach's alpha based on standardised items n of items .814 .810 5 table 8: total variance explained by drug pricing in observed variables component initial eigenvalues extraction sums of squared loadings total % of variance cumulative % total % of variance cumulative % 1 2.903 58.066 58.066 2.903 58.066 58.066 2 .927 18.534 76.601 3 .469 9.386 85.987 4 .391 7.817 93.804 5 .310 6.196 100.000 extraction method: principal component analysis table 9: component matrix (drug pricing) component 1 high drug prices significantly affect my ability to purchase and use medications as prescribed consistently. .813 i have skipped or reduced medication doses due to the cost of the drugs. .809 i have experienced financial strain due to the cost of my medications. .814 lowering drug prices would improve medication adherence in saudi arabia. .482 the cost of medications has led me to seek alternative treatments or therapies. .834 extraction method: principal component analysis emphasises how important this factor is. the first component is significant in our analysis since it accounts for 58.066% of the total variation. the component matrix (drug pricing) findings are illustrated in table 9. elucidates the relationships between observed variables and the extracted component. notably, observed variables exhibit strong loadings on component 1, further emphasising the relevance of drug pricing in our research. for example, observed variable 4, “seeking alternative treatments due to medication cost,” had a high loading of 0.834 and a good correlation with component 1 of drug price. other factors also exhibit significant loadings on component 1, confirming their significance in determining medicine pricing. measurement of insurance coverage reliability statistics for insurance coverage, in table 10. reinforce the credibility of our measurement scale with a cronbach’s alpha coefficient of 0.791. the entire variation explained by insurance coverage is shown in table 11 the first component solidifies its status as a significant factor by explaining 47.814% of the total variation. the component matrix (insurance coverage) in table 12 highlights the significant correlations between the extracted component and the observed variables, further supporting the significance of insurance coverage. table 10: reliability statistics of insurance coverage cronbach's alpha cronbach's alpha based on standardised items n of items .768 .791 5 table 11: total variance explained by insurance coverage in observed variables component initial eigenvalues extraction sums of squared loadings total % of variance cumulative % total % of variance cumulative % 1 2.391 47.814 47.814 2.391 47.814 47.814 2 1.246 24.912 72.726 3 .625 12.497 85.223 4 .441 8.812 94.035 5 .298 5.965 100.000 extraction method: principal component analysis pa ge 30 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 measurement of medication adherence with a cronbach’s alpha coefficient of 0.824, the reliability data for medication adherence shown in table 13 support the validity of our assessment scale. the reliability of the measuring items for medication adherence is attested to by cronbach’s alpha value of 0.824. the entire variation explained by medication adherence is shown in table 14. a significant 59.789% of the total variation is explained by the first component, demonstrating its importance. the first component accounts for 59.789% of the variation overall, highlighting the importance of medication adherence. based on the principal component analysis, table 15 shows the component matrix of medication adherence and its observed factors. the link between each observed variable and the extracted component is indicated by the numbers in the component 1 column. higher absolute values show a stronger association. the observed variable 3, for instance, has a high loading of 874 on component 1, indicating a substantial link between this variable and the extracted component of medication adherence. i am confident i can continuously follow my medication plan without missing doses. table 12: component matrix (drug pricing) component 1 my current insurance coverage adequately covers the cost of my prescribed medications. .785 i have experienced difficulties in obtaining insurance coverage for specific medications. .464 i am satisfied with the range of medications covered by my insurance provider. .863 the insurance claim process for medications is simple and efficient. .777 i have avoided or delayed seeking medical care due to concerns about insurance coverage for medications. .460 extraction method: principal component analysis. a. 1 component extracted: (insurance coverage) table 13: reliability statistics of medication adherence cronbach's alpha cronbach's alpha based on standardised items n of items .806 .824 5 table 14: total variance explained by medication adherence in observed variables component initial eigenvalues extraction sums of squared loadings total % of variance cumulative % total % of variance cumulative % 1 2.989 59.789 59.789 2.989 59.789 59.789 2 1.012 20.245 80.034 3 .447 8.931 88.965 4 .310 6.209 95.174 5 .241 4.826 100.000 extraction method: principal component analysis. table 15: component matrix (medication adherence) component 1 i understand the importance of adhering to my medication regimen to achieve the desired therapeutic outcomes. .788 taking medications exactly as my healthcare provider prescribes is essential for managing my health condition(s). .841 i am confident in following my medication schedule consistently without missing doses. .874 i actively communicate with my healthcare provider if i experience any difficulties or side effects related to my medications. .786 i have a system (e.g., reminders, pill organisers) to help me manage my medication adherence effectively. .529 extraction method: principal component analysis. a. 1 component extracted: (medication adherence) pa ge 31 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 results of correlation analysis the pearson correlation test findings in table 16 show significant correlations between the variables. drug cost and insurance coverage have a substantial negative connection (r = -0.768, p 0.001), while their link with medication adherence is inverse and negative (r = -0.773, p = 0.006). similarly, while it is not statistically significant, a positive association exists between insurance coverage and medication adherence (r = 0.002, p = 0.709). these results imply that increasing insurance coverage and medication adherence are related to higher drug prices. however, a weak and statistically insignificant link exists between insurance coverage and drug adherence. these findings in this study indicate the possible impact of drug costs on insurance coverage and medication adherence. table 16: results of the pearson correlation test drug pricing insurance coverage medication adherence drug pricing pearson correlation 1 .768** -.773** sig. (2-tailed) .000 .006 n 250 250 250 insurance coverage pearson correlation -.768** 1 .742 sig. (2-tailed) .000 .009 n 250 250 250 medication adherence pearson correlation -.773** .002 1 sig. (2-tailed) .006 -.768 n 250 250 250 **. correlation is significant at the 0.01 level (2-tailed). results of regression analysis because the value of p is less than 0.05, which symbolises the significant differences between the groups of the mean of each variable, the above-given anova table demonstrates that all the provided variables are substantially different. the regression analysis results employing the two independent variables of drug cost and insurance coverage are summarised in table 17. unstandardised coefficients (b) depict the anticipated change in medication adherence when each independent variable is changed by one unit while the others remain unchanged. the standardised influence of these factors on medication adherence is measured by standardised coefficients (beta). the statistical significance is determined by the t-values and p-values (sig.). the substantial effects of both independent factors on medication adherence have confirmed all research hypotheses. regression analysis p-values show a robust association between high drug costs, insurance coverage, and medication adherence. table 17: results of the anova test model sum of squares df mean square f sig. 1 regression 12.288 2 6.144 6.411 .002 residual 236.713 247 .958 total 249.000 249 a. dependent variable: medication adherence b. predictors: (constant), insurance coverage, drug pricing table 18: results of multiple regression analysis model unstandardised coefficients standardised coefficients t sig. b std. error beta 1 (constant) 4.070e-7 .062 .000 1.000 drug pricing -.747 .070 .749 3.516 .001 insurance coverage .758 .070 .759 2.243 .006 a. dependent variable: medication adherence discussion the findings of a current study highlight the difficulty of medication adherence in saudi arabia. this study reveals the relevance of medication cost and insurance coverage as the foundation of healthcare outcomes. according to the study, expensive medications have a considerable negative impact on medication adherence. the analysis revealed a disturbing truth: patients who struggle with pa ge 32 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 high pharmaceutical expenses are more prone to noncompliance. for those with chronic illnesses, there are significant accessibility barriers, and these barriers are especially obvious when considering prescription costs. the findings of kennedy-martin et al. (2017) show how expensive prescription drugs combined with insufficient insurance can result in significant patient outlays (kennedy-martin et al., 2017). the study by torresrobles et al. (2021), examining this relationship found that high out-of-pocket expenses are associated with reduced drug adherence, particularly evident in those dealing with chronic illnesses. these results demonstrate that medicine costs significantly influence patient behaviour (torresrobles et al., 2022). despite saudi arabia’s advanced healthcare system, high drug costs make it difficult for patients to comply with their treatments effectively. according to lee et al.’s (2020) study, people with multiple sclerosis must change their medicine consumption due to high out-of-pocket expenses. compromise in adherence caused by expensive drugs is a serious issue since it adversely affects patient outcomes (lee et al., 2020). high prescription prices may give off a negative impression, but the study demonstrates that adherence is supported by full insurance coverage. compliance rises when patients have access to insurance or less expensive medication choices. this conclusion emphasises the need for comprehensive and user-friendly insurance coverage for patients’ access and affordability. this theory is supported by two recent studies, those of yang et al. (2020) and wong et al. (2023), which demonstrate that health insurance eases the financial burden on patients by paying for a portion of their medical expenses (wong et al., 2023; yang et al., 2020). according to these aforementioned researches those with health insurance are more likely to follow their prescriptions. investments in insurance coverage-focused policies can increase adherence and enhance health outcomes, making them beneficial. in addition to examining the connection between prescription costs and insurance coverage, this study also examines other aspects of medication compliance. patient education and awareness campaigns are among the most important factors in encouraging adherence. providing patients with prescription information, such as dosing instructions and possible adverse effects, improves adherence to the prescribed course of action. according to research by reed et al. (2017), improved education results in higher adherence rates. equal focus should be placed on patient and healthcare professional communication (reed et al., 2017). when communication improved and became more compassionate, medication compliance increased. the relationship between positive patient interactions and medication compliance is clear when patients report. as brown et al. (2016) demonstrated, lowering patients’ expenditures necessarily increases adherence. the study emphasises the significance of easily available pharmacies. better adherence was closely correlated with easier pharmacy access, with patient convenience playing a crucial role (brown et al., 2016). morrissey et al. (2016) emphasise the importance of convenience in healthcare delivery, noting that patients may need more access to their treatment regimens (morrissey et al., 2016). conclusion in conclusion, the results of this study offer important new perspectives on the intricate relationships between saudi arabia’s medicine prices, insurance coverage, and medication adherence. the study used a quantitative research methodology to examine the correlations between these characteristics by gathering data from a sample of 250 people. the investigation produced significant results illuminating saudi arabia’s healthcare management possibilities and difficulties. the study first highlighted the considerable effect of expensive medicine costs on medication adherence. it was shown that patients who had trouble paying for their drugs were more likely to have poorer adherence rates. these findings support the body of material already in existence by highlighting the link between patient compliance and drug prices. studies by kennedy-martin et al. (2017), torres-robles et al. (2021), and yosef et al. (2023) have all shown that patients, particularly those with chronic conditions, may incur higher out-of-pocket costs as a result of high prescription drug prices. this emphasises the importance of affordability in influencing drug adherence behaviours. the study results show a considerable association between high drug prices and nonadherence (r = -0.773, p = 0.006). on the other hand, extensive insurance protection encourages adherence. sixty-nine per cent of the 250 participants had insurance, while just 31% did not. participants were split 80/20 between men and women. approximately 62% of the respondents in the survey were in their twenties to thirties, 56% had bachelor’s degrees, and 43% had monthly incomes above 20,000 sar. the largest portion (70.8%) was from the central area. the substantial effects of medicine cost and insurance coverage on medication adherence were also verified by regression analysis (beta = -0.749, p = 0.001 and beta = 0.759, p = 0.006, respectively). while extensive insurance coverage encourages compliance, exorbitant medication prices impede it. affordability and accessibility of insurance plans should be a priority for policymakers to enhance. recommendations the findings of this study can have a significant influence on saudi arabia’s healthcare policies. policymakers should think about a multifaceted strategy to improve drug adherence and, subsequently, health outcomes: affordable drug pricing reducing exorbitant drug costs should be a focus. indirectly, increased patient compliance is helped by policies designed to lower the cost of medications or to provide financial assistance. pa ge 33 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 24-34, 2023 comprehensive insurance coverage patients follow their programs more faithfully when they aren’t concerned about paying expensive out-of-pocket costs for required prescriptions, thanks to comprehensive health insurance. patient education and communication empowerment is attained by encouraging collaboration between healthcare professionals and patients, which results in improved condition management through education and communication. pharmacy accessibility by ensuring easy access to pharmacies, we can speed up the processes for picking up new prescriptions and renewing old ones. holistic approach as legislators create regulations about medication adherence, a complete viewpoint is essential. this study draws a strong conclusion by examining the complex relationships between drug cost, insurance, and medication adherence. these five factorscomplete coverage, improved education, crystal-clear communication, and easily accessible pharmacies-create an unbreakable wall of support for greater adherence in the face of pricey medications. these findings shed light on the importance of health and pave the way for healthier, more devoted communities. references adebisi, y. a., nwogu, i. b., alaran, a. j., badmos, a. o., bamgboye, a. o., rufai, b. o., okonji, o. c., malik, m. o., teibo, j. o., & abdalla, s. f. 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(2020). changes in drug list prices and amounts paid by patients and insurers. jama network open, 3(12), e2028510-e2028510. https://doi.org/10.1001/ jamanetworkopen.2020.28510 pa ge 1 pa ge 33 american journal of financial technology and innovation (ajfti) the effect of financial risks on financing decisions of saudi commercial banks a field study on commercial banks operating in arar city aisha badawi abdelrhman musa1*, amna abdelaal khaled ahmed1 volume 2 issue 1, year 2024 issn: 2996-0975 (online) https://doi.org/10.54536/ajfti.v2i1.3193 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: july 14, 2024 accepted: august 18, 2024 published: september 11, 2024 this study aims to analyze the effect of financial risks on the financing decisions of saudi commercial banks in arar city. the study explores the correlation between credit risk, liquidity risk, operational risk, and overall financial risk, as well as how these risks affect financing decisions, based on the survey administered among 50 participants from 3 different banks, which included junior and senior financial analysts, risk managers, and executives. the results show that every type of risk positively correlates with financing decisions. as these risks occur, elevate and become more prominent, banks use more sizable and riskier financing strategies to minimize possible negative impacts on their operations and preserve stability. this indicates that financial risks are interrelated and that robust risk management practices are essential to strategic financial management practices in the banking industry. however, it has some limitations, including the relatively small sample size and the crosssectional study design, which may reduce the generalizability of the findings and limit the possibility of temporal causal inferences. nevertheless, the study makes several contributions to the existing literature in terms of offering information about the relationship between financial risks and financing decisions of saudi commercial banks with significant practical implications for bank managers and policymakers. subsequent research employing increased data samples, longitudinal analysis, or a combination of both quantitative and qualitative studies may yield deeper insight into the interconnections above and strengthen risk management in the banking industry. keywords financial risks, risk management, financing decisions, banking sector, liquidity risk, financial performance introduction the saudi banking sector has shown tremendous change and expansion due to the kingdom’s vision 2030 of achieving economic diversification away from relying on oil (moshashai et al., 2020). this has led to changes in the regulatory environment as the saudi central bank (sama) has implemented reforms, which include initiatives for financial technology (fintech) companies and new banking laws for the first time in over fifty years. these changes promote the digital banking transition process, encouraging people to rely less on traditional methods and more on digital payments and smartphone banking (ramady, 2021). according to abro et al. (2023), the saudi banking sector has been one of the main driving forces of the country’s financial system as it shows sustainable development and relative stability (abro et al., 2023). the recent performance of key indicators like operating income, net interest margin, loans and advances, and deposits has also improved (alnajjar & assous, 2021). according to arslan et al. (2019), commercial banks occupy a crucial position in extending financial services to the other sectors of the economy, which is a major boost to credit markets and economic growth (arslan et al., 2019; farhan et al., 2022). additionally, the concept of corporate governance has been recognized in the saudi banking sector. numerous researchers have examined the impact of corporate governance on bank performance and have concluded that a positive link exists between corporate governance and the banking sector (al matari & mgammal, 2019; almoneef & samontaray, 2019; khanifah et al., 2020). in pursuing this, the capital market authority has put in place various regulations to improve the sector’s corporate governance standards and overall transparency, accountability, and best practices in the industry (lotto, 2018). after thoroughly examining the previously published literature, it was concluded that several topics covered the issues of the saudi banking sector, but very few studies were conducted specifically in arar, a city in the northern part of saudi arabia (abualsauod & othman, 2020; alaagam, 2019; alshebmi et al., 2020; t. alanazi et al., 2020). therefore, further research is needed to identify the specific mechanisms and processes, difficulties, and organizational effectiveness of banking institutions in arar. the results of this regional breakdown help to determine the unique drivers for the banking sector in this region and provide information for the stakeholders and policymakers. the saudi banking sector is characterized as viable, developing, and inclined towards corporate governance (al matari & mgammal, 2019). however, arar’s banking sector is crucial for comprehensively understanding the local banking environment and its implications. thus, financial risks are critical in developing banks’ financing decisions; very few researches explore this aspect in relation to the saudi arabian banking industry (alsahlawi, 2021; hacini 1 northern border university, college of business administration, saudi arabia; po box: 1321 postal code: 91431, arar, saudi arabia * corresponding author’s e-mail: aisha.badawi@nbu.edu.sa pa ge 34 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 et al., 2021). therefore, prior research has emphasized developed markets more, ignoring emerging economies’ distinct economic, regulatory, and operational contexts. according to alali & haddad (2023), the credit, market and operating risks significantly influence the strategic decisions of banks, although their findings are not directly applied in the saudi context because of market maturity, interventional policy, and risk management (alali & haddad, 2023). the development of financing strategies within the banking environment is also significantly affected by its economic activities and market conditions that impart unique economic and financial risks on banks’ operations compared to a more developed city (youssef et al., 2021). moreover, identifying potential sources of financial risks in the middle east and their impact on financing decisions is critical in saudi arabia’s vision 2030 goals of diversifying its economy and increasing the financial sector’s sustainability. therefore, it is important to fill the gap in the existing literature by investigating the influence of financial risks on the economic performance of commercial banks operating in arar. thus, this study aimed to contribute to the body of knowledge that is critical in improving risk management practices and achieving strategic banks’ goals in similar regional contexts by exploring the relationship of financial risk to financial decisions. theoretical framework the following risk management theories in banking contain conceptual frameworks and approaches to identifying, measuring, monitoring, and minimizing various risks associated with banking institutions. modern portfolio theory the markowitz theory, also known as the modern portfolio theory (mpt), is a breakthrough in investment theory as it systematically addresses the issues of portfolio management and investment risk (menjeri, 2018). the key principle of mpt is to diversify the portfolio and optimize its risk-adjusted returns. it states that investors look at the expected returns of individual assets and are concerned about the risk of correlation and variance in the portfolio’s composition. this forms the basis of having high correlation and diversification returns that are achieved by holding assets whose returns are low or negatively correlated whilst allowing investors to earn the same level of return at a lower level of risk or produce a lower level of return at an equivalent level of risk (de jong, 2018). mpt incorporated the notion of the efficient frontier, which illustrates the set of portfolios that provide the highest expected returns for a specific amount of risk or the lowest risk for a particular amount of returns. an efficient portfolio on the efficient frontier offers the best risk/return combination (roychoudhury, 2018). mpt also added the concept of the capital market line (cml), which illustrates efficient portfolios and the relationship between risk and return. the slope of the cml varies with the market risk premium, which represents the extra return that investors require for holding to systematic risk (chang et al., 2020). mpt has revolutionized investment management, and investors and financial institutions have used its four components to help them build their portfolios, determine asset allocations, and measure and manage risk. the mpt enables investors to construct adequately diversified portfolios based on risk tolerance and investment goals to increase long-term returns (shanmuganathan, 2020). capital asset pricing model (capm) the capital asset pricing model or capm is one of the main theories in finance that helps to understand the nature of the correlation between the risk level and the expected rate of return in financial markets (vergarafernández et al., 2023). capm was proposed in 1964 by william sharpe, john linter and jack treynor. capm postulates that the expected return on an investment asset should be positively related to systematic risk measured as a beta. the model assumes that investors demand a risk premium for bearing systematic risk over the riskfree level (mansuri & shah, 2022). the capm model describes the risk premium model, which suggests that the expected return on an asset equals the risk-free rate plus beta times the overall market risk premium (zhang, 2023). however, capm is a simple model with wide application that has been criticized for its assumptions and limitations, such as a risk-free rate and a perfectly efficient market (o’sullivan, 2018). furthermore, capm is a central theoretical tool in the field of finance that helps determine optimal investments and value assets and conduct research on portfolio selection and risk management (dhankar, 2019). value at risk (var) value at risk (var) is a popular quantitative risk measure and a practical tool for estimating the loss exposure of a portfolio or an investment based on a specified time horizon at a desired level of confidence (halkos & tsirivis, 2019). var helps financial institutions understand and manage their market, credit, and operational risks. a simple way to calculate var is through parametric, historical, or monte carlo simulation methods. historical var is based on historical returns; monte carlo var is based on random numbers and sample distributions, and parametric var is based on specific distributions (such as normal distribution) (khindanova & rachev, 2019). var is expressed as several dollars or a number as a percentage of the market value of the portfolio and represented in terms of the length of the period (e.g. one day or one month) and confidence interval (e.g. 95% or 99%) (babazadeh & esfahanipour, 2019). despite its versatility, var also has several drawbacks, including the requirement that it is based on assumptions of normality, the inability to predict the most unlikely events, and the need for knowledge of the timing of possible losses (chen, 2018). nonetheless, var still plays an important role in risk management in financial institutions and is pa ge 35 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 often used jointly with other methods, such as stress testing, to obtain a broader picture of risk exposures in a portfolio of assets (khindanova & rachev, 2019). stress testing stress testing is an important risk-eliminating practice used by banks and other financial institutions to assess the institution’s potential stability in adverse scenarios and to help find weaknesses that jeopardize the economic performance of the institution (goldstein & leitner, 2018). stress testing is different from conventional risk measurement techniques that use historical data and predicted values to assess risks the bank faces; in contrast, stress testing requires a more predictive perspective, the simulation of hypothetical scenarios to evaluate the consequences of severe and unexpected conditions on the bank’s balance sheet and capital position (sakib, 2021). two approaches were used: stress testing and reversed stress testing conditions. stress testing is conducted in the event of adverse scenarios that occur in the economy or market conditions, as well as the failure of operational conditions, whereas reverse stress testing is defined as the reverse analysis of the scenarios that lead to eventualities such as bankruptcy or failure in meeting regulatory capital. the stress testing process is often divided into the following stages: scenario design, data collection and aggregation, modelling and analysis, risk and stress assessment and actions, and reporting and disclosure (gogas et al., 2018). stress tests enable banks to anticipate problems, enhance their risk management procedures, and ensure the safety and soundness of the banking sector (acharya et al., 2018). trade-off-theory the tradeoff theory is a core concept in corporate finance that explains how firms maximize their wealth by selecting the least-cost capital structure primarily through debt and equity (khan et al., 2021). the tradeoff theory postulates that firms try to ensure that the benefits of financial leverage outweigh its costs (ai et al., 2020). the major opportunities of debt financing include tax shields on interest payments since interest is a deductible expense, and debt offers the potential for increasing the equity return through financial leverage (michalkova et al., 2021). debt also provides the advantage of financial flexibility that enables companies to continue operating and making strategic decisions without sharing ownership rights with outside investors. however, debt financing signals firm value and creditworthiness to investors and creditors (deangelo et al., 2018). nevertheless, debt financing also has some costs and risks. these include the repayment of interest and principal, which deplete companies’ cash flow and liquidity, especially during an economic downturn or financial distress. high debt levels increase the risk of financial distress or bankruptcy and raise agency costs (costs associated with the divergence of interest between shareholders and debt holders), increase the cost of debt and limit access to debt markets. secondly, the tax benefits of debt decrease as the firms improve their level of debt, causing the optimal capital structure to vary with the corporate tax rate and the leverage ratio (songhor, 2018). the tradeoff theory argues that each company strives to find the optimal capital structure by balancing the npv of tax shields and the costs of financial distress. this optimal capital structure differs from firm based on industry, future growth opportunities, cash flow stability, risk preference, and economic environment. debt financing involves weighing the debt’s advantages and disadvantages to increase the shareholders’ value and improve long-term profitability (nicodano & regis, 2019). empirical studies international banking regulations and systemic risk literature are usually set globally. several studies demonstrated how increased capital and liquidity levels impacted banks’ risk attitudes and capital structure (erülgen et al., 2020; ghosh & chatterjee, 2018; siddika & haron, 2020). according to alexander (2015), higher capital under basel iii requirements increases banking sector resilience and stability but also leads to declining lending activities and profitability because of the higher cost of holding higher capital (alexander, 2015). in addition, hossain et al. (2018) pointed out the international transmission channels of systemic financial shocks from the experience of banking systems worldwide. this study emphasizes the global governance approach in the regulation of financial markets to mitigate sri and prevent financial contamination (hossain et al., 2018). regional studies offer some unique information about the particular difficulties and peculiarities of banking sectors in various regions. gropp and heider (2010) examine the factors that affect the capital structure of european banks and identify regulatory pressures and market discipline as two key factors that influence european capital structure. they observed that banks in countries with strict legal regimes usually have higher car ratios to meet regulatory requirements and to mitigate the risks of getting penalized for violations (gropp and heider., 2010). lee and hsieh (2013) examined the causal links between economic conditions and monetary policy and bank performance and risk-taking in asia–pacific. they found that banks’ risk management strategies and financing choices in emerging asian economies were often shaped by factors such as rapid economic growth and regulatory change. they concluded that high economic and regulatory instability usually prompted banks to manage credit risk by taking a more cautious approach to credit funding (lee and hsieh, 2013). de jonghe, dewachter, and ongena (2020) explained how changes in the economy and regulations affect the capital structure of banks. they discovered that banks have been observed to raise capital buffers during economic instability and heightened regulatory pressure. yet, banks face a tradeoff between the benefits of debt financing and the costs of financial distress and regulatory sanctions pa ge 36 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 (de jonghe et al., 2020). naughton and veeramani (2020) explored how big data and technological solutions reshape risk management practices. also, they concluded that banks using the most sophisticated analytics and machine learning technology have better tools to recognize and manage risk, thus making those banks more stable and less likely to fail (naughton and veeramani, 2020). uch and goldberg (2020) examined the impact of post-crisis regulatory reforms on global banks’ capital requirements and risks. they also concluded that although banking regulation has strengthened banks’ stability, it limits their opportunities for risk-taking and high-risk/high-return transactions, affecting banks’ performance and their strategic choices (uch and goldberg., 2020). a recent study by crouhy, jarrow, and turnbull (2018) focused on the systemic risk hazards due to interconnected banking networks. they claimed that although interdependence enhances risk diversification and operating efficiency, it amplifies the risk of contagion and, therefore, requires effective risk governance and supervisory processes to mitigate the possibility of banking panics or systemic distress in the banking sector (crouhy et al., 2018). challenges in the banking sector of arar arar saudi banks are exposed to several challenges that the area’s economic performance has influenced, the policies applied to protect the banking sector, and the developments in banking technology. recent literature identifies these issues and focuses on the effective role of economic diversification efforts after the 2030 vision of the kingdom of saudi arabia, which targets reducing economic dependency on oil and boosting other sectors (khan & khan, 2019). it is difficult for banks in saudi arabia to implement stricter operation standards on regulatory compliance and basel iii stipulated by sama while having to strengthen their capital adequacy and risk management activities (al-hassan, khamis, & oulidi, 2020). diversification also requires implementing changed technology in the type and form of the new avenues of business in terms of digital banking and strengthening the security of transactions, which benefits from significant investment in new systems and infrastructure and, in a way, leads to the financial and human resource strain (almazari 2018). besides, banks in arar must face more competition from national and international banks to gain market share; thus, they need to distinguish their services in a smaller and less dynamic economy than the regional economy overall (aldeen, 2020). the need to support developing local economies and financial inclusiveness of certain population segments further adds to the complexity of the overall operating conditions as these companies have to launch products and services that respond to the particular needs of a specific local population segment, including underbanked ones (alkhatib, 2018). these interrelated issues highlight the dynamics of banking in arar and the need for risk assessment and strategic planning to be multifaceted. hypotheses development h1 there is a significant positive relationship between credit risk and financing decisions in saudi commercial banks. h2 there is a significant positive relationship between liquidity risk and financing decisions in saudi commercial banks. h3 there is a significant positive relationship between operational risk and financing decisions in saudi commercial banks. h4 there is a significant positive relationship between financial risk and financing decisions in saudi commercial banks. methodology figure 1: conceptual framework research design this study uses quantitative methods to address the impact of financial risks on the financing decisions of saudi commercial banks with a focus on arar city. this approach analyses quantitative patterns and factors that affect these decisions. in this study, the researcher analyzes how different types of risks impact the performance of saudi commercial bank, specifically in arar. the study also investigates and highlights the impact of saudi commercial banks’ performance on their financing decisions. a descriptive method is used for the theoretical research, which comprises risk management and financial decision-making theories. meanwhile, an analytical approach was used to analyze the data collected through pa ge 37 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 survey methods from different professionals from 3 banks in arar. sampling technique this study employed a purposive sampling technique in which participants were directly involved in financial decision-making and risk management. fifty respondents from different commercial banks in arar were selected to achieve a representative analysis. the reliability of the sample size was tested through cron bach alpha, and a pilot testing was conducted to identify the weaknesses and strengths of the structured questionnaire and the placement of different items. data collection method a structured survey questionnaire was designed to collect and administer key personnel, including risk managers, financial analysts, and senior executives, from commercial banks operating in arar. survey instrument the survey, designed to collect data from different professionals of commercial banks in arar, consisted of likert-scale questions to analyze the perception of various types of financial risks (credit risk, liquidity risk, operational risk) and their influence on financing decisions. some questions aimed at addressing efficiency issues in current risk management strategies and techniques banks adopt. thus, factor analysis was conducted to confirm that the survey items accurately measure the underlying constructs of financial risks and financing decisions. data analysis the data collected through the survey questionnaire was analyzed through spss, and the following mean, median, and standard deviation were used to summarize the survey responses. also, regression analysis was conducted to examine the relationship between different types of financial risks and financing decisions. this helps to identify which risks have the most significant impact on decision-making. results & analysis the following results were generated after analyzing the data collected through the questionnaire from 50 participants working in three banks in arar. table 1: demographic analysis job title frequency percent valid percent cumulative percent valid financial analyst 27 54.0 54.0 54.0 risk manager 7 14.0 14.0 68.0 senior executive 16 32.0 32.0 100.0 total 50 100.0 100.0 table 2: years of experience in banking years of experience in banking frequency percent valid percent cumulative percent valid 11-15 years 11 22.0 22.0 22.0 5-10 years 14 28.0 28.0 50.0 less than 5 years 18 36.0 36.0 86.0 more than 15 years 7 14.0 14.0 100.0 total 50 100.0 100.0 the study analyzes the effect of financial risks on the financing decisions of saudi commercial banks, with particular attention to the ones operating in arar city. the dataset includes 50 respondents categorized by their job titles: financial analysts, risk managers, and senior executives. financial analysts were the highest in the number of the banks, with 54% of the sample, which shows that these banks have a fairly strong analytical culture. senior executives involved in decision-making also make up 32% of the respondents, while 14% of the respondents were risk managers who were specifically involved in calculating and managing financial risks. this composition indicates that most of the knowledge on financial risks and their impact on financing decisions is obtained through analytical investigations with meaningful contributions from senior executives and professionals in the field of risk management. this enables the analysis to focus on the distribution of these roles and make inferences about the diverse perspectives and ideas that were shaping the financial strategies in the commercial banking sector in arar city. pa ge 38 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 the dataset describes the years of experience in banking for 50 respondents. the largest group has less than 5 years of experience – 36%, which implies that many young professionals are actively working in the industry. employees with 5-10 years of experience account for 28%, while those with 11-15 years account for 22%. the smallest group is those with more than 15 years of experience, accounting for 14%. these cumulative percentages reveal that individuals with up to 15 years of work experience cover 86% of the sample, with the remaining 14% having more than 15 years of work experience. this distribution reflects a working population with a varied experience level and a strong concentration on those still relatively new to the banking industry. table 3: type of bank type of bank frequency percent valid percent cumulative percent valid international bank 14 28.0 28.0 28.0 national bank 19 38.0 38.0 66.0 regional bank 17 34.0 34.0 100.0 total 50 100.0 100.0 table 4: impact of credit risk on financing decisions coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) -1.273 .212 -6.002 .000 credit risk 1.133 .050 .956 22.711 .000 a. dependent variable: financing decision table 5: impact of liquidity risk on financing decision coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) -.740 .160 -4.622 .000 liquidity risk 1.037 .038 .970 27.510 .000 a. dependent variable: financing decision the dataset consists of 50 respondents working in three different types of banks in arar city. these include 38% who work for national banks, thus making this group the largest, demonstrating the prevalence of domestic banks in the region. regional banks were the second segment, accounting for 34% of the participants, implying that most participants work in the banking sector, which is in a specific area or region. banks located globally or in many countries were represented by 28% of the participants, accounting for international banks. the cumulative percentages show that the sample comprises 66% of the national and international banks, while 34% are from regional banks. this distribution also shows a diverse banking workforce in the banking sector of arar city, including individuals working in national, regional and international banks. in table 4, regression analysis examines the impact of credit risk on the financing decisions of banks in arar city. the model includes a constant term of -1.273, which, though statistically significant (t = -6.002, p = 0.000), primarily serves as a baseline reference point. the unstandardized coefficient for credit risk was 1.133, which indicates that each unit increase in credit risk corresponds to a 1.133-unit rise in the financing decision. the standardized coefficient (beta) of 0.956 demonstrates a strong positive relationship between credit risk and financing decisions. this relationship is highly statistically significant, evidenced by a t-value of 22.711 and a p-value of 0.000. overall, the analysis shows that higher credit risk significantly and positively influences the financing decisions of banks, leading them to make more substantial or aggressive financial decisions. the regression analysis investigates the effect of liquidity risk on financial decisions in banks. the model’s constant term is -0.740, with a standard error of 0.160, and is statistically significant with a t-value of -4.622 and a p-value of 0.000. this negative intercept suggests that if liquidity risk were zero, the financial pa ge 39 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 is highly statistically significant, as evidenced by a t-value of 27.510 and a p-value of 0.000, implying the likelihood of this result occurring by chance is virtually zero. the results demonstrate a significant and robust positive correlation between liquidity risk and financial decisions. as liquidity risk increases, banks tend to make more substantial financial decisions, reflecting a strong influence of liquidity risk on their financial strategies. decision score would start at -0.740, although the intercept holds substantial practical meaning alone. the unstandardized coefficient for liquidity risk was 1.037, which indicates that for each unit increase in liquidity risk, the financial decision increases by 1.037 units. the standardized coefficient (beta) was 0.970, showing a strong positive relationship between liquidity risk and financial decisions. this relationship table 6: impact of operational risk on financing decisions coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) .853 .107 7.973 .000 operational risk .836 .026 .977 31.839 .000 a. dependent variable: financing decision table 7: correlation between credit risk, liquidity risk and operational risk correlations credit_risk liquidity_risk operational_risk credit_risk pearson correlation 1 .960** .963** sig. (2-tailed) .000 .000 n 50 50 50 liquidity_risk pearson correlation .960** 1 .973** sig. (2-tailed) .000 .000 n 50 50 50 operational_risk pearson correlation .963** .973** 1 sig. (2-tailed) .000 .000 n 50 50 50 **. correlation is significant at the 0.01 level (2-tailed). the above table-6 regression analysis examines the relationship between operational risk and financing decisions in banks. the model’s constant term is 0.853, with a standard error of 0.107, and is statistically significant with a t-value of 7.973 and a p-value of 0.000. this positive intercept suggests that when operational risk was zero, the baseline level of the financing decision was 0.853. the unstandardized coefficient for operational risk was 0.836, which indicates that each unit increase in operational risk corresponds to a 0.836-unit rise in the financing decision. the standardized coefficient (beta) was 0.977, demonstrating a strong positive relationship between operational risk and financing decisions. this relationship was highly statistically significant, as shown by the t-value of 31.839 and a p-value of 0.000, implying that the probability of this result occurring by chance is virtually zero. overall, the results reveal a significant and robust positive correlation between operational risk and financing decisions. as operational risk increases, banks tend to make more aggressive or substantial financing decisions, highlighting operational risk’s strong influence on their financial strategies. in table 7, the correlation analysis reveals strong and statistically significant positive relationships among banks’ credit risk, liquidity risk, and operational risk. the pearson correlation coefficient between credit risk and liquidity risk was 0.960, which indicates a very strong positive correlation, with a p-value of 0.000, confirming its significance. similarly, the correlation between credit and operational risks was 0.963, which is also highly significant with a p-value of 0.000. the strongest correlation was between liquidity risk and operational risk, with a pearson coefficient of 0.973 and a p-value of 0.000. these findings suggest that as one type of risk increases, the other risks also tend to increase, demonstrating the interconnected nature of these financial risks in the banking sector. this implies that banks experiencing high levels of one risk are likely dealing with elevated levels of the other risks, highlighting the need for comprehensive risk management strategies. pa ge 40 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 in table -8, the regression analysis examines the impact of financial risk on banks’ financing decisions. the constant term was 0.986, with a standard error of 0.098, and was statistically significant with a t-value of 10.049 and a p-value of 0.000. this indicates that when financial risk was zero, the baseline level of the financing decision was 0.986. the unstandardized coefficient for financial risk was 0.868, which suggests that for each unit increase in financial risk, the financing decision increases by 0.868 units. the standardized coefficient (beta) was 0.979, demonstrating a strong positive relationship between financial risk and financing decisions. this relationship was highly statistically significant, as evidenced by the t-value of 33.339 and a p-value of 0.000, implying that the likelihood of this result occurring by chance is virtually nonexistent. the results indicate a significant and robust positive correlation between financial risk and financing decisions. as financial risk increases, banks tend to make more aggressive or substantial financing decisions, highlighting the strong influence of financial risk on their financial strategies. discussion the study aimed to assess the effects of different types of financial risks on the financing decisions of saudi commercial banks, especially those in arar city. the developed hypotheses stated that credit risk, liquidity risk, operational risk, and total financial risk positively correlate with financing decisions. based on survey data from 50 participants of different organizational positions within banks, these relationships were established. hypothesis 1: credit risk and financing decisions the regression analysis confirmed a significant positive relationship between credit risk and financing decisions (beta = 0.956, p = 0.000). this is consistent with h1, which posits that financing decisions become more significant among the banks as credit risk increases. this relationship indicates that high credit risk negatively affects arar banks’ financial performance; however, the banks employ some financial mechanisms to address this problem. such adjustments involve a higher demand for cash or credit standards or striving for a higher yield, which can only partially offset the higher risk. this finding supports the literature arguing that to protect their stability and profitability; banks are willing to go the extra mile to mitigate increased credit risk. hypothesis 2: liquidity risk and financing decisions the study found a strong positive correlation between liquidity risk and financing decisions (beta = 0.970, p = 0.000), supporting h2. this result suggests that the greater the level of liquidity risk, the more excessive the financial decisions made by banks due to the need to maintain sufficient liquidity and deal with a potential liquidity gap. this relationship is consistent with prior studies done on the implications of liquidity management in ensuring the solvency and business viability of banking institutions, especially during periods of economic instability. hypothesis 3: operational risk and financing decisions the regression analysis also revealed a significant positive relationship between operational risk and financing decisions (beta = 0.977, p = 0.000), confirming h3. due to the higher level of operational risk, more significant financing decisions are likely to occur to prevent operational disruption and sustain normal operations in the banking industry. this concurs with the work that holds crucial values in managing operational risks to avoid large losses and ensure the integrity of banking services. hypothesis 4: financial risk and financing decisions finally, the analysis demonstrated a significant positive relationship between overall financial risk and financing decisions (beta = 0.979, p = 0.000), validating h4. this implies that with higher cumulative financial risks, banks are more likely to engage in aggressive financing techniques to negotiate the risk landscape proficiently. this approach towards risk management is in line with the research findings of the current literature, where the authors have emphasized the interrelatedness of financial risks and their combined effects on strategic financial decision-making. these results align with global and regional research from 2018 onwards, revealing that financial risks substantially affect banks’ financing activities. for instance, credit risk research shows that banks’ credit policies become tight in the face of higher credit risk, whereas if a bank is exposed to liquidity risk, the concern is shifted more towards having sufficient liquidity cushions. likewise, operational risk has been identified as a way for banks to improve risk management to support operations and reduce loss. table 8: impact of financial risk on financing decision coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) .986 .098 10.049 .000 financial risk .868 .026 .979 33.339 .000 a. dependent variable: financing decision pa ge 41 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 33-43, 2024 conclusion all the financial risks, such as credit risk, liquidity risk, operational risk and other general financial risks, have a remarkable impact on the financing activities of saudi commercial banks in arar city. research showed that as these risks rise, the banking industry provides more significant funding to offset dangers and secure steadiness. this observation is supported by current global and regional research, where the risk management framework is highlighted as a determinant factor in the strategic control of all financial decisions. positive correlations between these risks and financing decisions show that the examined financial risks are interconnected in the banking sector. as a result, banks require advanced and elaborate risk management strategies adopted that can help them sustain and grow through difficult financial conditions. this research also emphasizes regular risk assessment and proper risk mitigation measures to help banks protect their financial positions and ensure their ability to operate in volatile economic climates. recommendations • risk management continues to play a crucial role in enhancing the efficiency of credit and operational risk management in financing decisions. therefore, instituting frameworks for risk management solutions that address credit, liquidity, and operational risks is imperative. • appropriate risk identification and assessment, supported by advanced tools and technologies, allow for effective identification and timely response to potential financial risks. • stress testing enables the banks to assess their vulnerability amidst shocks and adjust their financing frameworks as necessary. • continuous training and development of financial analysts, risk managers, and senior executives will improve their understanding of existing risk management solutions. • firms should pay significant attention to internal controls and audits to develop better mechanisms for addressing existing and emerging operational risks. • risk reporting and communication should be enhanced within the bank to ensure all concerned parties are aware of the risks and measures taken to address them. • risk diversification means distributing risks so that they have minimal effect on the bank’s financial health. limitations there are several limitations in the study that are worth noting. one limitation is that the current study recruited only 50 participants, thus limiting the generalization of the results to all the commercial banks in arar city. however, the questionnaire responses might have self-report bias, which may have affected data collection accuracy. the cross-sectional approach also restricts the likelihood of establishing cause-and-effect relationships between the financial risks and financing every fiscal period. in addition, the cross-sectional approach and the focus on a single quantitative technique raise the possibility of missing the qualitative aspect. these limitations imply that the research findings should be interpreted with great care and indicate possibilities of further research to close the gaps effectively. references abro, a. a., alam, n., murshed, m., mahmood, h., musah, m., & rahman, a. a. 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(2023). certainty equivalent, risk premium and asset pricing. in fundamental problems and solutions in finance (pp. 151-192). springer. pa ge 1 pa ge 1 american journal of financial technology and innovation (ajfti) review of recent research directions and practical implementation of low-frequency algorithmic trading talal al-sulaiman1* volume 2 issue 1, year 2024 issn: 2996-0975 (online) https://doi.org/10.54536/ajfti.v2i1.2354 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: january 22, 2024 accepted: february 24, 2024 published: february 26, 2024 financial trading has undergone substantial technological evolution, with automation taking center stage, leading to approximately 80% of us market trades being executed by computer systems, predominantly by large financial institutions. the rise of algorithmic trading, poised to engage smaller entities, international markets, and individual traders, drives this article’s exploration of research in this field. providing a comprehensive overview, it outlines the evolution of trading practices and defines algorithmic trading as a computer-powered tool aiding investment decisions. the article details the steps involved in algorithmic trading, covering opportunity identification, quantitative research, implementation, testing phases, and continuous monitoring. it also examines prevalent programming languages and opensource platforms facilitating algorithm development. focusing on trading frequencies across financial instruments, it delves into high-frequency trading as a subset, alongside methodologies like technical and fundamental analysis, time series analysis, option trading strategies, and machine learning techniques used in algorithm creation. categorized by trading frequencies, analytical approaches, involved financial instruments, and analysis objectives, the reviewed papers contribute insights into algorithmic trading’s diverse landscape and methodologies, offering valuable perspectives for industry participants and researchers alike. keywords financial trading, algorithms, low frequency, practical implementation, technology introduction as in most life aspects, technology has tremendously advanced financial systems. it includes many financial systems such as credit business, real estate, insurance, and financial markets. this advancement motivates more quantitative financial mathematics, financial engineering, actuarial science, and risk management. this paper focuses on the evolution of trading in financial markets. overseas business growth during the industrial revolution at the beginning of the 17th inspired joint-stock companies and the dutch east india co. to issue the first paper shares. the paper shares make it very convenient to transfer the stocks’ ownership, increasing the issue of paper shares rapidly. the place where the buyers and sellers gathered to trade the paper shares is called the stock exchange, and the first established exchange was the amsterdam stock exchange (braudel & reynolds, 1983). the worldwide exchanges continued until the 90s of the previous century when it shifted to electronic trading (johnson, 2014). the shift quickly increases the trading volume. however, with the increase of computational power and cloud service availability, the middle of the first decade of this century promoted computers to perform trading on behalf of individuals. it allows for automated trading to be achieved through a finite sequence of steps algorithms. as of 2020, 80% of the trading volume is effectuated through algorithms, and most hedge funds use algorithms to set up their trading strategies. h. simon (simon, 1955) prevents declare the bounded rationality the human from making rational decisions due to human emotions, the mind’s cognitive limitations, and time availability. algorithmic trading (at) allows for reducing the limitation on rationality. algorithmic tradings have advantages over discretionary trading by removing emotions and coming up with consistent decisions. in addition, it can monitor the market all the time and implement back testing to ensure the strategy’s effectiveness. however, the algorithmic trading results depend on the quality of the developed model and its ability to capture the right signals. in other words, the algorithmic is superior to discretionary trading only if the algorithm itself besteads the discretionary traders. however, the main advantage of at, according to johnson (simon, 1955), are its ability to minimize the effect of emotions, back testing, maintain discipline and consistency, improve placing order speed, and diversification. however, he stated that the main disadvantages of at centers with the possibility of expense increase and machine failure. the area of algorithmic trading requires multidisciplinary knowledge and skills in finance, mathematics, engineering, and programming. algorithmic trading is usually executed through automated trading, robot trading, and black box (kissell, 2013). the paper proceeds as follows: in section 2, we defined and characterized algorithmic trading. section 3 surveys the programming languages and platforms to research algorithmic trading. section 4 demonstrates the domains of algorithmic trading. section 5 shows the matrix of performance measures for backtesting. section 6 explores the main methods used to develop an algorithm for trading. section 7 survey-related search on various domains. finally, section? provides the conclusion remarks. 1 audi real estate refinancing company and engineering management department, prince sultan university, riyadh, saudi arabia * corresponding author’s e-mail: talalsulaiman510@outlook.com pa ge 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 literature review the definition of algorithmic trading (at) and highfrequency trading (hft) varies among researchers. jarnecic et al. (jarnecic & snape, 2010) define at as computer algorithms executing predetermined trading decisions to minimize price impact. domowitz (domowitz & yegerman, 2005) characterizes it as automated equity order execution via direct marketaccess channels. hendershott et al. (hendershott et al., 2011) describe at as using algorithms for automatic trading decisions, order submissions, and management. hft, a primary type of at, relies on speed for profits. jarnecic et al. (jarnecic & snape, 2010) define hft as high-speed algorithms generating and executing trades for capital returns. cvitani et al. (cvitanic & kirilenko, 2010) define it as rapid, automated programs creating, directing, and executing orders in electronic markets, engaging in substantial order submissions and cancellations. gomber et al. (gomber & haferkorn, 2015) highlight typical at and hft characteristics involving pre-designated decisions, live market data observation, and automated order submission and management. however, hft differs with numerous orders and cancellations, profiting as a middleman and holding assets briefly. the development of hft is chiefly by financial institutions, emphasizing algorithmic trading’s researcher development and implementation for retail investors. choosing between buying or building trading software presents trade-offs. johnson (johnson, 2020) notes that buying existing software offers easy implementation and customization but can be costly and potentially contain loopholes. building software, although time-consuming, offers control and customization. numerous references like “learn algorithmic trading” (donadio & ghosh, 2019), “hands-on machine learning for algorithmic trading” (jansen, 2018), “trading evolved” (clenow, 2019), and “algorithmic trading” (johnson, 2020) provide valuable hands-on experience in developing trading systems. open-source trading platforms like quantopian, quant-connect, and quant-insti provide cloud-based services for algorithm development, backtesting, and live trading (cohan; quantconnec profile, 2011; oberoi). algorithmic strategies’ domains are crucial, as strategies may perform differently based on financial instrument types or trading environments. derivatives like forwards, futures, swaps, and options can impact trading strategy effectiveness (hull, 2003). back-testing using historical data is vital to evaluate algorithm performance. various performance measures such as portfolio diversification, concentration, and risk-return ratios (markowitz, 1952) help assess algorithm reliability and effectiveness. materials and methods financial markets employ technical analysis, a tool reliant on historical prices to predict market patterns and facilitate trading decisions. originating from charles dow’s dow theory in 1900 (achelis), it focuses on interpreting price movements through charts. the analysis mainly revolves around momentum and mean reversion strategies. momentum strategies advocate following existing trends, assuming their continuation, whereas mean reversion anticipates securities returning to their average prices. various technical indicators, such as moving averages (ma), exponential moving averages (ema), and bollinger bands (bb), aid in analyzing market trends. for example, the double exponential moving average (dema) utilizes two emas for mean reversion signals, while bollinger bands offer confidence intervals depicting potential overbought or oversold situations for both strategies. however, technical analysis lacks adaptiveness and learning capabilities. integrating modern algorithms like machine learning enhances pattern detection and prediction capabilities. contrarily, fundamental analysis estimates assets’ intrinsic value based on financial statements and economic factors, evading the efficient market hypothesis (fama, 1970). techniques include financial ratios, discounted dividends, or free cash flow models (graham & dodd, 2008). financial time series analysis evaluates and predicts security prices over time, including linear (ar, ma, arma, arima) and nonlinear models (threshold ar, markov switching ar). volatility prediction models like arch and garch forecast variations in asset returns due to changing volatility. moreover, computational mathematics, involving ai, machine learning, and data mining, supports these analyses (overby, 2011; mcmillan, 2002; kastenholz, 2019). decision trees (e.g., cart, c4.5) merge fundamental analysis with decision-making, offering actionable rules for stock actions (rokach, 2014; larose & larose, 2014). these methods complement each other, enhancing market understanding and trading strategies (box et al., 2011; tsay, 2005; tsay, 2013). furthermore, the evolution of algorithmic trading is significantly influenced by advancements in neural networks, particularly long short-term memory (lstm) networks introduced by hochreiter and schmidhuber (hochreiter & schmidhuber, 1997). lstm, a form of recurrent neural network (rnn), features context neurons representing short-term memory dynamically updating during a time sequence. differing from feedforward neural networks, rnns transmit output from context nodes back to hidden layers, involving input and forget gates, and output gate mechanisms. weight optimization in lstm employs backward training methods like back-propagation, resilient-propagation, and genetic algorithms. reinforcement learning, another ai branch, aims to maximize reward through iterative actions based on observed states, as described by q learning. studies explore ai-driven trading strategies, from pair-switching approaches (maewal & bock, 2011) and technical indicator-based decision support systems (dash & dash, 2016; henrique et al., 2018) to employing machine learning like deep learning neural networks (gao & chai, 2018; yu & yan, 2020) and sentiment analysis (bernile & lyandres, 2011; putra & kosala, 2011). various pa ge 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 other strategies underline the diversity and complexity of algorithmic trading models, ranging from leveraging historical market data to predicting stock trends and exploiting market anomalies (cohen et al., 2010; gerlein et al., 2016; kishore et al., 2008; nair et al., 2010). results and discussion on stock options weekly or based on options covered by the underlying assets of stocks with monthly data. table 1 shows the most common domains of algorithmic strategies. table 1: common domains of algorithmic strategies underlying asset derivatives trading frequency equity forward fractions of second commodity futures seconds bonds options minutes foreign currency swap days reit etf weeks cryptocurrencies mutual funds months years trend indicators attempt to detect a trend in the prices of the assets. calculating the moving average is a common procedure to identify the up or down trends by smoothing the prices. the momentum see section 6.2 indicators estimate the speed in the changes of prices in a given time-space. the volatility indicators focus on the trading activities, possible range, and security risk. finally, the volume indicators measure the attraction of financial assets. table 2 shows a comprehensive classification of the technical indicators. table 2: comprehensive classification of technical indicators trend indicators accumulative swing index (asi) andrews pitchfork aroon detrended price oscillator directional movement double exponential moving average dow theeory elliott wave theory exponential moving average (ema) fourier transform gann angles inertia linear regression indicator mass index mesa sine wave moving average convergence divergence (macd) parabolic stop and reverse (parabolic sar) simple moving average (sma) triangular moving average (tma) variable moving average (vma) weighted moving average (wma) price channel qstick raff regression channel speed resistance lines swing index triple exponential moving average (tema) trend lines vertical horizontal filter (vhf) wilder’s smoothing momentum indicators absolute breadth index (abi) accumulation/ distribution line advance/decline ratios advancing declining issues advancing, declining, unchanged volume bradth thrust chande momentum oscillator commodity channel index (cci) commodity channel index commodity selection index dynamic momountom index ease of movement forecast oscillator (fo) intaday momountom index mcclellan oscillator mcclellan summation index member short ratio momentum money flow index new highs-lows cumulative new highs-lows ratio price oscillator price-rate-ofchange (roc) projection oscillator pa ge 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 relative momountom index relative strength index (rsi) stochastic momentum index stochastic oscillator (so) trix williams accumulation / distribution williams %r volatility indicators average true range bollinger bands envelopes (trading bands) fibonacci standard deviation trin arms index open-10 trin relative volatility index standard deviation standard deviation channel standard error bands standard error channel ultimate oscillator volatility chaikin’s volume indicators market facilitation index negative volume index on balance volume positive volume index price and volume trend stix trade volume index upside/downside ratio upside/downside volume volume volume oscillator (vo) volume rate of change volume adjusted moving average (vama) other japanese candlestick kagi large block ratio odd lot balance index odd lot short ratio odd proability cones overbought/ oversold public short ratio put/call ratio random walk index renko spreads three line break time series forecast tirone levels total short ratio typical price weighted close zig zag a single firm’s outputs should be compared to other issues of a similar type, such as the average of firms in the same sector or the average of leading firms in a similar business. furthermore, the analyst should not look to a single period to assess the firm’s quality but instead see the ratios trending over multiple periods. for example, figure 5 shows the return on equity of aapl and msft from 2005 to 2020. as we can see from the figure, both companies were exposed to a drop in the roe between 2010 and 2013. however, starting in 2017, we can see the roe trending upward. table 3: financial ratios liquidity ratios current higher values are preferred current assets/current liabilities quick higher values are preferred current assets−inventories/current liabilities inventory turnover higher values are preferred sales/inventories dso lower values are preferred receivable/daily sales fixed assets turnover higher values are preferred sales/net fixed assets total assets turnover higher values are preferred sales/total assets debt management ratios debt ratio lower values are preferred total liabilities/total assets tie lower values are preferred ebit/interest charges ebitda coverage lower values are preferred ebitda+lp/interest+pp +lp profitability ratios profit margin on sales higher values are proffered nis/sales bep higher values are preferred ebit/total assets roa higher values are preferred nis/total assets pa ge 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 roe higher values are preferred nis/common equity market value ratios p/e high value indicates overpricing price per share/earning per share price/cash flow high value indicates overpricing price per share/cashflowpershare market/book high value implies high expectations market price per share/book value per share they analyze the algorithms with k = 10 before and after transaction costs on the stocks of the s&p 500 for the period from dec 1989 to oct 2015 using various performance measures. table 4 shows the comparative results after transaction costs obtained by (krauss et al., 2017). in a similar design, fisher and krauss (fischer & krauss, 2018) utilized the lstm type of deep learning networks to compare the results of lstm with a set of benchmarked memoryless models such as rf, deep neural network (dnn), and logistic regression (log). table 5 shows the comparative results after transaction costs obtained by (fischer & krauss, 2018), where lstm performs superior to the benchmarks models. table 4: comparison of results obtained by krauss et al. (krauss et al., 2017) before transaction costs after transaction costs dnn gbt raf ens dnn gbt raf ens daily mean return (long) 0.0033 0.0037 0.0043 0.0045 0.0013 0.0017 0.0023 0.0025 daily mean return (short) -0.0011 -0.0013 -0.0013 -0.0015 -0.0001 -0.0003 -0.0003 -0.0005 daily mean return 0.0022 0.0025 0.003 0.0029 0.0012 0.0015 0.002 0.0019 standard dev. 0.0269 0.0217 0.0208 0.0239 0.0269 0.0217 0.0208 0.0239 table 5: comparison of results obtained by fisher and krauss (fischer & krauss, 2018) before transaction costs after transaction costs lstm raf dnn log lstm raf dnn log daily mean return (long) 0.0029 0.003 0.0022 0.0021 0.0019 0.002 0.0012 0.0011 daily mean return (short) 0.0017 0.0012 0.001 0.0005 0.0007 0.0002 0.0 -0.0005 daily mean return 0.0046 0.0043 0.0032 0.0026 0.0007 0.0002 0.0012 -0.0005 standard dev. 0.0209 0.0215 0.0262 0.0269 0.0209 0.0215 0.0262 0.0269 max. drawdown on daily basis 0.466 0.3187 0.5594 0.5595 0.5233 0.7334 0.9162 0.9884 annualized mean return 2.0127 1.7749 1.061 0.7721 0.8229 0.6787 0.246 0.0711 annulaized sharpe ratio 10.0224 9.5594 4.2029 2.9614 2.3365 1.8657 0.5159 0.1024 the value strategy fundamentally values the currency, long undervalued, and short overvalued, assuming they will revert to their fundamental value. in addition, they developed a compounded strategy that composites the three strategies as a single trading strategy. the portfolio is balanced monthly, and the results are compared to a benchmark of global bonds and stocks. the results exhibit a superior performance of the composite strategy, as shown in table 6. the model of fernandez et al. (fernandez-perez et al., 2018) takes advantage of the skewness anomaly in the commodities’ future returns. the algorithm has a long commodity future of negative skew and a short commodity future of positive skew. zaremba et al. (zaremba et al., 2019) analyze 15 commodity factors from 1986 to 2017 to find if the momentum effect exists. the results confirm the assumption that buying a commodity with the highest past returns or selling a commodity with the lowest past returns supplement a significant profit. table 6: comparison of results obtained by kroencke (kroencke et al., 2014) mean returns standard deviation sharpe ratio global bonds 4.21 5.38 0.78 global stocks 5.81 14.31 0.41 fx carry trade 6.18 7.5 0.82 fx momentum 5.34 7.68 0.7 fx value 4.18 6.69 0.62 fx composite 8.23 7.22 1.14 pa ge 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 table 7: research considerations objective prediction (dash & dash, 2016; henrique et al., 2018; gao & chai, 2018; yu & yan, 2020; alsulaiman, 2022; nair et al., 2010; chen & hao, 2017; weng et al., 2018; lee et al., 2019; madan et al., 2015; deng et al., 2016; almahdi & yang, 2017; al-sulaiman, 2022; jang & lee, 2017; mcnally et al., 2018; alessandretti et al., 2018; colianni et al., 2015; alsulaiman & al-matouq, 2021; de almeida et al., 2018) profit from stylized anomaly (maewal & bock, 2011; krauss et al., 2017; fischer & krauss, 2018; gatev et al., 2006; chen et al., 2019; rad et al., 2016; bernile & lyandres, 2011; dimic et al., 2018; geyerklingeberg et al., 2018; berkowitz & depken, 2018; lev & nissim, 2006; cohen et al., 2010; frazzini & pedersen, 2014; kishore et al., 2008; liu et al., 2003; sadka, 2006; garfinkel & sokobin, 2006; chordia & shivakumar, 2006; frazzini & lamont, 2007; faber, 2007; faber, 2010; lisauskas, 2011; maze, 2012; kroencke et al., 2014; cenedese et al., 2012; barroso & santa-clara, 2015; baker & haugen, 2012; fernandez-perez et al., 2018; zaremba et al., 2019) financial instruments stocks maewal & bock, 2011; dash & dash, 2016; henrique et al., 2018; deng et al., 2016; deng et al., 2016; gao & chai, 2018; gatev et al., 2006; chen et al., 2019; rad et al., 2016; yu & yan, 2020; bernile & lyandres, 2011; dimic et al., 2018; berkowitz & depken, 2018; geyer-klingeberg et al., 2018; lev & nissim, 2006; al-sulaiman, 2022; cohen et al., 2010; frazzini & pedersen, 2014; kishore et al., 2008; liu et al., 2003; sadka, 2006; garfinkel & sokobin, 2006; chordia & shivakumar, 2006; nair et al., 2010; chen & hao, 2017; weng et al., 2018; lee et al., 2019; frazzini & lamont, 2007; faber, 2007; lisauskas, 2011; al-sulaiman & al-matouq, 2021) bonds (maewal & bock, 2011; faber, 2007) fx (kroencke et al., 2014; cenedese et al., 2012; barroso & santa-clara, 2015; de almeida et al., 2018) options (maze, 2012; baker & haugen, 2012) commodity futures (fernandez-perez et al., 2018; zaremba et al., 2019) bitcoin (madan et al., 2015; jang & lee, 2017; mcnally et al., 2018; alessandretti et al., 2018; colianni et al., 2015) resolution daily (dash & dash, 2016; henrique et al., 2018; krauss et al., 2017; deng et al., 2016; gao & chai, 2018; gatev et al., 2006; chen et al., 2019; rad et al., 2016; yu & yan, 2020; bernile & lyandres, 2011; dimic et al., 2018; berkowitz & depken, 2018; geyer-klingeberg et al., 2018; al-sulaiman, 2022; liu et al., 2003; sadka, 2006; garfinkel & sokobin, 2006; chordia & shivakumar, 2006; nair et al., 2010; chen & hao, 2017; weng et al., 2018; lee et al., 2019; deng et al., 2016; almahdi & yang, 2017; barroso & santa-clara, 2015; madan et al., 2015; jang & lee, 2017; mcnally et al., 2018; alessandretti et al., 2018; colianni et al., 2015; al-sulaiman & al-matouq, 2021; de almeida et al., 2018) monthly (frazzini & pedersen, 2014; faber, 2007; engle, 1982; lisauskas, 2011; maze, 2012; kroencke et al., 2014; cenedese et al., 2012; baker & haugen, 2012; fernandez-perez et al., 2018; zaremba et al., 2019) quarterly (maewal & bock, 2011; cohen et al., 2010; frazzini & pedersen, 2014; kishore et al., 2008) yearly (lev & nissim, 2006) methods pair trading and statistical arbitrage (gatev et al., 2006; chen et al., 2019; rad et al., 2016; bernile & lyandres, 2011; dimic et al., 2018; berkowitz & depken, 2018; geyer-klingeberg et al., 2018; fernandezperez et al., 2018) fundamental methods (lev & nissim, 2006; cohen et al., 2010; frazzini & pedersen, 2014) momentum (kishore et al., 2008; liu et al., 2003; sadka, 2006; garfinkel & sokobin, 2006; chordia & shivakumar, 2006; frazzini & pedersen, 2014; zaremba et al., 2019) pa ge 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 ai-ml methods (lstm, annt, rl, cf and ga) (dash & dash, 2016; krauss et al., 2017; deng et al., 2016; gao & chai, 2018; yu & yan, 2020; al-sulaiman, 2022; lee et al., 2019; deng et al., 2016; almahdi & yang, 2017; jang & lee, 2017; al-sulaiman & al-matouq, 2021; de almeida et al., 2018) ai-ml methods (svm and svr and logistic regression) (henrique et al., 2018; chen & hao, 2017; lee et al., 2019; madan et al., 2015; mcnally et al., 2018; alessandretti et al., 2018; colianni et al., 2015; de almeida et al., 2018) ai-ml methods (decision tree, k-nearest, and random forest) (nair et al., 2010; chen & hao, 2017; lee et al., 2019; weng et al., 2018; madan et al., 2015) the cycle of algorithmic trading starts with an investment idea followed by quantitative research and model development. after that, the model’s implementation using a programming language is needed, and then perform a back testing to measure the algorithm’s performance in the past. once we ensure the model validity, we test the algorithm’s performance on the live stream using paper trading. finally, as we are asserting the algorithm’s quality, we may deploy it for live trading and monitor its performance. figure 1 shows the cycle of algorithmic trading. figure 2 shows dema’s extracted signals with nf = 20 for the fast ema and ns = 100 for the slow ema on the google inc. (goog) historical prices for the period from 2001 to 2018. relative strength index (rsi) is a popular momentum figure 1: cycle of algorithmic trading figure 2: signal of double exponential moving average on google from 20012018 pa ge 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 indicator proposed by welles wilder (wilder, 1978). the rsi compares upward and downward movements over a specific period n and returns a range of oscillator values between 0 and 100. often, the value below 30 is an indication of oversold activities, and the value over 70 indicates overbought actions. a popular value of parameter n is n = 14, and the resolution is based on the trading frequency. in addition, n values of 9 and 25 are predominantly in use. the rsi value is determined as follows: where ut and dt are the upward and downward changes, respectively, and they following: ut={pt p(t-1) ifpt p(t-1) > 0 0otherwise } dt={|pt p(t-1) |ifpt p(t-1) < 0 0otherwise } the financial statements contain considerable information about company performance divided into the balance sheet, income statement, and cash flow statement. the balance sheet provides an overlook of the assets, liabilities, and equity of the company. the income statement shows the net sales, operating costs, interest (i), taxes (t), depreciation (d) and amortization (a), and the earnings (e) before and after these costs (ebitda, ebit, ebt, net income). finally, the cash flow statement measures the figure 3: macd for microsoft from 2018-2019 figure 4: rsi for microsoft for 2020 pa ge 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 the liquidity ratio measures the firm’s ability to meet its obligations. the asset management ratio identifies the efficiency of managing the investment on assets compared to sales revenue. the debt management ratios aim to measure the firm’s exposure to financial leverage. profitability ratios measure the effects of the other class ratio on the operating income. table 3 shows common ratios in each class5. figure 6 shows the co-movement of home depo. (hd) and wall-mart (wmt) along with the normalized spread with trading signal given ∆ = 1. for more on statistical arbitrage pairs trading strategies, see (krauss, 2017). figure 5: return on equity from 2005 to 2020 for aapl and msft figure 6: signal for sample pair trading pa ge 10 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 figure 7 shows the profits of the options strategies over a variety of possible prices at maturity. tin this paper, we do not aim to define, classify, differentiate or illustrate the methods in these areas, but alternatively, we focus on their application in trading and explore some of the standard methods used to develop algorithms for trading. however, machine learning and data mining algorithms aim to solve estimations, predictions, classifications, associations, and clustering problems. the problems can be classified into supervised learning, unsupervised learning, semi-supervised learn ing, and reinforcement learning. this paper discusses the decision tree methods, the long short-term memory neural network, and reinforcement q-learning. nevertheless, figure 8 shows a social network of the universal methods of machine learning used in algorithmic tradings. figure 7: possible profits of various option strategies figure 8: social network of machine learning methods pa ge 11 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 figure 9 shows an example of simplified decision tree rules. the rules are achieved through constructing a path for the tree, starting the root node to leaves through the branches of decision nodes. the classification and regression algorithm (cart) is a classical decision tree algorithm. cart partitions the tree in a binary manner by splitting the tree into two branches at each decision node. the splitting is based on the maximum value of the optimality measure function among all possible split candidates. similar arguments apply to all the nodes in all hidden layers and the output layer. for example, figure 10 illustrates the forward path of a simplified lstm consist of three input nodes, three nodes in the first hidden layer, two nodes in the second hidden layer, and one output node. figure 9: example of decision tree rules figure 10: illustration of lstm neural network conclusion in conclusion, the dominance of computer-based auto trading, representing 80% of wall street activity, is poised to extend to individual investors. this research comprehensively reviews algorithmic trading, encompassing definitions, project life cycles, platforms, languages, strategy classification, performance measures, and development methodologies. primarily focusing on pa ge 12 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 1-14, 2024 lower-frequency trading, it categorizes reviewed papers by objectives, financial instruments, trading periods, and methodologies. the findings highlight a focus on shorter investment periods for popular assets like stocks, with limited attention to derivatives due to complexity. serving as a valuable resource, this paper also charts a course for future research in algorithmic trading. references al-sulaiman, t., & al-matouq, a. 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(2019). picking winners to pick your winners: the momentum effect in commodity risk factors. the north american journal of economics and finance, 50, 101017. pa ge 1 pa ge 47 american journal of financial technology and innovation (ajfti) socioeconomic impact assessment of caritas nairobi self help programme: ruiru fund self help group programme boniface mbugua kabue1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3298 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: june 29, 2024 accepted: august 01, 2024 published: march 19, 2025 established in 1995 by 30 members of the catholic women association (cwa) in ruiru parish, kiambu county, the group has evolved into a leading entity within the caritas nairobi network, currently entitled to over 20,000 members and savings exceeding ksh 1 billion. this assessment aimed to evaluate the socioeconomic transformation facilitated by the ruiru fund self-help group programme within the framework of the caritas nairobi selfhelp programme. the researcher will particularly investigate the group’s growth trajectory including the challenges along the ragged progressive paths, its leadership and governance management, its impact on members’ livelihoods, community development initiatives, and its role in fostering financial inclusion and empowerment in ruiru, kenya and beyond. the study targeted 11,027 accessible members of the programme. the study adopted descriptive survey and predictive correlation research designs, collecting data from respondents with practical experience with the socioeconomic impact assessment of the ruiru fund selfhelp group programme. the study used a structured questionnaire administered online through google forms to collect the required primary data. the study established a positive significant influence of the group’s growth trajectory, the challenges along the ragged progressive paths, its leadership and governance management on socioeconomic impact of ruiru fund self help group programme. through surveys, the investigator aims to provide insights into the mechanisms through which self-help groups can contribute to socioeconomic development at the grassroots level, offering lessons and recommendations for similar programs elsewhere in other caritas kenya jurisdictions and beyond. keywords community development, entrepreneurship, financial empowerment, financial literacy, leadership, management socioeconomic transformation 1 kiambu water & sewerage company, and board of management, ruiru fund, kenya * corresponding author’s e-mail: mbuguab@gmail.com introduction globally, microfinance institutions (mfis) have become crucial components of development and economic rejuvenation strategies (chomen, 2021). researchers (awojobi, 2019; garcía-pérez et al., 2020) suggest that microfinance services can aid low-income individuals by reducing poverty, enhancing business management, increasing productivity, securing higher returns on investments, and improving their living standards along with those of others in society. typically, mfis provide small loans to low-income individuals with the goal of boosting labor productivity and investment, thereby enhancing household incomes (khan & luo, 2020). the self help group consists of community members who voluntarily come together to form a collective mobilization resources aimed at overcoming poverty. initially, they pool their financial resources through individual savings to tackle poverty. these accumulated savings are then lent to members as capital (vetrivel & mohanasundari, 2011). caritas movement caritas is an international network of catholic organizations dedicated to addressing humanitarian crises and improving the lives of marginalized individuals in communities. rooted in catholic social teachings, caritas agencies are committed to advancing development and justice globally. they provide essential aid during emergencies and work on long-term projects to uplift vulnerable populations, ensuring they achieve a dignified and fulfilling life. through their efforts, caritas seeks to foster solidarity, compassion, and sustainable progress across diverse regions and cultures. the self-help programme was launched by the servant of god michael maurice cardinal otunga, who recognized the socio-economic challenges faced by communities in the archdiocese of nairobi. these communities were trapped in severe poverty due to exclusion from the financial system’s benefits. without opportunities for economic empowerment, many were unable to initiate or expand income-generating activities. caritas operates at various levels: local (parish), diocesan, national, regional, and international. each national caritas organization functions autonomously under its bishops but is part of the global caritas international is confederation, which is affiliated with the universal church. in the archdiocese of nairobi, caritas nairobi serves as the social and development agency. registered as a charitable trust (archdiocese of nairobi social promotion registered trustees), it is chaired by the archbishop of nairobi. the governance structure comprises a two-tiered system with trustees and a non-executive board of directors. caritas nairobi is authorized by the archdiocese of nairobi to coordinate pa ge 48 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 and implement aid and socio-economic development programs. over the years, caritas has assisted thousands through initiatives in microfinance (self-help groups), refugee aid, agriculture and food security, support for individuals with disabilities and hiv/aids, women’s and youth empowerment, peer counseling, and child protection, among other areas. caritas nairobi operates within the archdiocese of nairobi, which encompasses the counties of nairobi and kiambu, divided into 14 deaneries. the archdiocese also includes 114 parishes and numerous christian communities. caritas microfinance in the 1980s, the servant of god michael maurice cardinal otunga dispatched fr. joseph mukui to sierra leone to study its savings and lending model, aiming to implement a similar system in kenya. upon his return, fr. mukui launched a savings and lending program in kiriko parish, in the archdiocese of nairobi with an initial group of 20 members, each contributing a minimum of kshs. 20 monthly. today, this self-help program has evolved into a model of socio-economic empowerment, benefiting society and operating on christian values of love and charity. the archdiocese of nairobi social promotion registered trustees, known as “the trust,” is a charitable organization operating under the name “caritas nairobi.” it is responsible for administering and managing the affairs and projects of self-help groups within the archdiocese of nairobi. the trust has the authority to delegate its responsibilities to one or more trustees to conduct any business or perform any tasks necessary for achieving its objectives. this delegation is given to trustees who possess the relevant professional or business expertise. the objectives within the parishes of the archdiocese of nairobi are as follow; firstly, to encourage the formation of self-help groups composed of members from each specific parish. secondly, to maintain, control, and regulate these selfhelp groups to ensure their proper functioning. thirdly, to ensure that these self-help groups adhere to proper fiscal management practices. fourthly, the tradition and doctrinal teachings of the catholic church within these groups must be upheld. lastly, to coordinate the growth of self-help groups in terms of both the number of groups and the membership within them, ensuring an equitable distribution of capital and effective leadership throughout. ruiru fund self-help group programme the st. francis of assisi catholic church ruiru self-help group, commonly known as the ruiru fund, operates under the caritas nairobi self-help programme within the archdiocese of nairobi. this group was established in 1995 by 30 members of the catholic women association (cwa) in ruiru parish, kiambu county. since its inception, the group has experienced significant growth and has become the leading self help group under the caritas nairobi self help programme. the ruiru fund aims to promote self-reliance and improve the socio-economic status of its members through savings and credit facilities. its objectives include: encouraging a savings culture among members; providing affordable credit facilities to members; empowering women and the community through financial education and support (ruiru catholic fund, 2023). the group engages in various activities and programs to achieve its mission savings and credit: members are encouraged to save regularly, and the accumulated savings are used to provide loans at affordable interest rates; financial education: workshops and training sessions are conducted to educate members on financial management, entrepreneurship, and other relevant skills; community support: the group participates in community development projects and supports charitable activities within the parish and beyond. the shg has already achieved the following; membership growth: from 30 members in 1995 to over 20,000 members currently; financial milestone: members’ savings have surpassed ksh 1 billion, reflecting the group’s strong financial foundation; leadership in caritas nairobi: recognized as the leading self help group under the caritas nairobi self help programme. the ruiru fund aims to continue its growth trajectory by; expanding its membership base; increasing the variety and scope of its financial products and services; enhancing the capacity of its members through continuous education and training; strengthening its role in community development and social welfare initiatives (ruiru catholic fund, 2023). objectives of the study the main aim of this investigation was to evaluate the socioeconomic transformation facilitated by the ruiru fund self help group programme within the framework of the caritas nairobi self help programme. the specific objectives of the investigation were. 1. investigate the influence of group’s growth trajectory on socioeconomic performance of ruiru fund self help group 2. analyze influence of the challenges along the progressive paths on socioeconomic performance of ruiru fund self help group 3. assess influence of the group leadership structure on socioeconomic performance of ruiru fund self help group hypothesis the hypotheses of the investigation included; h01: group’s growth trajectory does not significantly influence socioeconomic performance of ruiru fund self help group h02: the challenges along the progressive paths do not significantly influence socioeconomic performance of ruiru fund self help group h03: the group leadership structure does not pa ge 49 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 significantly influence socioeconomic performance of ruiru fund self help group. literature review theories of the study empowerment theory, as proposed by zimmerman (2000), involves using interventions to help communities gain a sense of control. communities may feel powerless for various reasons, and the focus of empowerment theory is on how oppression contributes to this feeling and how communities can overcome it. the theory aims to assist disadvantaged individuals at personal, group, and community levels to acquire personal, interpersonal, economic, and political power to enhance their livelihoods. fundamentally, empowerment theory challenges systems that prevent people from meeting their essential needs (perkins & zimmerman, 1995). external agents are crucial for capacity building because people need new ideas, understanding, and information to change their perceptions and mindsets, encouraging them to act against barriers in their households and communities. empowerment is seen as an ongoing process that involves personal determination in making choices that can improve individual and community well-being (kabeer, 2005; mosedale, 2005). for significant poverty reduction, interventions like microfinance services should help liberate poor individuals by providing them with the means to earn a living. microfinance institution services offer access to and control over resources to create a sustainable, long-term livelihood and reap the material benefits of this access and control (mosedale 2005). this fosters a spirit of independence and does not merely address existing needs (kabeer, 2005). empirical review abdullah et al. (2021) identified microfinance financial services, training programs, and business coaching had effect on household socioeconomic performance. additionally, the efficiency of microfinance institutions’ services is considered a moderating factor that can enhance the effectiveness of microfinance services. the findings offer valuable insights for policymakers, financial institutions, households, micro-enterprises, and researchers to better comprehend microfinance interventions and their impact on household economic mechanisms. mukabazaire and rusibana (2023) studied effect of microfinance institutions services on socio economic welfare of women in rwanda. a case of selected umurenge saccos in kigali city. the findings indicated that access to credit and social-economic welfare of women have a positive and moderately strong correlation (r = 0.614, p<0.05). saving facilities and social-economic welfare of women also have a positive and moderately strong correlation (r = 0.606, p<0.05). collaterals facilities and social-economic welfare of women have a positive and strong correlation (r = 0.536, p<0.05). these correlations suggest that women’s socioeconomic welfare of women in kigali city is positively influenced by better access to credit, saving facilities, and collateral facilities. the multiple regression r is 0.704, indicates the strength and direction of the overall linear relationship between the study variables. indicating a moderately strong positive relationship between the predictors and the dependent variable. the coefficient of determination (r square) represents the proportion of variance in the dependent variable that the predictors explain. in this model, the r square value is 0.495, which means that approximately 49.5% of the variance in the dependent variable can be explained by the combined effects of collaterals facilities, access to credit, and saving facilities. dhungana (2023) investigated the perceived impact of microfinance on livelihood improvement in the kaski district of nepal. the study revealed that microfinance significantly enhances the livelihoods of poor and marginalized populations. microfinance interventions have notably improved clients’ economic conditions, including the establishment of microbusinesses, increased income levels, saving habits, productive investments, consumption, and capital expenditures. additionally, clients’ social conditions have significantly improved, particularly in terms of educational status, health status, women’s empowerment, and social networking. despite debates over its efficacy, microfinance can be instrumental in promoting economic growth and improving the lives of low-income individuals and communities. the regulatory authority should ensure both the welfare of clients and the sustainability of microfinance institutions by developing sound financial and social outreach efficiencies. mehta (2023) examined the impact of microfinance institutions on the socioeconomic development of women in saurashtra. the study utilized both exploratory and descriptive research methods to achieve its objectives. the researcher collected data from various self-help groups across different blocks on multiple dates. to analyze the primary data, several statistical tools were employed, including descriptive analysis, chi-square test, anova, garret ranking technique, discriminate function analysis, friedman test factor analysis, kendall’s coefficient of concordance test, kruskal-wallis test, neural network, and structural equation modeling. the study concluded that obtaining microfinance loans from these institutions significantly empowers women economically, socio-culturally, and politically. additionally, the findings revealed that beneficiaries of microfinance loans experience better employment opportunities, increased income, and greater participation in household financial decisionmaking compared to non-beneficiaries. chikwira et al. (2022) argued that microfinancing aims to combat poverty by providing credit to poor and marginalized economic sectors. however, the core objective of these institutions has yet to materialize, pa ge 50 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 particularly in developing economies. their study analyzed the role of microfinancing in poverty alleviation using a vector error correction model on quarterly time-series data. the results indicated a significant long-term relationship among variables such as poverty, microfinancing, smes, and agricultural growth. contrary to expectations, microfinancing was found to increase poverty in the long run, while smes and agricultural development reduced poverty levels. in the short run, the regression results showed that sme growth alleviates poverty and that poverty drives the growth of microfinance loans. this suggests that the increase in smes is effective in reducing poverty, but the growth of microfinance institutions is propelled by existing poverty. the study concludes that improper use of microfinancing can exacerbate poverty, indicating that the mere provision of funds is insufficient for poverty reduction. mrindoko and pastory (2022) investigated the impact of microfinance institutions (mfis) services on poverty reduction among micro and small entrepreneurs in iringa municipality, tanzania. the study included 333 micro and small entrepreneurs who had utilized mfi services. using a cross-sectional survey design within a mixed research approach, data were collected from micro and small entrepreneurs through a structured questionnaire and from key informants using an interview guide. out of the 333 distributed questionnaires, 320 were deemed usable for data analysis, while the remaining contained incomplete data. the study found that the entrepreneurs were engaged in sectors such as manufacturing, agriculture, services, and commerce. while most mfi services did not significantly improve the income of these entrepreneurs, the results indicated that mfis have contributed to poverty reduction among micro and small entrepreneurs in iringa municipality. kireti and sakwa (2014) examined the socio-economic impacts of microfinance services on women using the case of rosewo microfinance in nakuru county, kenya. the findings highlighted that microfinance services significantly influenced the socio-economic status of women. access to microcredit services was associated with increased income levels, expanded enterprise stocks and output, and higher spending on health and education services. additionally, micro-savings services provided women with increased capital resources for education and healthcare, smoothed irregular income patterns for better consumption management, and boosted income generation for wealth accumulation. the study also found that microinsurance services fostered stronger trust networks, enhanced social connections, and motivated greater participation in development activities among women. lastly, non-financial services from microfinance institutions were noted to stabilize income levels, thereby alleviating production constraints and enhancing economic stability. patel (2023) suggests that the current body of literature indicates that microfinance affects household income, employment, gender empowerment, education, and health. these aspects are analyzed in terms of their influence on significant poverty indicators. while the study supports the link between microfinance and poverty reduction, it acknowledges scholarly debate about its effectiveness. technological innovation is proposed as a potential solution to address these issues, warranting further investigation. diar et al. (2017) investigated the factors influencing the growth of microfinance institutions (mfis) in kenya, focusing on selected microfinance banks in nairobi city county. the study employed a descriptive research design where data was collected through questionnaires distributed to respondents. the study population consisted of microfinance institutions within nairobi county, with a sample size of 20% drawn from a target population of 180 institutions. thirty-six staff members representing various levels of the institutions were included in the study using a stratified random sampling technique. both primary and secondary data were utilized for data collection, analysis, presentation, and discussion of research findings. primary data included financial and income statements spanning a five-year period, which were summarized and analyzed using spss version 21 to perform inferential statistics, including multiple regression analysis to ascertain relationships between dependent and independent variables. the study findings indicated a positive and significant relationship between leverage, financial literacy, and the growth of microfinance institutions in kenya. it was concluded that the expansion of these institutions could positively impact the welfare of their clients, contingent upon achieving sound financial growth and stability. the study recommended that microfinance institutions develop strategies to ensure adequate leverage levels to meet operational needs and implement appropriate policies and procedures to achieve these objectives. metarials and methods location of the study the investigation was conducted it st. francis of assisi catholic church ruiru self help group (ruiru fund) operating under caritas nairobi self help programme in the archdiocese of nairobi. st. francis of assisi catholic church ruiru self help group which is currently the leading self help group under caritas nairobi self help programme with over 20,000 (twenty thousand) members and members’ savings in excess of ksh 1 billion. study design the study adopted descriptive survey and predictive correlation research designs, collecting data from respondents who have had practical experience with the socioeconomic impact assessment of ruiru fund self help group programme. descriptive survey and predictive correlation research designs was pa ge 51 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 useful because the researcher wanted to collect data on phenomena that cannot be observed directly. its advantage is that it allowed for the collection of large amounts of data from a sizeable population in a highly effective, easily and in an economical way by the use of questionnaires. predictive correlation design was used due to its appropriateness in the measurement of two or more variables and the relationship between or among those variables (stangor, 2011). target population the total number of members of st. francis of assisi catholic church ruiru self help group is 20,000 scattered all over the nairobi and kiambu counties. th accessible population for the investigation was 11,027 members of st. francis of assisi catholic church ruiru self help group. this population was useful for the investigation because they have been involved in savings and taking loans towards the socioeconomic transformation of their social and economic activities. sample size and sampling procedure the sample drawn from every stratum was proportionate to the stratum’s share of the total population. representative sample which enabled generalization of the findings was derived from yamane (1967) formula n = n/(1+ne2) where; n = sample size n = population e = precision error which is 0.05 therefore, out of a population of 11,027 members of st. francis of assisi catholic church ruiru self help group, the sample consisted of 110 member as expressed in the formula n = 11,027/1+ (11,027 * 0.0025) = 109.1684 ≈ 110. the sample size in this study was selected based on the criteria set by roscoe’s rule of thumb sekaran (2003) that is a sample that is larger than 30 and less than 500 is appropriate for most research. the sample size in this study was 110 which is within the 30-500 range roscoe’s rule of thumb. the study then used random sampling technique to pick the sample. the study used structured questionnaire as the main data collection tool. questionnaire is appropriate in a survey research because it is simple to administer and ease for the respondents to score on a 5 point likert scale which is easy to analyze (cohen et al., 2007). structured questionnaire is also useful in obtaining consistency across the respondents (denscombe, 2007). this allowed the collection of ordinal measure data from the respondents. each section of the questionnaire investigated a given variable and was used to test the corresponding hypothesis and research questions. to increase response rate, questionnaires were sent to all members of the population and responses randomly picked to meet the sample numbers. to accelerated data collection process, the investigator used google forms where a link was sent to the respondents and when they filled the questionnaire, it came back to the investigator’s database. data analysis and presentation the researcher used multiple linear regression to investigate socioeconomic impact assessment of caritas nairobi self help programme: ruiru fund self help group programme. linear regression was used to test the relationship between variables due to the linear relationship between them. the following regression model was used for quantitative procedures examining the relationship between independent and dependent variables; y = α + β1x1+ β2x2 + β3x3 + ε where; y = socioeconomic impact α =constant β1……β3= regression coefficients x1= group’s growth trajectory x2= the challenges along the progressive paths x3= the group leadership structure ε = the error term. results and discussions demographic results figure 1: age bracket of the respondents the results established that majority of the respondents 44% observed that the members were within the age bracket of 41-50 years followed by 37% who were within 31-40 years age bracket and 19% who were within 5160 years age bracket. this finding indicated that the programme was attracting middle age members for their transform their socioeconomic status. this demographic may have specific needs such as career advancement, family support, health considerations, and financial planning for retirement. ruiru fund self help group should therefore address these needs effectively. the results on the motivation of joining the programme established that majority of the respondents 88% were motivated to join the ruiru fund self help group programme in pursuit of financial support compared to 12% who were motivated by training programme. this strong motivation suggests that financial needs are the primary concern for the majority of members. in order for the programme to pursue this motivation, it should increase the availability and accessibility of credit and pa ge 52 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 figure 2: the motivation of joining the programme loan facilities. ensure that these financial products are tailored to meet the diverse needs of members, such as small business loans, emergency loans, and microfinance options. the programme should allocate resources effectively to strengthen financial support services. this includes funding for loan schemes, financial counseling, and development of financial products that meet member needs. in its promotional programme. in marketing and outreach efforts, the programme should emphasize the financial support benefits of joining the ruiru fund self help group programme. success stories and testimonials from members who have benefited financially can attract new members. ruiru fund self help group growth trajectory the finding on the number of group members changed since its inception, the results revealed that majority of the respondents 79% observed that membership had increased significantly compared to 21% who observed that membership had increased slightly. the significant increase in membership (79% of respondents) indicates that the ruiru fund self help group programme is perceived as effective and attractive. this growth suggests that the program is meeting the needs and expectations of its members. the increase in membership reflects a positive reputation within the community, likely driven by word-of-mouth recommendations and visible benefits experienced by current members. as membership grows, it is crucial to maintain the figure 3: the number of group members changed since its inception quality of services provided. this might involve scaling up operations, hiring additional staff, and implementing robust quality control measures. efforts should be made to continue offering personalized services to members, ensuring that individual needs are met despite the larger group size. the significant increase in membership means that the program is likely having a broader impact on the community. this increased influence can be leveraged to implement larger community development projects and initiatives. a larger membership base enhances the social capital of the group, providing more opportunities for networking, collaboration, and collective action. the growth in membership is a positive indicator for the long-term sustainability of the program. however, it also requires careful planning to ensure that the program can sustain this growth over time without compromising its core values and objectives. by addressing these implications, the ruiru fund self help group programme can effectively manage its significant membership growth, ensuring continued success and a positive socioeconomic impact on its members and the broader community. the results suggest that the primary factor driving membership growth in the ruiru fund self help group programme is improved financial benefits, as indicated by 41% of respondents. this implies that financial incentives are the most significant motivator for joining the program. additionally, effective leadership, noted by 24% of respondents, plays an important role, suggesting that strong management and guidance are also key to attracting members. community outreach, observed by 21%, indicates that efforts to engage and involve the community are essential but less influential than financial benefits and leadership. lastly, better financial performance, cited by 12% of respondents, shows that while financial success is a factor, it is the least impactful of the four identified drivers. overall, these results highlight the importance of focusing on financial incentives and effective leadership to continue growing membership. results concerning the services to the members (see figure 5) revealed that majority of respondents 47% observed the members utilized savings and credit, 31% observed that members also utilized financial literacy, 13% observed that the members utilized business training and only 6% of the respondents observed that the members figure 4: factors have contributed to the change in membership pa ge 53 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 utilized market linkages. with 47% of respondents noting that members primarily utilize savings and credit services, there is a strong demand for financial products that support savings growth and access to credit. this suggests a need to continue emphasizing and expanding these services to meet member needs effectively. the significant observation (31%) that members utilize financial literacy services underscores the importance of education in financial management. strengthening these programs can empower members to make informed financial decisions, potentially improving their overall financial health and sustainability. despite being mentioned by 13% of respondents, the utilization of business training services highlights a notable but somewhat lower interest compared to financial and savings services. there may be opportunities to enhance these offerings to better support members in developing and managing their businesses effectively. the low utilization rate of market linkages, noted by only 6% of respondents, suggests a potential area for improvement. strengthening efforts to connect members with markets could enhance their ability to sell products or services, thereby increasing their income and economic opportunities. figure 5: services utilized from the programme results on rating the overall effectiveness of the programme (see figure 8) revealed that majority of respondents 67% observed that the ruiru fund self help group programme was effective, 20% observed that the programme was very effective and 13% observed that the programme was ineffective. this high satisfaction level suggests that the program is meeting or exceeding expectations in fulfilling its objectives and delivering benefits to its members. despite the overall positive ratings, the 13% who found the program ineffective highlight areas where improvements may be needed. understanding the reasons behind this perception can provide insights into specific aspects of the program that require attention, such as service delivery, member engagement, or program management. the high satisfaction rates present an opportunity to build on existing strengths and successes of the program. by identifying and reinforcing what members find effective, the program can continue to enhance its impact and relevance within the community. given the varying perceptions of effectiveness, it’s important for program managers to regularly evaluate feedback and adapt strategies accordingly. this ongoing process of evaluation and improvement can ensure that the program remains responsive to the evolving needs and expectations of its members. figure 6: rating the overall effectiveness of the programme figure 7: projects members of ruiru fund self help group programme participated in concerning the projects members of the ruiru fund self help group programme participated in, the results revealed that majority of respondents 43% observed that members used the credit from the programme in economic empowerment, 32% observed that members used the credit to support education of their family members, 14% used the credit for environmental conservation, 7% used the credit for infrastructural development and 4% used the credit for health initiatives. the majority (43%) of respondents noting that members used credit for economic empowerment highlights a significant priority within the program. this suggests that access to credit plays a crucial role in supporting members’ entrepreneurial activities and income-generating projects. emphasizing and expanding these economic opportunities can further empower members economically. the substantial number (32%) of respondents indicating that members used credit to support education underscores the importance of education within the community. this usage suggests that the program not only supports economic activities but also contributes to improving educational outcomes for members’ families, potentially leading to broader community development. while smaller percentages (14% and 7%, respectively) used credit for environmental conservation pa ge 54 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 and infrastructure development, these areas indicate a commitment to broader community welfare and sustainability. strengthening initiatives in these areas could enhance environmental stewardship and improve local infrastructure, contributing to long-term community resilience and quality of life. the variety of projects members engage in reflects the program’s multifaceted impact and its ability to cater to diverse community needs. this diversity underscores the program’s adaptability and responsiveness to local priorities, fostering a more inclusive approach to community development. figure 8: how quality of life improved figure 8 reveals the socioeconomic improvement of quality of life of the members. majority of respondents 26% observed that the ruiru fund self help group programme has improved members living conditions, employment and community cohesion respectively compared to 22% who observed that generally the services had improved. the majority (26%) of respondents noting improvements in members’ living conditions and employment highlights the program’s positive impact on socioeconomic factors. this suggests that the program effectively supports members in enhancing their livelihoods and achieving greater economic stability, potentially reducing poverty and improving overall well-being. another significant observation (26%) is the improvement in community cohesion attributed to the program. this indicates that the program not only benefits individual members but also strengthens social ties and unity within the community. building on this aspect can foster a supportive and collaborative environment among members, enhancing overall community resilience and solidarity. while slightly fewer respondents (22%) noted a general improvement in services, this finding underscores the program’s overall effectiveness in delivering valuable services to its members. continuously enhancing service delivery and responsiveness to member needs can further strengthen the program’s impact and sustainability. this finding is supported by patel (2023) who established that the current body of literature indicates that microfinance affects household income, employment, gender empowerment, education, and health. figure 9: challenges faced while participating in the programme ruiru fund self help group challenges along the progressive paths concerning the challenges faced while participating in the programme, results revealed that the ruiru fund self help group programme faced the majority of respondents 38% observed that the programme faced lack of market linkage to its members as a challenge, 27% observed that the members were still exposed to high interest rates, 24% observed that the members had inadequate training on operations of microfinance based self help groups. the majority (38%) of respondents identifying lack of market linkage as a challenge indicates a critical area for improvement. addressing this challenge can enhance members’ ability to sell their products or services, thereby increasing their income and economic opportunities. strengthening efforts to connect members with markets through partnerships or training programs could significantly benefit program participants. a significant number (27%) of respondents highlighting high interest rates as a challenge underscores the financial burden on members accessing credit through the program. lowering interest rates or providing financial literacy programs to help members manage debt effectively could mitigate this challenge and improve the overall financial health of participants. the observation (24%) that members have inadequate training on the operations of microfinance-based self help groups points to a need for capacity-building initiatives. providing comprehensive training on financial management, group dynamics, and governance can empower members to effectively manage their groups and maximize the benefits of program participation. addressing these challenges is crucial for enhancing the program’s sustainability and impact. by improving market linkages, reducing interest rates, and enhancing training programs, the program can better support members in achieving their economic and social goals. this, in turn, can contribute to the longterm success and resilience of the self help group programme. pa ge 55 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 concerning required improvement in the leadership structure, majority of respondents 41% observed that there is an urgent need in the improvement of the top management leadership structure of the ruiru fund self help group programme, 32% observed a need to improve programme based leadership structure while bere 22% observed a need to improve both general leadership structure and financial governance leadership structure. the majority (41%) of respondents emphasizing the urgent need to improve the top management leadership structure highlights a critical area for organizational development. strengthening leadership at the executive level can enhance strategic direction, decision-making, and overall governance, ensuring effective management and alignment with organizational goals. the significant number (32%) of respondents identifying a need to improve program-based leadership structure suggests a focus on enhancing leadership within operational and programmatic aspects of the organization. this can improve program implementation, member engagement, and service delivery, ultimately enhancing the program’s impact and effectiveness. the observation (22%) that there is a need to improve both the general leadership structure and financial governance leadership structure underscores the importance of governance and financial oversight. strengthening these areas can improve transparency, accountability, and financial management practices, thereby enhancing organizational integrity and sustainability. addressing these leadership structure improvements requires strategic development and capacity-building initiatives. investing in leadership training, succession planning, and governance frameworks can empower leaders at all levels to effectively manage and lead the organization, driving continuous improvement and organizational growth. these findings underscore the organization’s commitment to achieving excellence in leadership and governance. by prioritizing these improvements, the ruiru fund self help group programme can enhance its operational efficiency, member satisfaction, and overall impact, positioning itself for long-term success and sustainability. socioeconomic impact of ruiru fund self help group programme the investigator conducted inferential statistics using multiple regression analysis to establish the socioeconomic impact of ruiru fund self help group programme. the investigator tested the fllowing hypotheses; h01: group’s growth trajectory does not significantly influence socioeconomic performance of ruiru fund self help group h02: the challenges along the progressive paths do not significantly influence socioeconomic performance of ruiru fund self help group h03: the group leadership structure does not significantly influence socioeconomic performance of ruiru fund self help group figure 10: required improvement in the leadership structure table 1: model summary model r r square adjusted r square std. error of the estimate 1 .703a .494 .488 .76196 table 1 results showed that there was a strong association since the r-value was 0.703 and the r square was 0.494. the proportion of the dependent variable is revealed by the r2 value, “group’s growth trajectory, the challenges along the progressive paths and the group leadership structure “. in this case, 49.4% was the r squared, which was large indicating a high degree of correlation. table 2: anova socioeconomic impact of ruiru fund self help group programme model sum of squares df mean square f sig. 1 regression 178.001 4 44.500 76.648 .000b residual 182.301 314 .581 total 360.302 318 the predictor: measure indicators of group’s growth trajectory, the challenges along the progressive paths and the group leadership structure and the dependent variable is socioeconomic impact of ruiru fund self help group programme. table 2 showed that the regression prototype substantially forecasted the outcome variable with a p-value of 0.000, less than 0.05, and that the model generally significantly and statistically predicted the result variable. this result indicated that there was a strong association between the dependent (socioeconomic pa ge 56 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 table 3: influence of organizational structures on leadership performance model unstd coeff std coeff t sig. b std. error beta 1 (constant) .484 .156 3.096 .002 group’s growth trajectory .494 .091 .480 5.434 .000 the challenges .215 .092 .200 2.328 .021 leadership structure .363 .080 .048 3.788 .031 first, the study established a positive significant influence of the group’s growth trajectory on socioeconomic impact of ruiru fund self help group programme (β=0.494, p=0.000<0.05). this significant positive influence made the researcher conclude that the group’s growth trajectory influenced socioeconomic impact of ruiru fund self help group programme. based on this finding, an increase of the group’s growth trajectory by 1 unit will lead to an increase in socioeconomic impact of ruiru fund self help group programme by 0.494 multiple units. this finding is supported by mehta (2023) who established that microfinance loans from these institutions significantly empowers women economically, socio-culturally, and politically. additionally, the findings revealed that beneficiaries of microfinance loans experience better employment opportunities, increased income, and greater participation in household financial decision-making compared to non-beneficiaries. the null hypothesis that h01: group’s growth trajectory does not significantly influence socioeconomic performance of ruiru fund self help group was therefore rejected. second, the study established a positive significant influence of the challenges along the progressive paths on socioeconomic impact of ruiru fund self help group programme (β=0.215, p=0.021<0.05). this significant positive influence made the researcher conclude that the challenges along the progressive paths influenced socioeconomic impact of ruiru fund self help group programme. based on this finding, an increase of the challenges along the progressive paths by 1 unit will lead to an increase in socioeconomic impact of ruiru fund self help group programme by 0.494 multiple units. the null hypothesis that h02: the challenges along the progressive paths do not significantly influence socioeconomic performance of ruiru fund self help group was therefore rejected. third, the study established a positive significant influence of the group leadership structure on socioeconomic impact of ruiru fund self help group programme (β=0.361, p = 0.031<0.05). this significant positive influence made the researcher conclude that the group leadership structure influenced socioeconomic impact of ruiru fund self help group programme. based on this finding, an increase of the group leadership structure by 1 unit will lead to an increase in socioeconomic impact of ruiru fund self help group programme by 0.494 multiple units. this finding is supported by dhungana (2023) who established that microfinance significantly enhances the livelihoods of poor and marginalized populations. microfinance interventions have notably improved clients’ economic conditions, including the establishment of microbusinesses, increased income levels, saving habits, productive investments, consumption, and capital expenditures. additionally, clients’ social conditions have significantly improved, particularly in terms of educational status, health status, women’s empowerment, and social networking. the null hypothesis that h02: the group leadership structure does not significantly influence socioeconomic performance of ruiru fund self help group was therefore rejected. conclusion the main aim of this investigation was to evaluate the socioeconomic transformation facilitated by the ruiru fund self help group programme within the framework of the caritas nairobi self help programme. the first objective of the study was to investigate the influence of group’s growth trajectory on socioeconomic performance of ruiru fund self help group. the study established a positive significant influence of the group’s growth trajectory on socioeconomic impact of ruiru fund self help group programme. the significant positive influence made the researcher conclude that the group’s growth trajectory influenced socioeconomic impact of ruiru fund self help group programme. the second objective was to analyze influence of the challenges along the progressive paths on socioeconomic performance of ruiru fund self help group. the study established a positive significant influence of the challenges along the progressive paths on socioeconomic impact of ruiru fund self help group programme. the significant positive influence made the researcher conclude that the challenges along the progressive paths influenced socioeconomic impact of ruiru fund self help group programme. the third objective of the study was to assess influence of the group leadership structure on socioeconomic performance of ruiru fund self help group. the study established a positive significant influence of the group leadership structure on socioeconomic impact of ruiru fund self help group impact of ruiru fund self help group programme) variable and the independent variable (group’s growth trajectory, the challenges along the progressive paths and the group leadership structure). pa ge 57 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 programme. the significant positive influence made the researcher conclude that the group leadership structure influenced socioeconomic impact of ruiru fund self help group programme. recommendations 1. ruiru fund self help group programme should develop strong financial internal control systems and align all financial systems towards the recommendation of the parent organization; caritas nairobi self help group programm for effective functionality. by implementing these measures, the ruiru fund self help group programme can develop strong financial internal control systems that align with the recommendations of caritas nairobi self help group programme, thereby ensuring effective functionality, enhanced financial integrity, and long-term sustainability. 2. ruiru fund self help group programme should develop and implement comprehensive training programs focused on enhancing the leadership skills of current and potential leaders within the group. these programs should cover areas such as strategic planning, conflict resolution, decision-making, and effective communication 3. ruiru fund self help group programme should evaluate term limits and rotation policies for leadership positions to prevent stagnation and bring in fresh perspectives. this can also provide opportunities for more members to develop leadership skills. the programme should also promote diversity and inclusivity within the leadership structure. ensure that leadership positions are accessible to all members, including women, youth, and marginalized groups, to reflect the diverse makeup of the group 4. ruiru fund self help group programme should allocate a dedicated budget for leadership development activities, including training, workshops, and capacitybuilding initiatives. investing in leadership development can yield significant returns in terms of group performance and impact. the programme should provide leaders with access to the necessary tools, resources, and support systems to perform their roles effectively. this includes access to technology, information, and expert advice 5. ruiru fund self help group programme should streamline internal processes to enhance efficiency and reduce the likelihood of operational challenges. this includes improving communication channels, decisionmaking procedures, and project management practices. the programme should further utilize external resources such as grants, technical assistance, and mentorship programs to provide additional support in addressing complex challenges. by implementing these recommendations, the ruiru fund self help group programme can effectively address and mitigate the challenges encountered along its progressive paths, thereby enhancing its socioeconomic impact and contributing to the sustainable development and well-being of its members. references abdullah, w. m. z. b. w., zainudin, w. n. r. a., ismail, s. b., haat, m. h. c., & zia-ul-haq, h. m. (2021). “the impact of microfinance on households’ socioeconomic performance”: a proposed mediation model. the journal of asian finance, economics and business, 8(3), 821-832. awojobi, o. n. (2019). microcredit as a strategy for poverty reduction in nigeria: a systematic review of literature. global journal of social sciences, 18, 53-64. chikwira, c., vengesai, e., & mandude, p. (2022). the impact of microfinance institutions on poverty alleviation. journal of risk and financial management, 15(9), 393. chomen, d. a. (2021). the role of microfinance institutions on poverty reduction in ethiopia: the case of oromia credit and saving share company at welmera district. futur bus j, 7, 44. dhungana, b. r. (2023). perceived impact of microfinance on livelihood improvement in kaski district of nepal. interdisciplinary journal of innovation in nepalese academia, 2(1), 81-95. diar, a. y., rotich, g., & ndambiri, n. a. (2017). factors affecting the growth of micro finance institutions in kenya: a case of selected micro finance banks in nairobi city county, kenya. strategic journal of business & change management, 4(1), 1-12. garcía-pérez, i., fernández-izquierdo, m. á., & muñoztorres, m.j. (2020). microfinance institutions fostering sustainable development by region. sustainability, 12(7), 1– 23. kabeer, n. (2005). gender equality and women’s empowerment: a critical analysis of the third millennium development goal. gender and development, 13(1), 13-24. khan, a. a., khan, s.u., fahad, s., ali, m. a. s., khan, a. & luo, j. (2020). microfinance and poverty reduction: new evidence from pakistan. int. j. fin. econ., 1-11. kireti, g. w., & sakwa, m. (2014). socio-economic effects of microfinance services on women: the case of rosewo microfinance, nakuru county, kenya. international journal of academic research in economics and management sciences, 3(3), 43-59. madialo, l. o. (2022). an evaluation of the effect of prudential regulations on the social and financial performance of microfinance banks in kenya [masters dissertation, strathmore university]. mehta, h. p. (2023). a study on impact of microfinance institutions on socio economic development of saurashtran women. interdisciplinary social studies, 2(5), 1930-1937. mosedale, s. (2005). assessing women’s empowerment: towards a conceptual framework. journal of international development, 17, 243–57. mrindoko, a. e., & pastory, d. (2022). the contribution of microfinance institutions (mfis) services to poverty reduction among micro and small pa ge 58 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 47-58, 2025 entrepreneurs in iringa municipality, tanzania. african journal of applied research, 8(1). mukabazaire, d. and rusibana, c. (2023). effect of microfinance institutions services on socio economic welfare of women in rwanda. a case of selected umurenge saccos in kigali city. journal of finance and accounting, 7(7), 100–110. muthini, j. m., & ndede, f. w. (2022). socio-economic factors and table banking loans default levels among women groups in machakos county, kenya. int j curr aspects fin banking acc, 4(1), 91-103. patel, n. (2023). the impact of microfinance on poverty alleviation [masters dissertation, university of essex]. perkins, d. d. & zimmerman, m. a. (1995). empowerment theory, research, and application. american journal of community psychology, 23(5, 569579. ruiru catholic fund. (2023). about ruiru catholic fund. https://ruirucatholicfund.org stangor, c. (2011). research methods for the behavioural sciences (4th ed.). mountain view, ca: cengage. zimmerman, m. a. (2000). empowerment theory: psychological, organizational, and community levels of analysis. in j. rappaport & e. seidman (eds.), handbook of community psychology (pp. 43–63). pa ge 1 pa ge 44 american journal of financial technology and innovation (ajfti) fintech’s impact on the digital transformation of the qatar insurance sector: opportunities and challenges raed elomari1* volume 2 issue 1, year 2024 issn: 2996-0975 (online) https://doi.org/10.54536/ajfti.v2i1.2387 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: june 15, 2024 accepted: july 17, 2024 published: december 31, 2024 this study provides a comprehensive analysis of qatar’s digital insurance industry using a conceptual framework based on established theories. the study aimed to assess different challenges and opportunities, including the regulatory environment, market dynamics, competitive strategies, and digital payment and financial inclusion in qatar, and also fill a critical gap in the literature. the study uses a positivist perspective and deductive methodology to explore qatar’s digital insurance sector, revealing diverse demographics and strong correlations between variables like regulations and competitive rivalry, highlighting the positive attitude towards digitization in the industry. the regression model, elucidating 61.7% of the variance, accentuates the pivotal role of “competitive rivalry and enhanced digital insurance sector.” nevertheless, non-significant contributions from “security challenges for digitization” and “market competitiveness” beckon further exploration. the study concludes with insightful recommendations for transparency enhancements, digital tool exploration, and addressing new entrant barriers, offering invaluable guidance to industry stakeholders navigating qatar’s dynamic digital insurance landscape. keywords competitive rivalry, digital insurance, financial inclusion, insurance regulation, qatar insurance industry, tam introduction the worldwide fintech environment has seen remarkable growth, with investments reaching $34.5 billion by the end of 2019 (skoric et al., 2022). however, the middle east and north africa (mena) area, including qatar, fell behind, accounting for less than 1% of worldwide fintech investments (opportunities await: how insurtech is reshaping insurance, 2016). the covid-19 epidemic expedited global digital transformations, providing the stage for significant growth in the mena area (ibrahim et al., 2020). particularly because of the national fintech strategy and the creation of the qatar fintech hub (qfth), qatar, has become a prominent participant in promoting fintech innovation (hub, 2021). the country’s proactive regulatory strategy, demonstrated by the fintech office and the qatar central bank (qcb), establishes it as a possible leader in islamic fintech (ramiah et al., 2023). by introducing the national fintech strategy in 2019 and establishing the qatar fintech hub (qfth) in 2020 (villegas-mateos, 2022), qatar has made significant strides towards creating a strong fintech ecosystem (alkhazaleh, 2021; allen, 2021). qatar’s potential to flourish in islamic fintech by harnessing its islamic financial experience is significant (alnasr, 2022; cherqaoui, 2022). digital payment solutions are made possible by qatar’s expanding e-commerce sector, and digital wallets are becoming vital instruments for advancing financial inclusion as well as financial transactions (dahdal et al., 2020). concurrently, the covid-19 pandemic has sped up the digital revolution in qatar’s insurance industry. conventional insurance structures are changing as a result of the move towards customer-centric strategies and online distribution channels (feghali et al., 2022). digital innovations such as smart contracts and telematics devices are transforming claims processing, product creation, and risk assessment (łyskawa et al., 2019). the insurance industry in qatar is expected to develop at a 4.7% compound annual growth rate (cagr) and reach $1.9 billion by 2026 (al malkawie, 2020). as part of this progression, qatar insurance company (qic) has introduced insurtech platforms such as anoud+. the qatar financial centre (qfc), qatar fintech hub (qfth), and qic are working together to make it easier for insurtech startups to enter the qatari market (elmasri et al., 2019). the covid-19 epidemic has driven insurtech businesses to take the lead in the global insurance market, which is exceeded $7 trillion in 2022 and expected to reach 9.91 trillion by 2028 (njegomir & demko-rihter, 2023; statista & , 2023; tripathy). fintech has emerged as a viable alternative for small and medium-sized enterprises (smes) that are facing financial difficulties. despite the mena region’s lack of fintech investment, qatar’s national fintech strategy portrays the country as an innovator, laying the groundwork for digital transformation (eckert & osterrieder, 2020). even though the country’s fintech ecosystem is still in its early stage, it has a lot of room to develop if infrastructure and regulations are strengthened (khan et al., 2023). understanding the potential and difficulties that come with this digital transition is essential as fintech and digital insurance continue to grow in popularity in qatar (hujaimi et al., 2022). addressing this gap in the 1 metlife gulf, dubai, united arab emirates * corresponding author’s e-mail: dr.raed_elomari@outlook.com pa ge 45 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 existing research, this study presented a synthesized conceptual framework that integrates relevant ideas to comprehensively analyze the possibilities and challenges in the digital insurance business. the rationale lies in the necessity for a systematic, theoretical investigation to guide the development of the insurance industry through digitalization in the context of fintech (eling & lehmann, 2018). furthermore, this study aimed to thoroughly analyse the benefits and difficulties associated with the fintech landscape’s digitization of the insurance sector, with a particular emphasis on qatar. the study used a grounded conceptual framework to examine several aspects, such as the adoption of technology, legal frameworks, cybersecurity nuances, market dynamics, and the development of digital payment systems. the main objective is to give policymakers, academics, and stakeholders an enlightened perspective on the changing environment of digital insurance. through this, the study has made a significant contribution to the constantly changing fintech and digital insurance sector. given that the insurance business growth in qatar, it is critical to comprehend the prospects and obstacles in this field. policymakers may use the results to develop regulatory frameworks, industry participants can use the insights to make strategic decisions, and scholars can add to the academic discussion on digitalization’s disruptive potential in the insurance business. literature review global fintech acceleration and qatar the worldwide fintech business has grown at an exponential rate over the last decade, with investments reaching $34.5 billion by the end of 2019. the middle east and north africa (mena) area, on the other hand, accounted for less than 1% of worldwide investments (pant, 2020). the covid-19 epidemic has, therefore, expedited the global financial institutions’ digital transition and laid the groundwork for digital growth in the middle east and north africa (mena). the mena area, which includes qatar, has seen a notable shift towards online commercial activity and contactless payments throughout the covid and post-covid periods (abidi et al., 2022). financial regulators in developing fintech countries, such as qatar, have taken a proactive approach to fintech regulation, assuring consumer safety and financial system stability (sadigov et al., 2020). in addition, the growing dependence on digital platforms has sparked worries about cyber security threats (lallie et al., 2021), driving up cyber security expenditures and necessitating coordinated plans to drive digital transformation aggressively (wilson, 2020). furthermore, qatar has advanced significantly in creating a strong fintech environment. the national fintech strategy of qatar was released in december 2019 and was based on the work of the national fintech taskforce that was formed in 2017 (hub, 2021). through support from the qatar central bank (qcb), the plan calls for the creation of the qatar fintech hub (qfth) in april 2020. applications for qfth’s incubator and accelerator programs come from all around the world. additionally, the qcb formed its fintech office, whose duties include enacting rules and carrying out fintech goals (khan et al., 2023). consequently, qatar has an intriguing opportunity to harness its islamic financial knowledge and gain a competitive advantage in islamic technology. opportunities for qatar to flourish in the islamic fintech sector include shariah-compliant venture capital investments and islamic regtech. qatar might encourage islamic venture capital investments in fintechs by providing incentives and collaborating with islamic investment banks (muneeza & mustapha, 2021). additionally, alternative lenders and internet banks can reach the sme sector by offering specialized financial facilities and affordable small company loans (gopal & schnabl, 2022). in addition, the expanding e-commerce market in qatar presents opportunities for providers of payment solutions to meet the growing need for contactless and digital payment gateways (haron, 2016). with the ability to conduct banking, make payments, and send money without requiring a traditional bank account, digital wallets can also benefit low-income workers who are underprivileged or haven’t financed at all (hassan & shukur, 2019). nonetheless, sustained endeavors in regulatory structures, cybersecurity protocols, and global partnerships will be imperative for the expeditious advancement of fintech in qatar. digitalization in the insurance sector the covid-19 crisis has catapulted the insurance sector into a critical stage of digital transformation, elevating digitization from a strategic choice to an absolute must. the insurance industry is seeing a transformation in its conventional business structures and value chain due to digitization (cherqaoui, 2021). prior to the pandemic, the shift to digital technology was underway, with an emphasis on online distribution channels and customer-centric strategies. advanced digital technologies, however, aim to enhance market dynamics and competitiveness through openness, comparability, reduced transaction costs, and the expanded reach of online platforms, going beyond efficiency advantages (aidrous et al., 2021). m. eling and m. lehmann assert that the effects of digitization may be seen at every stage of the value chain, from sales and customer service to claim reporting (eling & lehmann, 2018). furthermore, online platforms and aggregator tools have changed the power balance, allowing customers to access information and evaluate items independently. conversely, risk assessment, product innovation, and claims processing have seen substantial changes as a result of digital technologies, including telematics devices, big data analytics, and smart contracts (doss, 2020). the obstacles associated with the insurance sector’s digitalization include pa ge 46 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 the requirement for large-scale, unstructured dataset analysis, ethical and regulatory issues surrounding the use of big data, and the introduction of new and current insurance products (cappiello & cappiello, 2018; eckert & osterrieder, 2020). the move towards on-demand insurance markets, cyber risk insurance, and telematics insurance is indicative of how digitalization can change industries completely. to guarantee sustainable growth in the digital age, issues with data privacy, cybersecurity, and the moral use of consumer information must be resolved (bohnsack et al., 2022). swiss re’s world insurance report projects that by the end of 2022, insurance premiums reached $8.89 trillion globally, offering a substantial opportunity for insurance companies operating around the globe (bohnsack et al., 2022). the worldwide covid-19 epidemic has expedited insurers’ digital transition, elevating insurtech firms to the fore. the insurance market in qatar is projected to develop at a compound annual growth rate (cagr) of 4.7% from 2021 to 2026 when it is expected to reach $1.9 billion (eckert & osterrieder, 2020). in an effort to improve operational effectiveness, qatar insurance company (qic) has also created insurtech platforms and subsidiaries. the anoud+ platform is provided by anoud technologies, a qic company that provides it services with headquarters in qfc. it includes a variety of features, including reinsurance administration and customer relationship management. qic’s endeavors, such as organizing an insurtech hackathon and introducing a comprehensive online platform for insurance policies, exhibit a dedication to promoting creativity within the insurtech domain (sharar & earley, 2018). furthermore, to facilitate insurtech businesses’ entry into the qatari market, the qatar financial centre (qfc), qatar fintech hub (qfth), and qic work together. these organizations organize seminars and activities that highlight insurtech’s potential in qatar’s finance scene. qatar has substantial development potential for insurtech businesses, given its 1% insurance penetration rate. this may be attributed to many factors, such as rising consumer awareness, regulatory laws that facilitate growth, and government initiatives delineated in the national fintech strategy (lynn et al., 2019). the global insurance landscape is changing due to the digitalization of the insurance business, with insurtech being a key player in this change. driven by factors including internet penetration, technological acceptance, and government assistance, qatar’s emerging insurtech business is primed for tremendous development. to guarantee the sustainable expansion of insurtech in qatar and throughout the world, the critical evaluation highlights the necessity to address issues with data privacy, cybersecurity, and ethical considerations (xu & zweifel, 2020). use-cases and challenges of digitization in the insurance sector the insurance sector is currently experiencing a digital transition that presents a range of possibilities and difficulties for industry participants. the insurance industry is becoming more digitally connected, but there are drawbacks as well (cappiello & cappiello, 2018; svetlana, 2016). for example, there is a need to integrate big data and artificial intelligence (ai), two important technical enablers. order to transform consumer relationships, boost operational effectiveness, and change the competitive environment, it also entails digging into important numerical data. its capabilities for gathering, processing, and evaluating vast volumes of client data highlight big data’s importance for insurers (nguyen et al., 2023). the value of capital invested in insurance tech startups, for example, increased significantly globally throughout 2012–2017, from $326 million to $2.134 billion, and the number of acquisitions increased from 86 to 247 (statista, 2019b; nicoletti & nicoletti, 2021). according to eling and lehmann (2018), big data analytics improves insurers’ comprehension of their clientele by offering insightful information gleaned from semi-structured and unstructured data, including social media. this capacity is essential for producing insights and enhancing decisionmaking procedures (eling & lehmann, 2018). moreover, the utilization of artificial intelligence, including machine learning and deep learning, is crucial in obtaining advantages from large and customer data sets. digitalization of insurance is receiving significant attention from insurance businesses, as seen by the rapid rise in global expenditures on machine learning algorithms and their iterative training procedures (łyskawa et al., 2019). according to brenner (2019), artificial intelligence (ai) gives insurers tools for predictive analytics, such as extreme gradient boosting approaches and helps them better segment their consumer base (brenner et al., 2022). consequently, insurers employ ai and big data for an array of applications to improve client experiences and optimize workflows. authorization procedures are made simpler by facial recognition, which improves client satisfaction in both sales and service. by automatically analyzing photographs, claims management uses image recognition to speed up operations. insurance companies may evaluate consumer emotions during interactions by using convolutional neural networks for emotion identification, which leads to increased customer engagement (pomazan et al., 2023). however, identifying a single metric for gauging digitalization in insurance firms remains a difficulty, even in light of the sharp increase in worldwide expenditures in this area. bohnert, fritzsche, and gregor (2019) state that insurance firms are investigating new avenues in information technology, including drones, satellites, telematics, voice biometrics, big data analytics, omni-channels, and the internet of things (bohnert et al., 2019). both revolutionary possibilities and difficulties are brought about by the insurance industry’s overall digitalization, which is fueled by big data and ai. the use cases that were presented highlight the potential advantages of digitalization, and these include client pa ge 47 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 segmentation, robo-advisory, and enhanced consumer experiences (eckert & osterrieder, 2020). for digital technologies to be used in the insurance industry in a way that is both sustainable and successful, issues like the absence of standardized procedures must be addressed (bandyopadhyay & sen, 2011). the data presented suggests that there is a strong and increasing emphasis on the transformative potential of digital technology within the insurance business. global patterns in insurance digitization are noteworthy (hanafizadeh & amin, 2023). literature gap the presented literature analysis provided an in-depth analysis of global fintech and insurance digitalization trends, with an emphasis on qatar (göll & zwiers, 2018). the lack of a synthesized conceptual framework that incorporates pertinent theories to methodically analyze the potential and difficulties posed in the digital insurance sector, however, creates a significant vacuum in the literature. closing this gap is essential to a systematic, theoretical investigation of the topic. conceptual framework the study develops an integrated conceptual model drawing from several established theories to both examine challenges and opportunities within the emerging digital insurance sector as well as guide survey design. however, an overreliance on conceptual abstraction risks limiting the model’s practical relevance and ability to generate actionable insights (johnson et al., 2019). the study applies rogers’ diffusion of innovations theory and the technology acceptance model to analyze customer and insurer adoption of digital technologies. while these provide a foundational understanding of adoption drivers, they may only partially capture disruption in immature industries (ching et al., 2020). furthermore, porter’s five forces is used to assess competitive dynamics but could overlook issues in rapidly evolving digital landscapes (ching et al., 2020). the resource-based view helps identify capabilities for competitive advantage but also struggles with dynamic markets (zahra, 2021). regulatory compliance theories and the toe framework aim to understand compliance difficulties and cybersecurity measures’ organizational/ environmental aspects (zahra, 2021). however, more than these may be needed to anticipate the regulation of novel technologies or future threats. lastly, financial inclusion and ethical decision-making theories also inform related opportunities and challenges. while useful baseline perspectives, such established theories alone need to be more incremental to guide disruption. overall, leveraging diverse conceptual frameworks offers a structured starting point. however, overreliance on established theories could constrain the model’s capacity to explore truly disruptive scenarios and generate pragmatic strategy recommendations for navigating ongoing transformation in this complex, uncertain industry domain (zahra, 2021). research design the study adopts a quantitative research design to investigate the opportunities and challenges in developing the insurance industry through digitalization in the context of fintech. a positivist philosophical perspective guides the research, utilizing a deductive approach for hypothesis testing and drawing precise conclusions (casula et al., 2021). the chosen quantitative methodology ensures objectivity and facilitates the collection of extensive data from a large population (casula et al., 2021). data collection primary data is collected through the distribution of structured survey questionnaires among digital insurance professionals in qatar. the survey employs a 5-point likert scale, ranging from “strongly agree” to “strongly disagree,” to gauge perspectives on the opportunities and challenges associated with fintech and digitalization in the insurance sector (kimberly et al., 2022). the survey methodology is cost-effective, enabling the collection of diverse opinions from a broad population, thereby minimizing bias in the outcomes (nayak & narayan, 2019). sampling and analysis a purposive sampling technique, a non-probability approach, is employed to select participants with expertise in digital insurance and fintech within the qatari context (tohang et al., 2021). the sample comprises 264 digital insurance professionals, out of which 250 participants’ responses were selected for final analysis, ensuring participants possess relevant knowledge and competence related to the study’s subject. spss software is utilized for statistical analysis, encompassing descriptive statistics, correlation analysis, and regression analysis to explore variable relationships and test hypotheses (manzoor et al., 2019). ethical considerations, including confidentiality and informed consent, are strictly followed throughout the research process (kang & hwang, 2021). the final dataset ensures a robust and representative sample for drawing meaningful conclusions regarding the impact of fintech on the digital insurance landscape in qatar. results and discussion results demographics the results are illustrated in figure 1. shows the gender distribution indicates a predominantly male representation (70.5%), while females make up 29.1%. in terms of professional experience, the majority fall within the 3-5 years category (43.8%), followed by 6-7 years (23.5%), 1-2 years (19.1%), and more than 8 years (13.1%). these demographics highlight a diverse range of experience levels within the digital insurance industry, providing valuable insights from professionals with varying tenures in qatar’s insurance sector. pa ge 48 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 compliance and digital integration in qatar’s insurance sector figure 2. illustrated survey responses regarding regulatory compliance’s role in the growth of the digital insurance industry in qatar, a noteworthy 64% of participants agree or strongly agree that insurance companies adhere to digital practices as per regulatory frameworks. moreover, a substantial 71.6% believe that regulatory compliance significantly influences decisionmaking processes for digital technology initiatives in the industry. additionally, 74.4% of respondents acknowledge a good understanding of digital regulations imposed by authorities among insurance professionals, while 62.8% perceive that regulatory compliance enhances the credibility of digital insurance services. these findings emphasize the pivotal role of regulatory frameworks in shaping and enhancing digital practices within qatar’s insurance landscape. figure 1: demographics figure 2: compliance and digital integration pa ge 49 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 building trust through data security and ethical practices in qatar’s insurance industry the results are presented in figure 3. showing the participant’s perceptions on data security and ethical practices, it is evident that a majority (76.8%) strongly agree or agree that insurance companies in qatar prioritize robust cybersecurity measures. however, concerns arise regarding transparency, with only 14% strongly agreeing that there is transparency in how customer data is handled and 72.8% agreeing. moreover, 74% of participants emphasize a strong industry emphasis on ethical considerations in digital practices, while 77.2% believe that ethical practices positively impact customer trust in digital insurance. these results highlight the imperative need for enhancing transparency in data handling processes to foster trust, even as ethical considerations are acknowledged as pivotal in building trust in qatar’s digital insurance landscape. figure 3: data security and ethical practices in qatar’s insurance industry market dynamics and digital competitiveness figure 4. illustrated the impact of market dynamics and competitiveness in qatar’s insurance sector, which reveals a positive perception toward digitalization. a significant portion (74%) believe that digitalization enhances market competitiveness, emphasizing a transformative influence. furthermore, 65.1% strongly agree or agree that insurance companies actively leverage digital technologies for a competitive edge, showcasing an industry-wide commitment to digital innovation. participants (58%) strongly agree that market dynamics have fundamentally changed due to digital transformation, highlighting the profound impact on the insurance landscape. however, respondents express some reservations, with 90.8% acknowledging the need for further exploration of how digital tools enable the delivery of unique and innovative services. these findings underline the industry’s evolving nature and the ongoing quest for innovative digital solutions. pa ge 50 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 influence the bargaining power of both customers and suppliers, indicating an intricate relationship with stakeholders. additionally, 67.2% recognize digital resources as contributors to a competitive advantage in the digital insurance market. however, there are reservations, with 88.8% neutrally or affirmatively agreeing that digital advancements pose barriers for new entrants. these results underscore the nuanced dynamics of digitalization, showcasing both its potential benefits and challenges in fostering competitive environments. navigating competitive realities in qatar’s insurance sphere figure 5 shows insight into participant perceptions on the impact of digital advancement and increasing competitive rivalry in qatar’s insurance sector and unveils insights into industry perceptions. a significant majority (56.8%) acknowledge that digitalization has a substantial impact on competitive rivalry, reflecting an awareness of the transformative effects of digital technologies. respondents (58.8%) believe that digital technologies figure 4: market dynamics and digital competitiveness pa ge 51 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 co-relation analysis the correlation analysis reveals strong and significant relationships among key variables in qatar’s digital insurance landscape. challenges and adoption of digitization exhibit positive correlations with the impact of regulatory frameworks (r = 0.443, p < 0.01) and competitive rivalry in the digital insurance sector (r = 0.717, p < 0.01). notably, security challenges for digitization are strongly correlated with challenges and adoption (r = 0.516, p < 0.01) and competitive rivalry (r = 0.719, p < 0.01). furthermore, competitive rivalry shows a significant positive correlation with market competitiveness (r = 0.602, p < 0.01). these robust correlations underscore the interconnectedness and interdependence of factors influencing the digital insurance landscape in qatar. figure 5: navigating competitive realities in qatar’s insurance sphere table 1: correlations c ha lle ng es a nd a do pt io n of d ig iti za tio n im pa ct o f r eg ul at or y fr am ew or ks se cu ri ty c ha lla ng es f or d ig iti za tio n c om pe tit iv e r iv al ry a nd e nh an ce d d ig ita l in su ra nc e se ct or m ar ke t c om pe tit iv en es s challenges and adoption of digitization 1 .443** .516** .717** .429** 250 250 250 250 250 impact of regulatory frameworks .443** 1 .270** .189** .228** sig. (2-tailed) .000 .000 .003 .000 n 250 250 250 250 250 security challanges for digitization .516** .270** 1 .719** .811** sig. (2-tailed) .000 .000 .000 .000 n 250 250 250 250 250 competitive rivalry and enhanced digital insurance sector pearson correlation .717** .189** .719** 1 .602** sig. (2-tailed) .000 .003 .000 .000 n 250 250 250 250 250 market competitiveness pearson correlation .429** .228** .811** .602** 1 sig. (2-tailed) .000 .000 .000 .000 n 250 250 250 250 250 ** correlation is significant at the 0.01 level (2-tailed). model fit the model demonstrates a good fit, with a substantial r-square value of 0.617, indicating that approximately 61.7% of the variance in challenges and adoption of digitization is explained by the included predictors, as presented in tables 2,3 and 4. the anova results are pa ge 52 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 highly significant (p < 0.001), suggesting that the model is a meaningful improvement over the null model. the reliability statistics reveal a cronbach’s alpha of 0.823, indicating a high level of internal consistency among the 20 items, enhancing the overall reliability of the model. table 2: reliability model variables entered variables removed method 1 market competitiveness, impact of regulatory frameworks, competitive rivalry and enhanced digital insurance sector, security challanges for digitizationb . enter a. dependent variable: challenges and adoption of digitization b. all requested variables entered. table 3: model summary model r r square adjusted r square std. error of the estimate change statistics r square change f change df1 df2 sig. f change 1 .785a .617 .610 .448 .617 98.496 4 245 .000 a. predictors: (constant), market competitiveness, impact of regulatory frameworks, competitive rivalry and enhanced digital insurance sector, security challanges for digitization table 4: anovaa model sum of squares df mean square f sig. 1 regression 79.214 4 19.804 98.496 .000b residual 49.260 245 .201 total 128.474 249 a. dependent variable: challenges and adoption of digitization b. predictors: (constant), market competitiveness, impact of regulatory frameworks, competitive rivalry and enhanced digital insurance sector, security challanges for digitization regression analysis the regression analysis is in table 5. indicates that the model is statistically significant (f(4, 245) = 98.496, p < 0.001), suggesting that the included predictors collectively contribute to explaining the variance in challenges and adoption of digitization. among the predictors, “competitive rivalry and enhanced digital insurance sector” has the most substantial impact (beta = 0.721, p < 0.001), followed by “impact of regulatory frameworks” (beta = 0.331, p < 0.001). however, “security challenges for digitization” and “market competitiveness” do not significantly contribute. the constant term is not significant (p = 0.090). overall, the model underscores the importance of competitive dynamics and regulatory influence in shaping digitization challenges in the insurance sector. table 5: coefficientsa model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) -.254 .149 -1.701 .090 impact of regulatory frameworks .474 .059 .331 8.064 .000 security challanges for digitization -.068 .073 -.074 -.942 .347 competitive rivalry and enhanced digital insurance sector .651 .051 .721 12.653 .000 market competitiveness -.023 .073 -.021 -.314 .754 a. dependent variable: challenges_and_adoption_of_digitization discussion the study’s conceptual framework, grounded in established theories, provides a robust foundation for exploring the complexities of the digital insurance sector in qatar. drawing on technological acceptance and innovation adoption theories, the framework illuminates how customers and insurers embrace digital technology, emphasizing strategic innovation opportunities and pa ge 53 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 addressing resistance factors (schilling, 2013). regulatory environment and compliance variables are integrated to comprehend the impact of regulatory frameworks on industry practices, allowing insurers to conform to changes proactively (grima et al., 2020). cybersecurity and ethical considerations, informed by the technology, organization, and environment (toe) framework, tackle data privacy and ethical digital behavior, which is essential for sustainable growth (ullah et al., 2021). market dynamics and competitive strategies are incorporated into the survey to analyze competitive dynamics and identify internal capabilities for a digital edge (wang & gao, 2021). this comprehensive assessment set enables us to empirically validate, enhance, or expand these theoretical underpinnings. furthermore, the study adopted a quantitative approach, aligning with a positivist paradigm and employing deductive reasoning as its research design. this method ensures objectivity and extensive data collection from digital insurance professionals in qatar (al-qurashi, 2017). the study has utilized a diverse sample, enriching the study’s insights with varying experience levels within qatar’s digital insurance industry (maouchi et al., 2022). the findings on compliance underscore the industry’s adherence to digital practices and the pivotal role of regulatory frameworks in shaping digital practices (maouchi et al., 2022). furthermore, examining data security and ethical practices, the majority recognize the industry’s emphasis on robust cybersecurity measures is positive. however, concerns about transparency in customer data handling reveal a critical need for improvement. the positive correlation between ethical practices and customer trust underscores their interconnectedness, emphasizing the imperative for transparent data processes in fostering trust (halim et al., 2023). the study findings also reflect that market dynamics and digital competitiveness reflect a positive industry outlook on digitalization, highlighting its transformative influence (halim et al., 2023). acknowledging the need for further exploration indicates an industry aware of ongoing challenges and committed to innovative solutions; navigating competitive realities reveals industry awareness of digitalization’s substantial impact on rivalry, bargaining power, and competitive advantage. however, concerns about barriers for new entrants signal a nuanced understanding of digital advancements’ potential challenges (nicoletti, 2020). the positive correlations identified in the study between challenges and the adoption of digitization, the impact of regulatory frameworks, and competitive rivalry are consistent with kimwaki’s work, underscoring the intertwined dynamics in the digitalization landscape. these findings reinforce the idea that challenges in adopting digitization are not isolated but influenced by regulatory frameworks and the competitive environment (kimwaki, 2023). ibrahim et al. (2021) suggest that stringent regulatory frameworks can both catalyze and hinder digital adoption(ibrahim & trubyjon, 2021). the significant correlation between challenges and regulatory impact (r = 0.443, p < 0.01) suggests that navigating regulatory complexities is a substantial hurdle in the digitalization journey. this aligns with studies emphasizing the pivotal role of regulatory environments in shaping organizational practices during technological transitions(knight & wójcik, 2018; park & kim, 2020). however, the lack of significance for security challenges and market competitiveness in the regression analysis prompts further exploration. while security challenges may not have a direct impact on digitization challenges in the model, their real-world importance is well-documented (kshetri, 2018). future research should delve into the nuanced ways security concerns influence digitalization strategies. moreover, the non-significant contribution of market competitiveness in the model contradicts existing literature on the transformative impact of digital strategies on market dynamics (ali et al., 2023; rodríguezespíndola et al., 2022). this discrepancy necessitates a more nuanced investigation into the specific market conditions influencing the adoption of digital initiatives within the insurance sector. critically assessing the findings, the positive correlation between challenges and adoption of digitization, impact of regulatory frameworks (r = 0.443, p < 0.01), and competitive rivalry (r = 0.717, p < 0.01) aligns with literature highlighting the interconnected nature of these factors in digitalization processes (demeter et al., 2023; kumar & bhatia, 2021). however, the lack of significance for security challenges and market competitiveness in the regression analysis calls for a nuanced exploration of these aspects in future research, considering their potential significance in realworld scenarios. the high r-square value indicates a substantial proportion of variance in challenges and adoption of digitization is explained by the included predictors, suggesting the model’s reliability. nonetheless, the study could benefit from further exploration of additional variables, such as customer perceptions and external market forces, to enhance its explanatory power. the research design, while robust, could strengthen its theoretical grounding by incorporating qualitative elements to provide a more holistic understanding in future research (sreedharan v & saha, 2021; wiegard & breitner, 2019). the survey methodology’s reliance on self-reporting introduces potential biases, necessitating cautious interpretation of the results. future studies could employ mixed-methods approaches for a more comprehensive exploration of the digital insurance landscape (kirkpatrick et al., 2019). overall, the study’s findings contribute valuable insights into the digital insurance sector in qatar. the positive industry outlook, coupled with identified challenges, provides a nuanced understanding of the dynamic digital landscape (ghosh et al., 2022). based on the study results, transparency improvements, further exploration of digital tools, and addressing barriers for new entrants should pa ge 54 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 44-56, 2024 be prioritized by industry stakeholders in navigating the evolving terrain. building on this foundation, future research could delve deeper into customer perceptions, emerging technologies, and the interplay of external market forces. conclusion in conclusion, this study addressed a critical l gap by providing a synthesized conceptual framework that incorporates established theories to comprehensively examine the complexities of the digital insurance sector in qatar. the adoption of a quantitative research design and a robust theoretical foundation allowed for a nuanced exploration of challenges and opportunities. the high r-square value attests to the model’s reliability, revealing that approximately 61.7% of the variance in challenges and adoption of digitization is explained by the included predictors. the positive correlations among challenges, regulatory impact, and competitive rivalry align with existing literature, emphasizing the interconnected nature of these factors. however, the non-significant contribution of security challenges and market competitiveness calls for further exploration in future research. the study’s findings offer valuable insights for industry stakeholders, guiding efforts to enhance transparency, explore digital tools, and address barriers for new entrants in the everevolving digital insurance landscape in qatar. references abidi, n., el herradi, m., & sakha, s. 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(2021). the resource-based view, resourcefulness, and resource management in startup firms: a proposed research agenda. journal of management, 47(7), 1841-1860. pa ge 1 pa ge 35 american journal of financial technology and innovation (ajfti) financial technology and economic growth in nigeria: 2012q1-2022q4 obumneke ezie1*, jonathan oniore1, princewill chisom ajaegbu1 volume 1 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: november 25, 2023 accepted: december 23, 2023 published: december 26, 2023 in the recent decade, the surge in financial technology (fintech) has dramatically reshaped the financial landscape of many countries, including nigeria. however, despite these developments, the economy continues to slide in a state of decline. the primary objective of this study was to investigate the impact of financial technology on nigeria’s economic growth spanning 2012q1 to 2022q4. using the fully modified ordinary least squares (fmols) technique, the research focused on three aspects of fintech: internet (web) transactions, mobile payment transactions, and instant pay transactions. the findings were enlightening. web-based transactions showed a significant enhancement in nigeria’s economic growth. these transactions not only simplified banking processes but also bolstered economic activities, making financial services more accessible to a wider demographic. in addition, mobile payment transactions significantly impacted economic growth, acting as catalysts in spurring both urban and rural economic activities and inclusivity. on the other hand, instant pay transactions displayed a rather unexpected negative impact on economic growth within the study period. while they augmented financial fluidity, they exhibited a negative, yet statistically significant, impact on overall economic growth. drawing from these insights, specific recommendations were provided. for web transactions, the paramount focus was suggested on fortifying the digital infrastructure and enhancing cybersecurity measures. this twofold approach would foster growth and shield the burgeoning digital economy from cyber threats. with their transformative essence, mobile transactions necessitated a dual strategy: expanding mobile connectivity, especially in underserved areas, and intensifying user education on safe mobile transactional practices. given its negative relationship with economic growth, instant pay transactions required a more proactive approach. an exhaustive infrastructure audit was advocated to expose any underlying inefficiencies. simultaneously, a call for more stringent regulatory oversight on instant pay transactions was made to align it more harmoniously with the nation’s broader economic objectives keywords internet (web) transactions, mobile payment transactions, instant pay transactions, fmols, economic growth, jel codes l86, g23, g22 and o42 introduction financial technology, popularly known as fintech, refers to integrating technology into financial services to enhance their delivery, reduce costs, and create new business models. from peer-to-peer lending platforms to digital-only banks, fintech innovations have revolutionized the way people interact with their finances globally. according to mckinsey & company (2018), fintech investments reached $111 billion globally, a testament to its rapid ascent. its significance lies not only in the volumes but in its potential to democratize access to financial services, especially in regions where traditional banking has been out of reach for many. originally serving as software support systems for traditional banking institutions, fintech has transformed into a robust industry, offering a wide range of financial services, from peer-to-peer lending platforms to automated wealth management solutions (schueffel, 2017). with the global transaction value of digital payments projected to exceed $6.6 trillion in 2021 (statista, 2021), instant pay transactions have become a central feature in the fintech landscape. this mode of payment assures real-time transfers and minimizes transaction costs, augmenting economic efficiency. when leveraged adequately, such transactions can expedite the velocity of money, enhancing economic activity. at its core, fintech encompasses a broad range of financial operations, including instant pay transactions, internet (web) transactions, and mobile payment transactions. instant pay transactions have surged in nigeria. per the nigerian inter-bank settlement system (nibss), these transactions rose from just a few million in 2012 to over 1.5 billion transactions worth 83.89 trillion naira in 2019, indicating increasing adoption (nibss, 2020). this drastic shift signifies the growing acceptance of fintech and points to its positive impact on economic growth by increasing transaction velocity and facilitating commerce. equally transformative is the rise of internet (web) transactions. e-commerce platforms like jumia and konga, supported by secure web transaction systems, have seen exponential growth. according to statista (2020), the nigerian e-commerce market was valued at over $12 billion in 2019, with projections of further significant growth, all enabled by the robust web transaction infrastructure underpinning the fintech ecosystem. mobile payment transactions in nigeria present another intriguing case study. with the proliferation of mobile phones, over 100 million nigerians can access mobile services (nigerian communications commission, 2020). 1 department of economics, bingham university, karu, nasarawa state, nigeria * corresponding author’s e-mail: eobumneke@yahoo.com pa ge 36 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 this widespread access has been leveraged by fintech startups like flutterwave and paystack to provide mobilebased payment solutions. the convenience of mobile money, like mtn’s momo and airtel money, means that even those without a traditional bank account can now participate in the economy, make transactions, save, or even access loans. the central bank of nigeria reported that mobile payment transactions rose from a mere 2 billion naira in 2012 to an impressive 5 trillion naira in 2019 (central bank of nigeria, 2020). achieving inclusive economic growth and sustainable development has remained one of the core macroeconomic objectives of all economies irrespective of the level of development. the relationship between financial development and growth has since remained topical issue in the finance literature. nigeria, often referred to as the “giant of africa” due to its vast population and rich natural resources, has an economy that has undergone significant transformation in the last decade. historically dependent on oil, efforts have been made to diversify the economy with increased focus on sectors like agriculture, telecommunications, and services. the world bank (2023) reports that while nigeria’s gdp stood at $445.12 billion in 2022, an underlying issue has been the limited access to financial services for a significant portion of its nearly 200 million populace. with only about 60% of nigerian adults having access to formal financial services as of 2018, according to the central bank of nigeria, there is a vast untapped potential that fintech promises to unlock. despite nigeria’s status as africa’s largest economy, with a gdp surpassing $450 billion, its annual growth rates have often lagged, averaging around 2.2% in the last decade (imf, 2021). while gdp growth rate for 2021 was 3.40%, a 5.44% increase from 2020. gdp growth rate for 2020 was -1.79%, a 4% decline from 2019. nigeria gdp growth rate for 2019 was 2.21%, a 0.29% increase from 2018. this sluggish growth has been accompanied by persistently high unemployment rates and a widening wealth gap. this paradoxical scenario, where a robust economic structure coexists with subdued growth rates, raises pressing questions about the efficacy of interventions. therefore, given the universally acknowledged potential of fintech as a catalyst for economic growth, the underwhelming performance of nigerian economy is disturbing, and as such it is of interest in this study to investigate the impact of financial technology on economic growth in nigeria between 2012q1 and 2022q4. the study addressed the following research objectives, and they are to: i. examine the impact of instant pay transactions on economic growth in nigeria ii. analyse the impact of internet (web) transactions on economic growth in nigeria iii. evaluate the impact of mobile payment transactions on economic growth in nigeria based on the highlighted specific objectives, the following hypotheses were raised and tested: h01: instant pay transactions has no significant impact on economic growth in nigeria h02: internet (web) transactions has not significantly enhanced nigeria’s economic growth h03: mobile payment transactions has no significant impact on nigeria’s economic growth literature review conceptual review financial technology financial technology, commonly abbreviated as fintech, is a term that has garnered significant attention in both academic and industry circles in recent years, resulting in a plethora of definitions and interpretations. at its core, fintech intertwines the realms of finance and technology, marking a departure from traditional financial systems and methodologies in favor of innovative, technologydriven solutions. arner, barberis, and buckley (2016) offer a comprehensive perspective on fintech by characterizing it as “a new financial industry that applies technology to improve financial activities.” in their view, fintech represents the evolution and transformation of the financial sector, where technology acts as a pivotal force in reshaping financial services. it is not just about digitizing money but about monetizing data. it seeks to optimize the delivery of financial services, making them more efficient, convenient, and widely accessible. zavolokina, dolata, and schwabe (2016) delve deeper into the constituent elements of fintech, suggesting that it “is a phenomenon driven by technology and user expectations which leads to a disruption of the current financial markets, touching upon its various sectors.” this definition accentuates the disruptive nature of fintech. unlike mere technological upgrades in financial services, fintech introduces entirely new paradigms, models, and business processes that challenge and often supplant established norms. another enlightening viewpoint is provided by lee and shin (2018), who perceive fintech as “a fusion of advanced information technologies and financial services, leading to the creation of new business models, applications, processes, and products.” the essence of this definition lies in the notion of fusion. fintech isn’t just about juxtaposing technology and finance; it’s about the synthesis of the two, where the boundaries between finance and technology blur, giving birth to hybrid entities that redefine financial intermediation. schueffel (2017) synthesizes various definitions and describes fintech as “the new marriage of financial services and information technology.” in this union, it is not merely the technological facet that stands out, but the profound implications of this union on the industry’s structure, competition, and value proposition. the implications span across sectors, influencing everything from banking to insurance to investment. it’s noteworthy to mention that these definitions, while pa ge 37 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 varying in their differences, converge on the central theme of fintech being a transformative force. this transformation is not just limited to the digitization of financial transactions but extends to altering the very fabric of the financial ecosystem, redefining the way stakeholders interact, and crafting novel avenues of value creation. in its core, fintech encompasses a broad range of financial operations, including instant pay transactions, internet (web) transactions, and mobile payment transactions. instant pay transactions (ipt) instant pay transactions signify real-time or near-realtime transfer of funds between two parties without any significant delay. unlike traditional banking systems where transfer of funds might require hours or even days, instant pay ensures the immediate availability of funds. zhang, guo, and chen (2016) elucidate ipt as “a financial transaction system that processes funds transfer and ensures the settlement of payments within seconds.” the allure of ipt lies not only in its speed but also in its ability to provide 24/7 financial services, breaking away from the time constraints of conventional banking hours. internet (web) transactions internet or web transactions denote the array of financial transactions conducted over the internet. these transactions encompass a wide range of activities, from online shopping to securities trading, enabled through web-based platforms. choi, stahl, and whinston (2017) expound on this by stating that “internet transactions are electronic transactions that occur over the internet, facilitating trade of goods or services.” such transactions have revolutionized commerce, with global e-commerce sales soaring to $4.2 trillion in 2020 (statista, 2021). the essence of internet transactions is the convergence of convenience, reach, and variety, empowering consumers with unparalleled choices and capabilities. mobile payment transactions mobile payment transactions are financial transactions initiated, executed, and confirmed using mobile devices, primarily smartphones and tablets. they offer a new horizon of financial inclusion, especially in regions with limited banking infrastructure. ondrus and lyytinen (2015) define mobile payment transactions as “transactions involving monetary value in exchange for goods, services, or even as a transfer, executed using mobile technology.” these transactions can range from paying for a coffee using a mobile wallet to transferring money across borders using a mobile banking application. by 2019, over 2 billion individuals worldwide used mobile payments, signifying their growing ubiquity and importance (gsma, 2020). economic growth economic growth stands as one of the most widely deliberated and researched concepts in the realm of economics, public policy, and development studies. at its core, economic growth pertains to the increase in the output of goods and services in an economy over time, typically measured by the rise in the gross domestic product (gdp) or gross national product (gnp). one seminal definition comes from solow (1956), who postulates economic growth as “the long-term rise in the capacity to supply increasingly diverse economic goods to its population, based on advancing technology and the institutional and ideological adjustments that it demands.” this viewpoint emphasizes the role of technological advancements and the concomitant societal and institutional changes, portraying economic growth as a multidimensional and dynamic process. lucas (1988) presents a more human-centric perspective, underscoring the role of human capital in fueling economic growth. he defines it as “the sustained, longterm augmentation in the living standards and material well-being of the populace, predominantly driven by accumulated skills, knowledge, and expertise.” in lucas’s interpretation, the cognitive and skill enhancement of a nation’s citizens is paramount, serving as a key driver of increased productivity and, subsequently, economic growth. barro (1991) introduces a holistic view of economic growth, suggesting that it’s “a composite outcome influenced by factors such as physical capital accumulation, human capital development, technological progress, and the macroeconomic environment.” this perspective recognizes the intricate determinants shaping economic growth, from tangible assets like infrastructure to intangible facets like governance and policy frameworks. more recent scholarship, exemplified by the work of acemoglu and robinson (2012), delves into the institutional foundations of growth. they opine that economic growth is “deeply intertwined with the inclusivity and structure of a nation’s institutions – both economic and political.” in their widely acclaimed work, they argue that nations with inclusive, democratic, and robust institutions are more likely to experience sustained economic growth compared to those with extractive, authoritarian regimes. theoretical review innovation diffusion theory financial technology (fintech) has been a burgeoning field of study, with numerous theories attempting to elucidate its implications on global finance. among these, the “innovation diffusion theory” offers valuable insights. originally conceived by everett rogers in 1962 in his seminal work “diffusion of innovations,” this theory explores how, why, and at what rate new ideas and technology spread through cultures. rogers’ theory doesn’t exclusively focus on fintech; however, its applicability to the sector is both timely and profound. rogers (1962) posited that innovations disseminate through societies in an s-shaped curve, beginning with pa ge 38 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 innovators, then early adopters, the early majority, the late majority, and finally, the laggards. the theory encapsulates factors determining the rate of adoption, including relative advantage, compatibility, complexity, trialability, and observability. in the context of fintech, this suggests that financial technologies, despite their inherent advantages, would still face varying rates of adoption based on their perceived benefits, ease of use, and alignment with existing systems and cultural values. the strength of rogers’ theory lies in its comprehensive framework. it aptly explains the variegated adoption rates of fintech solutions across different countries and demographics. for instance, mobile money services like m-pesa saw rapid adoption in kenya due to its immediate perceived relative advantage for the unbanked populace. the theory also underscores the importance of early adopters – a group crucial for fintech startups seeking market penetration and influence. however, the “innovation diffusion theory” isn’t without criticisms. some scholars argue that it’s too linear and deterministic, not sufficiently accounting for the dynamic feedback loops often present in the adoption of innovations (wolfram, 2016). others posit that the theory, while elucidative, often underemphasizes the role of societal structures and power relations in shaping adoption patterns (greenhalgh et al., 2004). in relation to the present study on the impact of fintech on the nigerian economy, the “innovation diffusion theory” provides a different approach to interpret findings. nigeria presents a mosaic of adoption patterns – while certain fintech innovations like mobile banking have gained traction, others face resistance. the theory could explain why certain demographics or regions in nigeria might be early adopters, while others lag behind. moreover, understanding the stages of diffusion could be pivotal for policymakers and businesses aiming to catalyze fintech’s positive impact on nigeria’s economic growth. financial intermediation theory a pivotal theory in understanding the structure, behavior, and evolution of financial markets and institutions is the “financial intermediation theory.” the “financial intermediation theory” revolves around the role and functioning of financial intermediaries – entities that act as middlemen between savers and borrowers. the classical work of gurley and shaw (1955) serves as a foundational pillar in this domain. they argued that financial intermediaries emerge to reduce transaction costs, manage risks, and address informational asymmetries in the market. instead of individual savers directly lending to borrowers, which can be inefficient and fraught with uncertainties, intermediaries like banks, insurance companies, and mutual funds pool resources and lend them out, leveraging their expertise, scale, and diversification. in the fintech landscape, this theory becomes especially salient. traditional banks, as financial intermediaries, are now being complemented (or even challenged) by digital platforms that also perform intermediation, albeit with different mechanisms. peer-to-peer lending platforms, robo-advisors, and crowdfunding portals are all modern embodiments of the financial intermediation concept, leveraging technology to potentially enhance efficiency, reduce costs, and democratize finance. the strength of the “financial intermediation theory” lies in its comprehensive explanation of why certain financial institutions exist and how they add value in the economic system. it provides a framework to understand the transformation of savings into investments, the management of risks, and the allocation of capital in economies. however, it’s not without criticisms. the digital age has introduced new forms of intermediation that challenge traditional paradigms. critics argue that the classical intermediation theory may not fully capture the importance of digital finance platforms, especially those that operate on decentralized systems like blockchain (tapscott & tapscott, 2016). moreover, while intermediaries reduce risks, they can also introduce systemic risks, as was evident in the 2008 financial crisis (gorton & metrick, 2012). for nigeria, understanding the “financial intermediation theory” is essential in navigating its burgeoning fintech landscape. with a robust traditional banking system juxtaposed with rapidly emerging digital financial platforms, nigeria is at an inflection point. the theory offers insights into how digital platforms might reshape financial intermediation, potentially offering more inclusive, efficient, and resilient financial services. as nigeria aspires for robust economic growth, the evolution of its financial system, guided by both traditional and modern tenets of financial intermediation, will play a pivotal role. solow growth model one of the foundational theories used to understand the determinants and drivers of economic growth is the “solow growth model,” also known as the neoclassical growth theory. “the solow growth model” was developed by robert solow in the 1950s. the theory distinguishes between short-term fluctuations and longterm trends in the growth process of an economy. at its core, the model revolves around three primary inputs: labor, capital, and technology. solow postulated that in the early stages, an economy can grow by increasing labor or capital; however, over time, there are diminishing returns to these inputs. as a result, sustained long-term growth can only come from technological progress or improvements in efficiency (solow, 1956). the strength of the solow growth model lies in its simplicity and general applicability. it provides a foundational framework for understanding the macrolevel factors that drive economic growth. the model has been instrumental in highlighting the crucial role of technological advancement and innovation in ensuring sustainable economic growth, a perspective pa ge 39 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 that’s profoundly relevant in today’s tech-driven global economy. however, the model also has its share of criticisms. romer (1986) and lucas (1988) argued that the solow model doesn’t adequately address the role of human capital (knowledge, skills, and health of the population) in growth. moreover, the model’s assumption of constant returns to scale and its treatment of technology as an exogenous factor have been challenged in endogenous growth theories, which emphasize the internal factors within an economy, like r&d and education, that influence growth. for nigeria, the solow growth model offers essential insights. the nation’s rich endowments in natural resources, especially oil, and its young labor force provide short to medium-term avenues for growth. however, for sustained long-term prosperity, nigeria’s focus on leveraging and integrating technology into its economic fabric becomes imperative. the advent of fintech, as discussed earlier, is an example of how technological innovation can significantly impact economic productivity and growth. as nigeria grapples with the challenges of diversifying its economy, ensuring stability, and fostering inclusive growth, theories like solow’s underscore the indispensable role of technological innovation and integration. empirical review obinna and uche in their 2017 research titled “digital payments and economic growth in nigeria: risks and rewards,” examined the influence of digital payments on nigeria’s economic growth. this research spanned the period from 2005 to 2016. adopting a quantitative analysis, the dependent variable was economic growth, while the independent variable was the proliferation of digital payments. the study’s outcomes indicated that digital payments can indeed spur economic activities. however, the associated risks can be counterproductive, especially in nations with emerging cyber-infrastructure. this study was appreciated for its relevance to the nigerian context. however, some critics suggested that it would have been beneficial to explore the nature and types of digital payments in use more in-depth and their differential impacts. folorunso and ikpefan (2018) present a broader perspective in their study on the impact of financial technology on economic growth. their research isn’t solely anchored to nigeria but spans several african nations. although centered around events and data from 2005 leading up to 2018. folorunso and ikpefan’s methodological approach is both diversified and robust. they combined econometric modeling with qualitative evaluations to uncover the of the relationship between fintech and economic growth. their choice of dependent variable was economic growth, which they assessed using gdp growth rates amongst other macroeconomic indicators. in contrast, the independent variable under scrutiny was financial technology. they particularly emphasized the adoption and utilization rates of instant financial transaction platforms across the countries in their study. their conclusions differ notably from akinwale and tijani’s study. folorunso and ikpefan observed that there’s a direct and positive correlation between the rapidity of financial transactions enabled by fintech and economic growth. they posited that such immediate transaction capabilities can speed up business processes, acting as a catalyst for economic activity. however, a potential critique of their work is the broad generalizations made across different african countries. each of these nations has its unique economic, cultural, and regulatory backdrop, which might necessitate a more bespoke approach to research. mutua and ouma (2017) in their seminal work titled “the impact of mobile banking on financial inclusivity in kenya,” mutua and ouma explored the domain of financial technology, specifically focusing on mobile banking’s role in fostering financial inclusivity. their study covered the period between 2010 and 2016, reflecting a significant phase during which kenya witnessed a surge in mobile banking users. utilizing regression analysis, the researchers treated mobile banking adoption (measured by the number of users and volume of transactions) as the independent variable, while financial inclusivity (gauged by the percentage of the banked population) stood as the dependent variable. their findings revealed a positive correlation between mobile banking adoption and financial inclusivity, suggesting that mobile platforms were instrumental in integrating a larger section of the kenyan population into the formal financial system. however, critics of their study argue that while the correlation is evident, causation isn’t conclusively proven. some believe that external factors, such as government policies or global tech trends, could have played a more significant role than what was credited to mobile banking alone. patil and kumar’s research, titled ‘e-commerce and india’s economic growth: an analytical exploration,’ focused on ‘digital transactions and its impact on a country’s economy.’ from 2012 to 2017, their study aimed to ascertain the role of burgeoning e-commerce platforms in influencing india’s gdp growth. adopting a mixed-method approach that combined qualitative and quantitative data, they considered e-commerce adoption rates and digital transaction volumes as independent variables, with the gdp growth rate as the dependent variable. their research revealed that the proliferation of e-commerce platforms, supported by governmental policies, significantly correlated with india’s economic uptick during the period. however, this study is not without its detractors. critics have pointed out that the research sometimes oversimplified the complex interplay of various economic factors. they argued that it attributed disproportionate credit to e-commerce, potentially downplaying other concurrent economic drivers. desalegn (2020) focusing on the ethiopian context, pa ge 40 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 desalegn, in his paper “web transactions and economic growth: a critical evaluation of ethiopia’s landscape,” embarked on examining the intricate relationship between web-based transactions and the nation’s economic landscape. his study spanned from 2015 to 2019. using time-series analysis, desalegn chose the volume of web transactions as the independent variable, with the country’s gdp growth rate being the dependent variable. while the research underscored an observable increase in the volume of web transactions, desalegn took a cautious stance. he argued that without parallel growth in supportive infrastructure and apt regulations, the observed economic benefits might be ephemeral. his work, however, faced criticism for its somewhat pessimistic outlook. opponents believed that while his concerns were valid, they overshadowed the vast potential web transactions hold for ethiopia’s economy in the long run. kamau and waiganjo in their 2016 study focused on the burgeoning landscape of kenya’s mobile banking sector, particularly assessing its role in the growth of small and medium-sized enterprises (smes). spanning the time frame from 2010 to 2015, the study employed a descriptive research design with stratified sampling techniques to gather data from a diverse group of smes across kenya’s major cities. by utilizing regression analysis, they set out to understand the relationship between mobile banking (independent variable) and sme growth (dependent variable). their findings suggested a positive correlation between the two, indicating that the surge in mobile banking adoption greatly facilitated the operations and growth of smes by offering them access to expedited financial services. however, some critics argue that the study doesn’t factor in other socio-economic parameters that might have influenced the growth of smes during the same period. singh and agrawal embarked on a comprehensive exploration in 2019, dedicated to india’s digital economy with a spotlight on mobile payments. the study, which covered the period from 2014 to 2018, made use of a quantitative method, collecting data through structured questionnaires disseminated among urban and rural populations. analyzing the data through a series of regression models, the independent variable was the infrastructure of digital payment systems, while the dependent variable was the rate of financial inclusion in the country. their findings highlighted the indispensable role of robust digital infrastructure in the successful proliferation of mobile payments. yet, they emphasized the challenges posed by infrastructural bottlenecks. while the study is profound in its insights, some scholars feel it might have glossed over the cultural and behavioral aspects of the population that could influence their adoption of mobile payments. in 2021, teshome turned his research lens towards ethiopia’s digital economy, focusing on the realm of digital payments. the study, spanning the years 2016 to 2020, employed a mixed-method approach, combining both qualitative interviews and quantitative surveys from different demographic sections. employing factor analysis, the research delineated the relationship between the dependent variable, economic growth, and the independent variable, the adoption rate of digital payments. teshome’s conclusions underlined the immense potential of digital payments in ethiopia but also spotlighted the impediment of limited financial literacy among the populace. the study, while in-depth, has been critiqued for not sufficiently addressing the regulatory and policy environment that could affect the growth of digital payments. materials & methods in this study, the selected research design is the expost facto design. this approach is characterized by the researcher’s inability to manipulate the data under examination. defined by kerlinger (1973), the ex-post facto, also termed as ‘causal comparative research’, delves into identifying potential cause-effect relationships between dependent and independent variables. the primary objective is to establish a definitive causal connection between them. the pertinence of this design, especially in discerning such relationships, motivated its adoption for the present study. for this study, the core dataset is constructed from annual secondary data that align closely with our research objectives. these data have been generated from esteemed publications, primarily the central bank of nigeria (cbn) and the national bureau of statistics (nbs). the data sets focus on several pivotal metrics: instant pay transactions, internet (web) transactions, mobile payment transactions, and indicators of economic growth, represented through real gross domestic product (rgdp). the model for the study modified the framework of mamudu and gayovwi (2019) in estimating the relationship between cashless policy (measured as instant pay transactions, internet (web) transactions, mobile payment transactions) and economic growth. for this study, the mathematical specification of the implicit model that expresses the relationship between fintech and economic growth in nigeria is expressed as: rgdpt = ƒ(ipt, itr, mpt) (1) setting up equation (1) in a linear stochastic form (or econometric form) is expressed as: rgdpt = λ0 + λ1 ipt + λ2 itr + λ3mpt + μ1 (2) where: rgdp = real gdp ipt = instant pay transactions itr = internet transactions mpt = mobile payments transactions λ0 = constant term λ1 λ3 = coefficients of fin-tech (as instant pay transactions, internet (web) transactions, mobile payment transactions) at time t. μ1 = the error term at time t building equations (2) into a fmols model, we have: (3) pa ge 41 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 integrated. unlike certain econometric techniques which demand that time series variables be integrated of order one, or i(1), fmols allows for rigorous analysis without pre-existing assumptions about the integration properties of the series under examination. this flexibility positions fmols as a preferred choice, especially when scrutinizing relationships between variables like instant pay transactions, internet (web) transactions, mobile payment transactions, and rgdp in a dynamic economic landscape like nigeria. results and discussions descriptive statistics table 1 presents the descriptive statistics for the real gross domestic product (rgdp) and three types of digital financial transactions: value of instant pay transactions (ipt), value of internet (web) transactions (itr), and value of mobile payment transactions (mpt) all denominated in naira billion. equation represents the long-run relationship between rgdp and the selected financial technology variables using the fmols methodology. the coefficients will give insights into how each of the financial technology variables impacts economic growth in nigeria. the fmols methodology offers superior robustness in the face of endogeneity, negating the necessity for instruments like the 2-stage least squares or instrumental variable approach. fmols corrects for potential endogeneity stemming from the feedback effects among the variables, ensuring unbiased long-run estimates. what sets the fmols apart is its technique to account for the potential endogeneity in the independent variables and serial correlation in the error terms. this method provides adjustments for potential biases and serial correlation in the error terms that often plague ols in non-stationary contexts. fmols can be applied irrespective of whether the variables under study are integrated of order one, i(1), mixed, or even fractionally table 1: descriptive statistics rgdp ipt itr mpt mean 2.386039 17792.59 83.45523 837.7586 std. dev. 2.194305 13993.28 73.83482 952.7098 skewness -0.04536 0.378421 0.501017 0.762613 kurtosis 2.151877 1.638418 1.587568 1.868649 jarque-bera 1.333829 4.448979 5.498229 6.611496 probability 0.513290 0.108123 0.063984 0.036672 observations 44 44 44 44 source: authors computation, 2023 (eviews-12) the average rgdp growth rate over the period studied is 2.386039%, indicating the general pace of economic growth. meanwhile, the digital transactions show a considerably large volume, with ipt transactions leading the way with an average value of 17,792.59 billion naira. this is followed by mpt and itr, with average values of 837.7586 billion naira and 83.45523 billion naira, respectively. the standard deviations for these variables provide insights into their spread around the mean. the rgdp has a relatively low spread with a standard deviation of 2.194305%, indicating stable economic growth. ipt has the highest variability, as indicated by its standard deviation of 13,993.28 billion naira, hinting at significant fluctuations in the value of instant transactions. this high variability can also be seen in mpt and itr, which have standard deviations of 952.7098 billion naira and 73.83482 billion naira, respectively. skewness gives a sense of the direction and degree of asymmetry of the distribution. the rgdp distribution is almost symmetrical with a skewness close to zero. in contrast, all digital transaction variables are positively skewed, with mpt having the highest skewness, suggesting that there are a few periods with exceptionally high values of mobile transactions. kurtosis measures the “tailedness” of the distribution. all variables, except rgdp, have kurtosis values less than 3, suggesting a distribution with lighter tails compared to a normal distribution. finally, the jarque-bera statistic tests the hypothesis that the data is normally distributed. for rgdp, with a value of 1.333829 and a probability of 0.513290, we fail to reject the null hypothesis, suggesting that rgdp is normally distributed. for the financial technology variables, ipt and itr have p-values above 0.05 (0.108123 and 0.063984, respectively), suggesting that these distributions do not significantly deviate from normality. however, mpt, with a jarque-bera statistic of 6.611496 and a probability of 0.036672, indicates a potential deviation from a normal distribution. unit root test time series data often exhibit tendencies that can be addressed through differencing, primarily to ascertain the stationarity of the data. the unit root test, therefore, checks the stationarity of our model’s series data, helping to determine the authenticity of the relationship between financial technology variables and the nigerian economy. essentially, the null hypothesis presumes non-stationarity in the variables. a variable is regarded as non-stationary if pa ge 42 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 its test statistic, when taken in absolute terms, falls below its critical value at specific significance levels. table 2 thus presents the results of the augmented dickey-fuller (adf) unit root test, an essential step in time series analysis to determine the stationarity of the series. stationarity implies that statistical properties, such as mean and variance, remain constant over time, which is crucial for modeling and forecasting. table 2: unit root test result variable adf test statistics adf critical value order of integration rgdp -3.743706 -3.520787** i(1) int -7.460580 -4.198503* i(1) ipt -5.295919 -4.192337* i(1) mpt -3.264371 -3.192902*** i(1) note: *, **, *** significant at 1%, 5% and 10% source: authors computation, 2023 (eviews-12) for the variable rgdp, the adf test statistic is -3.743706, which is more negative than its critical value at the 5% significance level (-3.520787**). this suggests that rgdp is stationary after first differencing, hence it is integrated of order one, i(1). similarly, the internet transactions variable (int) has an adf test statistic of -7.460580. this value is far more negative than the critical value at the 1% significance level (-4.198503*), indicating strong evidence against the presence of a unit root. therefore, int is also stationary at first difference, i(1). for the instant pay transactions (ipt), the adf test statistic is -5.295919, surpassing the critical value at the 1% significance level (-4.192337*). thus, ipt is stationary at first difference, i(1). lastly, the mobile payment transactions (mpt) has an adf statistic of -3.264371, which is more negative than its critical value at the 10% significance level (-3.192902***). this indicates that mpt is stationary after first differencing and is integrated of order one, i(1). therefore, all variables in table 2 rgdp, int, ipt, and mpt are integrated of order one, i(1), suggesting that each of them becomes stationary after taking their first differences. this implies that any modeling or forecasting involving these variables will require addressing this nonstationarity, typically through techniques like differencing or cointegration. cointegration test building upon our ongoing discussion on time series analysis and the exploration of relationships between economic and financial variables, the “table 3” unveils an essential aspect of our analysis: the co-integration of series. co-integration ensures that even if the individual series are non-stationary, their linear combinations can be stationary, suggesting a stable, long-term relationship among them. table 3: results of engle and granger (residual based) cointegration test variable adf test statistic 95% critical adf value remarks residual -2.785041 -2.621185* co-integrated note: * significant at 1% source: authors computation, 2023 (eviews-12) in “table 3: results of engle and granger residual based cointegration test”, the adf test statistic for the residuals is -2.785041, which exceeds the critical value at the 1% significance level of -2.621185, indicating cointegration. this is of paramount importance, as it signals a long-term equilibrium relationship between financial technology adoption or advancements and economic growth in nigeria. fmols regression results and test of hypotheses building upon our discussion regarding the impact of financial technology on economic growth in nigeria, the “table 4: fully modified least squares (fmols) result” offers some interesting insights into the direct influences of specific financial technology facets on the country’s economic growth, as measured by rgdp (%). starting with the value of instant pay transactions (log(ipt)), we observe a coefficient of -5.6296, which is statistically significant at the 0.0016 level. the negative sign indicates an inverse relationship with rgdp. this might initially seem counterintuitive, but it suggests that as instant pay transactions increase, there may be a decline in the rgdp. it’s possible that while these transactions provide convenience, they could be displacing other more profitable or traditional transaction methods, hence leading to a short-term decrease in economic growth. based on the outcome of the p-value which was found to be (0.0016) less than 0.05 (or 5%) level of significance, the study concludes that instant pay transactions have a significant impact on economic growth in nigeria. the value of internet (web) transactions (log(itr)), on the other hand, has a positive coefficient of 0.7707, pa ge 43 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 significant at the 0.0122 level. this indicates a direct relationship with rgdp, suggesting that as web-based transactions grow, there’s a favorable impact on the country’s economic growth. it can be inferred that internet transactions, which might encompass e-commerce, online services, and other web-based financial activities, are contributing positively to nigeria’s economic landscape. inline with the outcome of the p-value (which was found to be 0.0122, and also less than 0.05, the study concludes that internet (web) transactions has significantly enhanced nigeria’s economic growth within the study period. moreover, the value of mobile payment transactions (log(mpt)) also presents a positive relationship with a coefficient of 2.5144, significant at the 0.0412 level. the influence of mobile payments can’t be understated, especially in a country like nigeria where mobile penetration is high, and many citizens rely on mobilebased solutions for their financial needs. the positive coefficient suggests that as the adoption and use of mobile payment solutions grow, it’s conducive to nigeria’s economic prosperity. therefore, based on the outcome of the p-value which was found to be 0.0412, and also less than 0.05 (or 5% level of significance), the study further concludes that mobile payment transactions has a significant impact on nigeria’s economic growth. the r-squared value of 0.678682 implies that approximately 67.87% of the variation in the dependent variable (in this context, economic growth or rgdp) can be explained by the independent variables in the model (which could include value of instant pay transactions (ipt), value of internet (web) transactions (itr), and value of mobile payment transactions (mpt)). this is a relatively high value, indicating that the model has captured a substantial portion of the variability in the dependent variable. the wald-statistic of 7.3161, accompanied by a wald (p-value) of 0.0407, is employed to test the joint significance of coefficients in the model. the p-value being less than 0.05 suggests that the coefficients of value of instant pay transactions (ipt), value of internet (web) transactions (itr), and value of mobile payment transactions (mpt) in the model are jointly significant at the 5% level. this means that the fintech indicators in the model, as a group, play a significant role in explaining the variation in economic growth or rgdp. table which shows the results of residual test provides insights into the quality and validity of the regression model utilized in the study. table 4: fully modified least squares (fmols) result dependent variable: rgdp (%) variable coefficient std. error t-statistic prob. log(ipt) -5.6296 1.6164 -3.4828 0.0016 log(itr) 0.7707 0.2728 2.8255 0.0122 log(mpt) 2.5144 1.1401 2.2055 0.0412 c 37.5433 1.8627 20.1551 0.0000 r-squared 0.678682 adjusted r-squared 0.523196 long-run variance 11.98656 wald-f-statistic 7.3161 wald-f-statistic (p-value) 0.0407 source: authors computation, 2023 (eviews-12) table 5: results of residual test tests outcomes coefficient probability correlogram q-statistics (serial correlation) f-stat. 2.189336 0.2592 normality test jarque-bera 3.592803 0.1658 source: authors computation, 2023 (eviews-12) the correlogram q-statistics test is utilized to identify any potential serial correlation in the residuals of a regression model. serial correlation can be problematic as it suggests that the model may not be capturing all relevant information, leading to inefficiency in the regression estimates. in this test, an f-statistic of 2.189336 with a probability of 0.2592 is observed. since the probability value is greater than conventional significance levels (e.g., 0.05 or 0.01), it indicates that there’s no significant serial correlation in the residuals. this is a positive outcome, indicating that the fmols regression model is well-specified in this aspect. next, the normality test, specifically the jarque-bera statistic, assesses whether the residuals of the model are normally distributed. normal distribution of residuals is a key assumption for many statistical tests and is pivotal for the validity of many inferences drawn from the regression model. the jarque-bera value stands at 3.592803 with a probability of 0.1658. the probability value being greater than the conventional significance levels indicates that the residuals do not significantly deviate from a normal pa ge 44 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 distribution. therefore, the residual tests from table 5 suggest that the regression model employed in studying the nexus between financial technology and economic growth in nigeria seems well-fitted, with no evidence of significant serial correlation in the residuals and with residuals that are approximately normally distributed. this reinforces the reliability of the conclusions and inferences drawn from the model regarding the role of fintech in nigeria’s economic trajectory. discussion findings from the study indicate that instant pay transactions (ipt) exhibit a negative, yet statistically significant, impact on economic growth in nigeria. the implication of this observed negative effect is that while instant pay transactions may have streamlined payments and remittances, simplifying financial processes for both individuals and businesses, they could be inadvertently stifling longer-term economic growth. one conceivable interpretation is that the immediate gratification offered by instant payments may be encouraging a culture of instantaneous consumption at the expense of more sustainable, long-term investments that are vital for robust economic expansion. furthermore, the ease of instant transactions could be creating a transient economic environment, where funds are rapidly circulated but not necessarily utilized in avenues that stimulate meaningful economic development or growth. this understanding resonates with a study by obinna and uche (2017) in nigeria who found that while digital payments can spur economic activities, the associated risks, especially in countries with less mature cyber-infrastructure, can be detrimental. the study suggests that for economies like nigeria, transitioning to digital modes should be gradual and well-structured. conversely, our results diverge from the conclusions drawn by folorunso and ikpefan (2018). their research, which spanned several african countries, suggested that instant financial transactions directly correlate with economic growth. they argued that the proximity of such transaction’s aids in accelerating business processes, reducing downtimes, and consequently spurring economic growth. in addition, findings from the study showed that internet (web) transactions have significantly enhanced nigeria’s economic growth. the findings imply that with the increasing adoption of web-based transactions, there’s an obvious positive shift in nigeria’s economic growth. one of the primary reasons is the direct access to a broader market provided by the internet, eliminating many geographical and logistical barriers that previously hindered growth. furthermore, businesses, especially small and mediumsized enterprises, have been able to tap into new revenue streams, ensuring a more sustained cash flow and enabling them to reinvest in their ventures. this perspective aligns with the study by mutua and ouma (2017) from kenya, who observed that the increase in web transactions, facilitated mainly by mobile banking and e-commerce, has been pivotal in the country’s recent economic growth. they argued that technological advances in financial platforms have enabled better financial inclusivity, ensuring that even the traditionally unbanked populations participate in the economy. similarly, patil and kumar (2018) from india shared insights into how digital transactions, especially in the e-commerce and service sectors, have augmented the country’s gdp growth. lastly, findings from this study suggest that mobile payment transactions (mpt) have a positive and significant impact on nigeria’s economic growth. the implications of these findings reveal that the increase in the adoption and use of mobile payment platforms has dynamically transformed the nigerian financial landscape, providing easy access to financial services and facilitating swift and secure transactions. the broader acceptance of these platforms has enhanced economic activities by empowering a larger portion of the population, especially those in remote areas, to participate in the digital economy. additionally, the surge in mobile payments has reduced the dependency on physical banking infrastructure, leading to reducing operational costs for banks and fostering financial inclusivity. this finding is in line with the findings of kamau and waiganjo (2016), who explored the transformative effect of mobile banking in kenya. their research posits that the rise of mobile banking systems catalyzed the growth of small and medium-sized enterprises (smes), a backbone of the kenyan economy, by providing them with convenient financial tools and services. conclusion over the past decade, financial technology has prominently emerged as a significant driver influencing economic growth, especially in developing nations such as nigeria. our primary objective was to investigate the impact of financial technology on nigeria’s economic growth between 2012q1 and 2022q4. three salient conclusions emerged from our discussions: the spread of web-based transactions has considerably enhanced nigeria’s economic canvas. their adoption has facilitated smoother business operations, expanded market reach, and fostered global integrations, all of which have contributed positively to economic growth. our discourse revealed that mobile payment mechanisms have benefited nigeria’s economic landscape. the ease and convenience of mobile transactions have empowered even the remotest parts of the country, bridged economic disparities, and fostered inclusive growth. remarkably, instant pay transactions (ipt), while being an innovation in seamless transactions, displayed a negative yet statistically significant correlation with nigeria’s economic growth. this could be attributed to factors like rapid adoption without adequate infrastructural support or potential misuse. recommendations arising from these findings, the following recommendations were suggested: i. the evident economic influence of web-based transactions in nigeria necessitates focused action. in collaboration with private entities, the government should pa ge 45 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 35-45, 2023 prioritize strengthening digital infrastructure. this ensures reliable internet access nationwide. concurrently, as online transactions expand, so do cybersecurity threats. enhanced cybersecurity measures and public awareness campaigns are paramount to safeguarding the nation’s digital financial frontier. ii. mobile transactions have transformed nigeria’s financial landscape. however, to optimize their potential, a two-pronged approach is recommended. firstly, expanding mobile network connectivity to rural and remote areas ensures that no community is left behind. secondly, there is a clear need for consistent user education to enlighten individuals about safe mobile payment practices and the system’s broader capabilities. iii. the negative association of instant pay transactions with economic growth raises concerns. an in-depth infrastructure audit is essential to reveal and address any underlying inefficiencies or bottlenecks within that gateway payment system. alongside, tighter regulatory oversight by the cbn on ipt is pivotal. such measures will not only mitigate potential misuse but also realign ipt to bolster 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(2016). mobile payment: the origins, the technologies, and the business models. electronic commerce research and applications, 20, 141-150. pa ge 1 pa ge 7 american journal of financial technology and innovation (ajfti) research on the path of enterprise economic innovation and development under the background of digital economy changxia mu1,2*, fuqiang guo1, polonik irina stepanovna1 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.3994 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: november 04, 2024 accepted: december 09, 2024 published: february 03, 2025 the development of the digital economy is in full swing, becoming the backbone of promoting economic development, which brings severe challenges to enterprises but also brings new opportunities for development. under this background this paper introduces the digital economy under the background of the achievements of industrial economies, such as by strengthening the development of enterprise economy in the development of innovation path and make digital economy enterprise economy under the background of digital organization, and through the relevant research found that enterprise economy is facing development difficulties, so this paper discusses the digital economy under the background of realistic problems, such as the lack of standardization enterprise economy, enterprise economy lack of digital transformation concept, digital talent, enterprise economic management mode lag and management supervision mechanism. finally, according to the research on how to develop the innovative development path of enterprise economy under the background of the digital economy, the relevant countermeasures and suggestions for the innovative development of enterprise economy under the background of digital economy are put forward. for example, strengthening innovative paths for the development of key core technologies in enterprise economy and constructing digital organizational forms for enterprise economy in the context of digital economy, hoping to provide effective suggestions for the transformation of enterprise economy to digital economy and the realization of innovative development. keywords digital economy, enterprise economy, innovation and development introduction the intervention of digital economy brings peoples cognition of enterprise management to a new height. under this opportunity, many enterprises begin to take measures one after another to realize the digital transformation of enterprise management. among them, the relationship between enterprises and enterprises and the relationship between enterprises and users are the two key levels of the digital transformation of modern enterprises. based on this change, the operation and management of modern enterprises has gradually entered a new stage of development. according to the measures for statistical classification of large, small and medium enterprises issued by the national bureau of statistics in 2017, the classification is mainly based on the number of employees and sales of employees: large industrial enterprises usually have 1000 or more employees with an annual sales of at least 400 million yuan; sales and small industrial enterprises are characterized by no more than 300 employees and sales of no more than 20000 yuan (cao baichuan, 2023) see figure 1 classification method for large, small and medium-sized enterprises. therefore, this paper will pay attention to the challenges and coping strategies faced by the enterprise economy in the era of digital economy, and discuss how to make better use of digital technology to optimize resource allocation, improve production efficiency and innovation ability, so as to promote the development of industrial economy to the direction of higher quality, more efficient and more sustainable. 1 belarusian national technical university, minsk, 220013, belarus 2 tianfu college of swufe, chengdu, 610052, china * corresponding author’s e-mail: muchangxiamm@163.com figure 1: classification method of large, small and medium-sized enterprises pa ge 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 7-13, 2025 literature review related theoretical research and analysis of digital economy and enterprise economy nowadays, the wave of information revolution is sweeping the world, the digital economy is booming, and the way of production and life is undergoing profound changes. in the enterprise economy, digital economy, as a major outlet in the new era, has attracted a lot of attention. however, the development of digital economy in the enterprise economy is still relatively simple, so this leads to a series of problems in the current enterprise economy. how to use the power of digital economy to make the development of enterprise economy reach another height will be the main goal of enterprise economic development in the future. in this regard, this paper will make relevant theoretical research on digital economy and enterprise economy through literature analysis method, case analysis method and comparative demonstration method. research status of digital economy at home and abroad data from the research report on the development of digital economy has further achieved reasonable growth in volume. in 2022, the scale of digital economy reached 50.2 trillion yuan, with a year-on-year nominal growth of 10.3%, which has been significantly higher than the nominal gdp growth rate of the same period for 11 consecutive years. the proportion of digital economy in gdp is equivalent to the proportion of the secondary industry in the national economy, reaching 41.5%. at the same time, in keeping with the trend of global trade, china actively carries out foreign investment activities and participates in international market competition (chen chen, 2023). overseas investment enterprises join and constantly seek ways of value growth to seek long-term sustainable development. figure 2 shows the flow of foreign direct investment in china over the years. due to the low level of science and technology in the early stage, the productivity of enterprises is low, and there is no obvious competitive advantage compared with similar foreign enterprises. however, there are still many enterprises carrying out transnational investment, hoping to learn and accumulate foreign advanced production technology through overseas direct investment, and enhance their own value and enhance their international competitiveness. figure 2: chinas foreign direct investment at present, the united states, japan, germany and other countries are vigorously developing the digital economy. in 2020, the us digital economy will dominate the national economy, both in terms of scale and proportion. as a digital economy first countries, the us government attaches great importance to the innovation of digital technology research and development, continuous investment in artificial intelligence, chip, semiconductor and other scientific research activities, actively launch digital chip eri plan, jump plan, etc., from the internet of things, cloud technology and other new areas of network threat, efforts to digital infrastructure, quantum information science and technology of international strategic cooperation, with the uk, japan, poland and other countries have signed a cooperation agreement. in 2021, the information technology and innovation foundation (itif) released a report, showing that the united states pays attention to the construction of the digital system, attaches importance to the development of the digital economy, constantly deploys national strategies, and dominates the layout of key areas of the digital economy. the japanese government put forward the concept of “society 5.0”, which aims to rely on scientific and technological innovation to boost social change, and use technology to solve the social problems caused by aging. since 2020, germany has been accelerating the discussion of “digital” issues. in may 2020, chancellor angela merkel stressed the importance of digital sovereignty and reducing the degree of digital dependence on foreign countries (yueshu, 2022). in january 2021, germany released the federal government data so that the value of data can effectively respond to the epidemic and stabilize economic growth. in february 2021, germany and france jointly formulated pa ge 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 7-13, 2025 a new european industrial strategy, stressing the need to strengthen industrial and digital sovereignty. in order to further promote the industrial innovation and development of germany, germany has continuously strengthened the policy layout of digital economy, promoted the digital transformation of manufacturing industry, and laid the foundation for building a high-end manufacturing power research status of enterprise economy at home and abroad the development of enterprise economy in china began very early. in 2007, enterprises in our provinces appeared obvious industrialization, especially the industrial scale of the six central provinces, far higher than the degree of industrialization of other provinces, as high as 18.5%. the overall industrial structure of chinese enterprises is biased to heavy and chemical industries, especially in energy, steel and chemical industry. with electric power and steel as the pillar industries, and most of the resourcebased industries, the overall framework of chinas industrial enterprises has been formed. the development of large and medium-sized industrial enterprises cannot be separated from financial support. statistics show that from 1999 to 2013, the investment in research and experimental development of these enterprises showed a steady growth trend. during this period, the capital expenditure increased from 249.9 billion yuan in 1999 to 674.4065 billion yuan in 2013, an increase of 649.4165 billion yuan, an annual growth rate of about 26.72%; compared with 2012, the r & d expenditure in 2013 increased by 12.55%. for example, among the six central provinces, shanxi province spent 15.16 billion yuan on industrialization in 2008, which is far from the first henan province, and is located in the middle of the six central provinces. according to the survey data, the proportion of scientific research activities of large and medium-sized industrial enterprises in shanxi province is relatively high, which is higher than 1.57% of the national average level, and shows a trend of increasing year by year (jianjun & dan, 2022). figure 3 capital growth of industrial chart in the evaluation of enterprise economic performance figure 3: 1998 capital growth trend of industrial enterprises in 2014 in foreign countries, germany pays special attention to the use of enterprise solvency indicators to evaluate enterprise benefits. they believe that the level of debt repayment ability not only shows the ability of economic and financial management of enterprises, but also directly reflects the economic and financial situation of enterprises and the ability of the enterprises to operate. maintaining the solvency is more important than making profits. the eight main indicators to evaluate the economic benefits of enterprises in germany include self-owned capital ratio, liability ratio, self-owned asset compensation rate of fixed assets, long-term capital compensation rate of fixed assets, cash ratio, quick ratio, current ratio and debt turnover period (borrowed capital / year total cash flow). each of these eight indicators is related to solvency, and the latter six directly measure either long-term or shortterm solvency. exploration on the innovation path of digital economy and enterprise economy the development of the digital economy highlights, accelerates the transformation of factors, acceleration of basic reconstruction, optimization and upgrading of driving forces, and integrates innovation and quality, boosting high-quality development with a new attitude. as a new economic form with data resources as the key element, digital economy has promoted the new vitality of the world economy in the world trend of digital sharing. as the international community becomes increasingly connected in cyberspace, countries economic development opportunities and growth depend on the digital world. the digital development of enterprise economy has broad prospects and great potential, and also faces many practical challenges, such as the lack of enterprise digital infrastructure, the lagging development pa ge 10 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 7-13, 2025 of enterprise management system, the immature processing of enterprise data elements, and the lack of enterprise digital technical talents. therefore, it is particularly important to explore the innovation path of accelerating the integration of digital economy and enterprise economy. materials and methods the impact of the advent of the digital economy era on the enterprise economy the author analyzes the timg index from the four dimensions of digital technology (technology), digital infrastructure (infrastructure), digital market (market) and digital governance (governance) (guobao, 2021). as shown in table 1, the index system of the timg index. it is concluded that enterprise economy is an important link to promote the continuous development and progress of social economy, and digital economy is an important link to promote the development and upgrading of enterprise economy. table 1: index system of the timg index level 1 indicators weight secondary indicators weight level 3 indicators digital technique technology 25% r & d output 1/3 digital patent scale number of papers published papers in mathematics and computers human capital 1/3 enrollment rate in higher education national digital literacy innovation level 1/3 innovation activity level of industry-university-research cooperation digital infrastructure infrastructure 25% pueblance 1/3 active fixed broadband users active mobile broadband users mobile phone subscriptions convenience 1/3 international internet bandwidth per capita mobile rates mobile phone price safety 1/3 network security index digital market market 25% demand side 1/3 digital consumer scale mobile social media penetration rate the supply side 1/3 number of digital enterprises digital enterprises get the financing scale international market 1/3 export scale of digital services digital governance governance 25% digital government 1/3 e-government index economic and social environment 1/3 business environment index degree of intellectual property rights protection political and legal environment 1/3 digital-related legal regulation construction the ict regulatory tracking index government support the specific impact of the digital economy on the enterprise economy with the advent of the digital economy era, the social labor force is rapidly shifting to economy and society. china is a big agricultural country. among all the types of social division of labor in china, the primary industry is agriculture, the secondary industry is the traditional manufacturing industry, and the tertiary industry is the service industry. however, with the continuous reform and development of economic society, chinese economic and social division of labor also slowly shows the trend of “great differentiation” and “great reorganization”. based on the needs of social development, some agricultural and industrial producers turn to the tertiary industry; from the detail level, the rise of emerging industries such as electronics, information, biology, new materials, new energy and ocean makes chinese enterprises in the digital economy era more dynamic. these emerging industries have become the pioneers of leading the era trend of economic development. the digital economy under the background of enterprise economy gradually present open characteristics, based on big data, artificial intelligence technology innovation enterprise management mode and management idea, on the one hand can effectively ensure that enterprise internal management to strengthen information sharing, through pa ge 11 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 7-13, 2025 digital technology to ensure different levels across time and space, and to ensure that the flow of communication departments, reduce information barriers, provide more power for enterprise management. on the other hand, the internal enterprise can use digital technology to exchange information with the external company and timely understand customer needs, so as to improve customer viscosity and enhance customers trust in the enterprise. with the help of digital technology to understand the needs of different customers, enterprises can also predict the market development dynamics based on customer needs, and strengthen product design and targeted marketing, so as to ensure continuous innovation and optimization in decision-making, sales and personalized service in the process of enterprise management, so as to provide consumers with better consumption experience. with the advent of the era of digital economy, the management process and management mode of enterprises have gradually changed. strengthening the effective application of digital technology can not only realize the innovation and development of enterprises, but also enable the enterprises to maintain the continuous construction of characteristic brands on the basis of enhancing the core competitiveness. challenges faced by enterprise economy under the impact of digital economy with the rapid development of digital economy, its influence on the economic development of enterprises is increasingly significant, thus providing new opportunities for the high-quality development of enterprise economy. it is of great theoretical and practical significance to study how digital economy promotes the endogenous driving force and high-quality development of enterprise economy. through the analysis of table 2 of the internalization analysis framework of the digital economy era, (xiangxiang, 2019) in the context of digital economy, the impact challenges facing the enterprise economy are mainly reflected in the following aspects: first，the enterprise economy lacks the standardization. at present, chinas digital economy is gradually developing, it is necessary to pay attention to the economic management of enterprises, which has a certain impact on the development of enterprise economy and operation quality, and is also very critical to improve the core competitiveness of enterprises. the continuous development of digital economy has brought opportunities and challenges to enterprises. therefore, only by constantly paying attention to economic management, can enterprises ensure that they can operate efficiently and achieve stable development. second，enterprise economy lacks the concept of digital transformation and digital talents. through the analysis of the current digital transformation of some small and medium-sized enterprises, we can know that some small and medium-sized enterprises in china are still exploring in the digital transformation, and some other enterprises are in the specific implementation stage. among them, many small and medium-sized enterprises do not have high enthusiasm for digital transformation and lack the concept of digital transformation. digital talent is also a key element of the economic transformation and development of enterprises. at present, the supply of digital talents is in short supply, and the competition of enterprises for digital talents is gradually intensifying. small and medium-sized enterprises have low innovation level, small scale, lack of competitive advantages, which makes the enterprise talent recruitment and talent cultivation face bottlenecks. third，the economic management mode in the enterprise has a lag and the management and supervision mechanism needs to be improved. the enterprise economic management mode is too traditional, which affects the improvement of the core competitiveness of enterprises. enterprise supervision and management mechanism is also an important enterprise development, the improvement of enterprise management supervision mechanism for management has a critical role, but the enterprise management mechanism is not perfect, there are many problems, the discovery of the future and realize the effective supervision of management work affected, and the enterprise did not timely give solutions, enterprise management implementation more difficult, lead to enterprises in the development operation without reliable management reference. table 2: internal analysis framework in the digital economy era internalization analysis conceptualization dimension interaction new classification and new roles apply the ability of local organizations based on transaction characteristics strong ability and complete system: “dc” enter the mode of the selection strong ability and incomplete system: “test field” institutional system of the host country weak ability and complete system: "practice platform" pathway of knowledge transfer weak ability and incomplete system: “oviating factory” pa ge 12 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 7-13, 2025 how to innovate and develop the enterprise economy under the background of digital economy first of all, improve the external environment of technological innovation to provide a guarantee for technological innovation. the cooperation between the government and various financial institutions and jointly participating in the technological innovation of enterprises is an important way to promote the technological innovation of major enterprises. the cooperation between the government and financial institutions can not only effectively solve the problems related between science and technology education and economic development, but also lay a good foundation for industrial technology innovation. secondly, to enhance the advanced level of production equipment, improve the internal environment of enterprises. each region should continuously increase the number of enterprises engaged in science and technology activities, the number of enterprises conducting research and development activities, the number of enterprises launching new products and the proportion of the establishment of science and technology institutions. when their own technological innovation level meets bottlenecks, they can establish a new internal environment of technology through virtual ways such as external cooperation, so as to improve their own technological innovation level and technological innovation efficiency (huixin, 2021) the optimal allocation of the internal environment of enterprise technological innovation is the key, following the principle of efficiency priority, scientifically allocating resources, and dynamically adjusting according to the efficiency. finally, establish and improve the talent incentive system, and increase the investment in technical personnel. the key of scientific and technological innovation lies in having high-quality technical personnel. in order to establish high-quality technical innovation talents, the governments policy support is an essential key factor. in order to promote corporate technologysurgical innovation and sustainable development, we need to strengthen the introduction and training of technical personnel. through the enterprise internal incentive policy to improve the enterprise internal talent cohesion results and discussion a new path of enterprise economic innovation and development under the background of digital economy in order to improve the enterprises sustained growth ability and core competition ability, the enterprise needs to take on, with the help of the power of digital transformation, the enterprise subversion potential transformation and rebirth upgrade, find the enterprise development goals, through the goal setting, leading the enterprise digital change, and through the digital change, swallow enterprise short board, to form a complete and scientific enterprise management system (lijun. 2019). digital transformation has become an irreversible trend, and its necessity and urgency are becoming increasingly obvious. and through this paper studies the relationship between the digital economy and enterprise economy, can strengthen the key core technology in the development of enterprise economy innovation path, make digital economy digital organization form for the new development path, further under the background of digital economy enterprise economic innovation development of new path to make new contributions. strengthen the innovation path for the development of key and core technologies in the enterprise economy scientific and technological innovation drives economic development, and technologies in key fields are at the core of social and economic development. our country to develop and perfect represented by 5g, industrial internet, digital infrastructure, speed up the traditional digital infrastructure, promote digital to intelligent social infrastructure, such as building intelligent transportation system, promote wisdom city technology services, promote digital public service resources, the construction of wisdom school, wisdom city, etc. from 1995 to 2010, the degree of technology dependence of chinas large and medium-sized industrial enterprises decreased significantly, from 71.81% to 8.77%, indicating that the independent innovation activities were gradually strengthened. the proportion of digestion and absorption input was also increased, from 3.63% to 42.78%, but the digestion and absorption rate was still low (wenying, 2021) although there is still a gap between chinese enterprises and developed countries in core technologies, the momentum of independent innovation is good, but it still takes time to achieve breakthrough innovation. huawei adhere to technological innovation, through independent research and development of core technologies and patents to promote industrial development. huawei has invested a lot of resources in research and development and now has more than 100,000 patents, giving huaweis products a unique competitive advantage in the market. huawei the success fully shows that technological innovation is an important means for enterprises to establish competitive advantage. the success of huawei also proves that the globalization strategy is in the enterprisethe important role in industry competitive advantage. huawei resource sharing and win-win results through open platforms and ecosystems. huawei the successful experience shows that in order to realize the internal connection between digital management innovation and the competitive advantage of enterprises, enterprises need to technological innovation, globalization strategy and open cooperation, optimize the allocation of resources by building global r&d and manufacturing networks, and realize innovation through cooperation with partners. to achieve the sustainable development of enterprises. build the digital organizational form of enterprise economy under the background of digital economy digital organization has a variety of forms, among them, the representative organization is the platform, flat and pa ge 13 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 7-13, 2025 promote enterprise organization and human resources, and give full play to the digital change effect, improve the comprehensive competition ability of enterprises, to help enterprises seize the opportunity of the times, realize the sustainable development of the enterprise. references cao, b. (2023). the impact of manufacturing digitalization on enterprise innovation. southwestern university of finance and economics. chen, c. (2023). tax risk management analysis based on internal control. commercial 2.0, 9, 22–24. dou, h. (2021). research on the innovation strategy of enterprise management under the new normal of the economy. the economic research guide, 25, 13–15. feng, g. (2021). research on enterprise management innovation under the background of the economic new normal. china’s collective economy, 25, 60–61. han, l. (2019). discussion on the influence of “internet +” on the innovation mode of modern enterprise economic management. modern marketing: information version, 6, 130. li, j., & zhao, d. (2022). research on the development of digital transformation from the perspective of enterprise management. modern commerce, 6, 126–129. tian, w. (2021). exploration on the standardized strategy of enterprise economic management mode under the background of the digital economy era. modern commerce, 34, 156–158. wang, y. (2022). research on enterprise tax risk control based on internal control. chinese and foreign corporate culture, 12, 46–48. wang, x., ju, z., kovshar, s. n., leonovich, s. n., & solopova, n. a. (2023). the use of non-metallic fiber in the protection of building materials and its impact on the environment. construction economics, 7, 86–91. xianpeng, w., & haoxuan, y. (2024). commercial economic value of non-metallic fiber concrete. google scholar. https://scholar.google.com.hk/scholar?hl=zhcn&as_sdt=0%2c5&q=commercial+econom1c+v alue+of+non-metallic+fiber+concrete.&btng= zhao, x. (2019). under the new normal. modern marketing (business version), 12, 138. flexible organization, digital organization with digital technology and management science for endorsement, can release in the virtual network and space more flexibility and convenience, can break through the limitation of physical space, links and assign traditional organization node, with fast information transmission and low communication cost, improve the operation efficiency of the enterprise. many enterprises at home and abroad have opened up their enterprise platforms, using digital technology to attract suppliers, consumers, and partners, building the ecosystem, to achieve sustainable development, when the influence of the enterprise in the platform reaches a certain level, enterprises can use the platform to earn more than the average profit; the flattening of the tissues reduces the path length of the information transfer, also reduces the distortion of the information, it also reduces labor costs and administrative costs, companies that use the digital technology, it can be uploaded in a more timely manner, strengthen the response ability of enterprises; enterprises building flexible organizations can eliminate the boundaries of enterprises to the greatest extent, let the information flow efficiently within the enterprise, improve the efficiency of internal and external interaction, it also stimulates the endogenous innovation power of enterprises. enterprises promote organizational change, with cutting-edge technology, enterprise scene and business model innovation, for enterprises development injects new movement. conclusion in this paper, through the development of the digital economy and the relationship and significance of enterprise economy, literature and concept, deeply discusses the background of the digital economy and how to promote the development direction of enterprise economic innovation, including the problems in its development and challenges, finally from the perspective of the digital economy, put forward about the effective way of industrial economic development. first, to strengthen the development direction of digital economy. thus, enterprises should take the initiative to put themselves in the era of digital economy background, deeply analyze their realistic conditions, combined with the development of digital, strengthen digital change management ideas, pa ge 1 pa ge 1 american journal of financial technology and innovation (ajfti) effects of board diversity on the earning quality of non-financial firms listed on the ghana stock exchange timothy masuni nagriwum1*, wiredu richard2, newman amaning3, matthew kuunyigr1 volume 1 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: september 20, 2023 accepted: october 12, 2023 published: october 18, 2023 to maximize wealth, corporate finance must carefully balance cash flows and the cost of capital for a corporation, which results in corporate governance. corporate governance guarantees openness, responsibility, equity, the organization’s long-term financial health, investor credibility, and the optimization of investor wealth. the study aimed to determine how board diversity affected the profitability of non-financial listed companies on the ghana stock exchange with a specific focus on the effects of gender diversity, age diversity, and nationality diversity on earnings quality. the study used a descriptive design and a quantitative research methodology. the data were examined and reviewed using spss version 23, stata 14, and excel. the study used secondary data that was collected for a period of 11 years (2011-2021) from the financial accounts of the five (5) non-financial institutions that were listed on the gse. from the perspective of non-financial listed firms on the ghana stock exchange, the study found that gender diversity and nationality diversity significantly influence earnings quality but age diversity does not have a significant impact on the earnings quality of non-financial listed companies on the ghana stock exchange. the study also recommended that management of publicly traded companies consciously adopt more diverse boards, especially in terms of gender diversity because it is associated with an increase in earnings quality. keywords board diversity, earnings quality, ghana stock exchange and non-financial firms introduction according to eka (2018), the goal of corporate finance is to maximize wealth, which necessitates a careful balance between cash flows and the cost of capital for a company. earnings that have not yet been paid in cash are categorized as normal accruals and abnormal accruals; higher abnormal accruals are associated with lower-quality earnings. profitability influences whether a business can get bank financing, attract investors to fund its operations, and grow. according to lazonick (2014), firms cannot continue to exist if they are not turning a profit. to maintain the standard of earnings and win the stakeholders’ trust and confidence, operational and non-operational income should be balanced. quality of earning, which measures the company’s actual growth as a result of operational activity, is the ratio of net functioning income to net income (abbadi et al., 2016). organizations place more emphasis on the quality of their earnings because if their operational revenue is strong enough, they can also sustain over the long term; nonoperational income is only a plus. most businesses define their earning evaluation criteria in terms of repeatable, controllable, and bankable (hashim et al., 2019). to meet predetermined goals and maximize profits while also attracting investors, it is crucial for managers and expert analysts to maintain quality in the earnings. strong corporate governance improves the firm’s standards and long-term performance. corporate governance ensures transparency, accountability, fairness, the long-term financial viability of the organization, investor confidence, and the maximization of shareholder wealth. the performance of a corporation is significantly impacted by the board’s culture and in commercial governance, the board of directors is important. the board’s structure and membership have an impact on performance and the quality of reported outcomes. chapple and humphrey (2014), the term “board diversity” is vague so numerous empirical studies have been done, focusing on various aspects of board diversity (gender, age, board independence, and nationality). busirin et al. (2015) indicated that an advanced number of independent directors on a board will reduce the tendency of earnings manipulation. one of the demographic characteristics that may affect important decisions made by ceos is ceo origin, such as accounting judgments that may affect earnings quality (shen et al., 2021). the corporate governance law, which was developed by ghana’s securities & exchange commission, increased the impact of corporate governance on businesses. however, to overcome this agency conflict and satisfy the interests of shareholders, managers should look for solutions that are commensurate with increasing shareholder wealth by raising the firm’s earning quality. this conflicting information tie-up has a negative impact on the financial statements. although there are no specific guidelines for corporate governance to follow, these tools are typically divided into internal and external methods. despite the lack of consensus, numerous studies have identified a strong correlation between corporate governance and earnings management for constructing and maintaining 1 school of finance and economics, jiangsu university, jiangsu, china 2 school of business, kwame nkrumah university of science and technology, kumasi, ghana 3 department of accountancy, sunyani technical university, sunyani, ghana * corresponding author’s e-mail: nagriwumtm@gmail.com pa ge 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 the earning quality of organizations. to prevent investors from making regrettable decisions as has frequently happened around the world when accounting fraud has been used financial statements should exactly reveal all facts needed by users to make well-versed decisions on a company’s worth, the value of its shares, and the precise future cash flows. by monitoring how firms are run while keeping in mind that managers’ interests differ from those of owners, the board of directors protects the interests of investors. to smooth out earnings, managers use the art of earning management to convert and manipulate the outcomes. this leads to the development of the idea of corporate governance, which aims to improve quality by curtailing irregular and non-accounting business operations. boards make sure that the interests of owners and managers are associated. in the context of ghana, adeabah et al. (2018) studied the effectiveness of ghanaian banks, corporate governance, and board gender diversity. in the context of ghanaian listed firms, kukah et al. (2016) concentrated on corporate governance practices and accounting information quality. the focus of boadi and osarfo’s (2019) study was on diversity and return: the effects of board members’ education diversity on performance. some research has been conducted to determine how board diversity and earnings management affect each other but this study was set out to fill a knowledge gap caused by the fact that previous research on the subject has not been conclusive, particularly in the case of developing countries like ghana with gross domestic product, firm size, firm sector and inflation as control variables from the viewpoint of listed non-financial firms on the stock exchange. the prime goal of this study was to investigate the connection between board diversity on the earnings quality of non-financial companies quoted on the ghana stock exchange by narrowing board diversity to; gender diversity, age diversity, and nationality. literature review conceptual review earning quality (eq) concept there is no established definition of earnings quality (eq) or method for determining it in literature (abdelghany, 2005; schipper & vincent, 2003). managers, accountants, auditors, and policymakers are all concerned with eq since capital markets depend on accurate and reliable financial information. regarding this, teets (2002) claimed that “higher quality earnings provide more information about the features of a firm’s financial performance that are relevant to a specific decision made by a specific decisionmaker”. with this description, “quality” depends on a particular decision context and is determined subjectively (dechow, ge, & schrand, 2010). to extract information from earnings patterns that are significant to value, investors, for instance, employ eq “as a conditioning variable” (francis, lafond, olsson, & schipper, 2003). even when reported earnings and the associated revelation comply with generally accepted accounting principles, false reporting is referred to in the financial press as an “earnings quality” issue. the press may not agree with standard setters, policymakers, and auditors on this matter because, in their opinion, earnings are of extraordinary quality if they adhere to the spirit and regulations outlined in gaaps and ifrss. conversely, when earnings are readily convertible into cash flows, creditors are more inclined to consider those earnings as being of good quality. otherwise, when compensation reflects managers’ actual performance and is mostly unaffected by circumstances outside of management control, compensation committees are likely to consider profits as being of high quality. these instances demonstrate how the concept of eq is driven by the decision-objective makers and the function that earnings play in the resolution model. eq study was reviewed in depth and detail by dechow and schrand (2010), who also offered other insightful observations that when earnings give decisionmakers additional details about a company’s financial performance, they are seen as being of higher quality. according to the authors’ analysis of eq from the perspective of financial analysis, earnings are of high quality if they “precisely annuitize the inherent value of the firm.” they distinguish this value quality by reporting an earnings number that is normalized, productive, or representative and equates to long-term earnings. they said that earnings have three qualities that make them of excellent quality: they provide a helpful summary for estimating business worth; they accurately reflect actual performance; and they predict future success. eq is the term used to describe the capacity of reported profits to properly represent the underlying earnings of the company as well as their efficacy in projecting future earnings. the relationship between the most basic indicators of a company’s performance, namely cash flows, and earnings, can be used to gauge emotional intelligence (eq). eq is related to the process by which a company converts its cash flows into reported earnings. board diversity concept board diversity is one of the most significant governance challenges in recent years (barako & brown, 2008). however as the workforce became more diverse in respect of gender, race, and age, the need for a more diverse board became more essential (darmadi, 2011). an organization benefits from having a diverse board of directors because it increases corporate leadership effectiveness, fosters market understanding, legitimizes businesses, forges international linkages, and improves corporate administration (van der walt & ingley, 2003 and erhardt et al., 2003). according to the agency idea, the more varied a board is, the more independent it will be, which will result in better management oversight (carter et al., 2007). diversity is described as the state of including or consisting of various elements, of diversity (cabrerasuárez et al, 2017). a collection of diverse individuals in terms of their cultures, races, backgrounds, etc. is referred pa ge 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 to as a group that has undergone diversification (ararat et al., 2010). board diversity hence refers to the diversity of the board. when discussing board diversity, people usually consider gender diversity; however, true diversity also includes things like cultures, races, educational levels, ethnicity, nationality, and more. numerous international organizations have made efforts to diversify their boards of directors and administration and ghana is not an exception since ghana is a nation with several different ethnic groups. the resource dependence theory, on the other hand, contends that board variety will boost the funds given by members, including expertise, knowledge, legitimacy, and access to important stakeholders (such as vendors, customers, federal policy decision-makers, and social groupings) (hillman et al., 2000). as a result, the board of directors diversity in relation to age, gender, and race would be able to offer the management special knowledge for improved decision-making (ayuso & argandoa, 2007). these may affect a company’s earning potential. while gender diversity, as per perryman et al. (2016), aids in enhancing the stock charge in information by encouraging the collection of private data in small businesses and increasing public disclosure in large corporations. gender diversity gender variety is the term used to describe situations in which a person’s gender identity, role, or manifestation differs from the expectations placed on members of a particular sex in society. this expression is being used to refer to people more frequently. gender identities are said to exhibit gender diversity when they display a range of expressions outside of the binary framework. many gender-diverse people find the concept of binary gender, which forces you to decide whether to manifest yourself as male or female, to be limiting. some people would want to have the freedom to alter their gender or not identify at all. others merely want the ability to publicly disagree with or reject more prevalent gender stereotypes. gender diversity has been incorporated into the larger idea of board diversity (carter et al., 2003). carter et al., (2003), allude to the fact that there are female directors on the board of directors of the corporation. women directors provide a variety of viewpoints, experiences, and working methods to the board which improves decision-making and the board’s debate. age diversity the acceptance of people of different ages in a professional context is referred to as age diversity. businesses can take action to combat ageism at work and address the aging population. age can be viewed as a board asset and is part of human capital, per sonnenfeld (2002) and darmadi (2011), because it can replicate experience and risk-taking. however, in the business world of today, youthful directors are constantly involved, while the majority of board members are older (benjamin et al, 2018).young directors could provide the organization with fresh insights and ideas. there is a claim that youthful directors are more imaginative and have a greater capacity to process fresh ideas (van ness et al, 2010). additionally, they have a better relationship with strategic change and are more eager to take part in the control process (darmadi, 2011). the board’s effectiveness and decision-making may be improved as a result. nationality diversity several governance principles promote the appointment of members of various nationalities to the board of directors to reflect the national variety of its stakeholders, employees, and consumers (fidanoski et al, 2014). additionally, it is believed that adding foreign directors to the board can enhance the quality of the decision-making process (van den et al, 2005). according to resource dependence theory, foreign directors can offer a variety of viewpoints, have distinct cultures and behaviors, as well as varied life experiences, all of which may be able to advance decision-making and the business’s plans (ruigrok et al, 2007; ayuso & argandona, 2007). it was suggested that hiring foreign directors would help the team make better decisions since they would provide a variety of viewpoints and opinions about the country or region’s culture, language, life experiences, religion, and social customs (ruigrok et al., 2007). according to ayuso and argandona (2007), the knowledge of foreign directors enhances corporate strategy decisions, for instance, by supporting csr reporting techniques, while also increasing board capital (which eventually may lead to improved financial performance). theoretical review identifying current theories, their connections, the depth of their research, and the creation of new testable hypotheses are all aided by a theoretical literature review. many corporate governance ideas incorporate stewardship theory and agency theory. agency theory in 1952, jensen and meckling created the agency hypothesis. according to jensen and meckling (1976), an agency relationship is defined as the parameters of an agreement whereby the principal appoints a distinct agent to carry out tasks like exercising decision-making authority on his behalf. regarding the agency theory, the degree of firm complexity and the possibility of agency benefits have an impact on managers’ capacity to alter data and manipulate earnings, both of which may be crucial. a large company with complex organizational structures and agency issues is deemed to be more varied than an industry or a nation (padilla, 2000). according to jensen and murphy (1990), managers are also urged to call for diversification to increase their pay, as well as for status and authority that help preserve pa ge 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 their post within the company by managing particular investments to reduce the risks associated with both their personal investments and the organization’s investments. one of the motivations for managers to limit costs is agency cost. per agency theory, controlling earnings is prioritized in the management of figures so that they can profit from the contracting procedure, according to actual data. numerous studies have documented the existence of information asymmetry between investors and directors, which is a prerequisite for effective earnings management (yu, 2008). as a result of the shareholders receiving less information, the internal management may have used its point to manage and influence the reported earnings (amihud et al, 2006). given that boards of directors represent shareholders while managing the operations of the company, agency theory is extremely pertinent to the current research on the link between board diversity and earnings management. agency issues may arise if the directors act in their self-interest, for as by falsifying financial records to present a positive performance picture, particularly if their compensation is contingent on the success of the company. stewardship theory according to barbuto & wheeler, the stakeholder theory merged the fields of sociology and organizational studies (2006). according to the stakeholder theory, the institution’s goals might be affected or impacted by a group of people. the systems of connections that administrators must manage include those with employees, traders, and business partners. additionally, it is asserted in this theory that the set of systems is more significant than the relationship between the employer and employees as described in the agency theory. the incentive mechanisms in share options provide the managers with a way to justify their exceptional overpaying. keasle et al (1997). since executive compensation has grown significantly quicker than the average wage and there is a poor correlation between managerial performance and pay, executive power is being misused. this is especially related to the issue of overpay (conyon et al, 1995; brennan et al, 2008). the development of independent remuneration committees, as is the case in large businesses, is ineffectual, and the humility of the managers is the only important factor that can limit executive pay (owen, n., 2018) the analysis of how board diversity affects the management of profitability in publicly traded manufacturing organizations can be done using the stakeholder theory. the study outlines the many parties that aside from owners whose control might affect a firm’s survival, have a stake in the administration of manufacturing enterprises. the proponents of this model argue that the most effective control mechanisms are the main lines of modifications in corporate governance, such as non-executive directors, shareholder participation in important decision-making, and complete disclosure of company affairs (kay and silberston, 1995). empirical review gender diversity and earnings quality the study by ain et al (2021) uses a sizable sample spanning the years 2003–2017 to evaluate the connotation between gender diversity on the board and dividend payouts in china. our findings offer solid and convincing proof that gender diversity on the board is favorably linked to dividend payments made in cash. the empirical results back the idea that gender diversity on the board improves corporate governance, which in turn encourages dividend payments. they demonstrate that the benefit of gender diversity on the board is greatest when there is a critical mass of engagement (three or more female directors), as opposed to just nominal engagement. female independent directors have a considerable impact on dividend payouts, but female executive directors do not. we also add to the body of knowledge on the relationship between dividend payments and public control by providing data demonstrating that gender diversity has a bigger impact on dividend payouts for state-owned businesses than for non-state-owned enterprises. our results are trustworthy and solid after the endogeneity issues are taken into account. using data analysis of 152 businesses quoted on the tehran stock exchange between 2011 and 2016, kazemi and abdi’s (2019) study intended to evaluate the effects of gender diversity (at least one female delegate on the board of directors and in the audit committee) on profit quality. the archive-based method was used to collect the data, and regression analysis with the imbalanced panel data method was used to evaluate the hypotheses. the findings showed that having women on audit committees had a big impact on the quality of earnings. also, the results showed that gender variety on the board of directors does not significantly affect the quality of the company’s earnings. when women are well-represented among senior business executives, financial reporting and managerial control are of higher quality. the audit committee and board of directors are consequently more impartial, which raises the standard of earnings. the results show that having female directors with significant financial experience improves earnings quality more than doing without them. additionally, our findings show that the only female directors who can minimize earnings management are those who have relevant financial expertise and fewer outside directorships. the study found no indication that female directors without the necessary financial skills may mitigate profit management, irrespective of their external directorships or duration. the study by dimitrova (2017) examines the impact of social connections between ceos and board members as well as the gender of these ceos on the standard of earnings as determined by earnings administration. the scrutiny of financial reporting has intensified in recent years as a result of several accounting scandals, which have also brought to light the value of sound corporate governance. the board should be independent and diverse to lessen agency conflicts. although previous research pa ge 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 has not shown definitive results, social bonds may reduce independence. according to dimitrova (2017), connected ceos and profit quality should be negatively correlated. in a professional setting, gender diversity is a subject that is frequently explored. women are generally thought to be more ethical at work, which implies that they take part in less earnings administration and hence produce higher-quality earnings. the hypothesis is tested using a sample of 99 uk-listed companies with 198 observations spanning two years (2015 and 2016). age diversity and earnings quality hoang et al. (2017), in a survey of vietnamese public companies, the study looked at how board diversity affected the quality of the earnings. a wide range of structural and demographic aspects of a board of directors are covered by the two dimensions of board diversity measures used in this study, which include a diversity-of-boards index (dissimilarities among company boards, i.e., board structure) and a diversity-in-boards index (dissimilarities among directors within a board, i.e., demographic features of board members). four accounting-based factors accruals quality, earnings consistency, earnings predictability, and accruals smoothness combine to provide the overall indication of profits quality. they find a non-linear, u-shaped link between the two variables, but a strong, significant linear association between the diversity of boards and earnings quality. almomania et al (2020), study looks at how board diversity and profits quality relate to a sample of amman stock exchange-quoted companies (ase). there were 68 firms in the sample from 2010 to 2019, totaling 680 firm-year observations. the yearly reports of companies registered on the ase were used to gather secondary data. the discretionary accruals (da) model developed by kothari in 2005 was used to gauge the quality of earnings. board gender, board experience, board age, and board religion were used to measure board diversity. board age, experience, and gender all have a big impact on earnings quality, but board religion does not. this shows that a key explanation for the quality of earnings is provided by corporate governance. this study shows how a diverse board can improve the earnings quality of companies quoted on the ase. additionally, this research reveals that the board of directors has a crucial role in promoting corporate governance. a more diversified board of directors should be encouraged and should also ensure that listed companies’ corporate governance is effective. nationality diversity and earnings quality hashim et al. (2019), the study aimed to investigate the connection between board diversity and the quality of earnings in the companies listed on bursa malaysia main market. malaysia has a multi-ethnic population with many distinct beliefs, which may have a good impact on the standard of earnings. in order to improve the firms’ profits quality, the study also looks at whether internal audit functions are carried out internally or externally. it is discovered that ethnic and national diversity significantly affects the sampled companies’ earning quality. age and gender diversity, however, do not seem to have a major consequence on the quality of wages. the quality of the companies’ earnings will be able to rise with the inclusion of more representatives of various races on the board. by examining listed corporations in pakistan, khan and abdul subhan (2019) look into how board diversity and high-quality auditing affect financial performance. the diversity of the board is examined in terms of gender and nationality. even though many firms desire a diverse board composition, it is unclear how this will affect business performance. this study showed an intriguing correlation between board diversity and firm financial performance. higher audit expenses lead to more efficient audit services when compared to organizations with lower audit prices. sector representation and the largest market capitalization are taken into consideration while choosing the pse-100 index. a panel data collection with a time range of 2008 to 2017 is gathered. in its methodology, the study used panel data and quantitative econometric methodologies to close the research gap in the body of existing governance literature. according to research, nationality diversity is inversely correlated with corporate financial performance, primarily as a result of communication hurdles and varying crosscultural perspectives. due to extended audit hours and skilled audit employees conducting a more thorough inquiry, which costs more in audit fees, high audit cost implies a good quality audit. kouaib and almulhim (2019), examined the question of whether an audit index controls the association between boardroom diversity in terms of gender and foreign directors and earningsmanagement practices in the european environment. a moderation model was tested using information from a sample of 429 european companies featured on the stoxx europe 600 index between 1998 and 2017. evidence shows that non-european directors are linked with earnings-management activities, while accruals-based and real earnings-management activities are inversely associated with board gender diversity. the relationship between board diversity and earnings management is further dramatically moderated by the audit index. this study is distinctive in that it offers european proof of the controlling role of audit quality in the relationship between board member demographics and business performance. haruna et al. (2018), investigated how board characteristics affect the profitability of nigerian conglomerate enterprises. the secondary source of data gathering was the audited accounts of nigerian conglomerate corporations, the gathered data was examined using two steps of regression. the outcome showed that the board characteristics proxies significantly influence the earnings quality of nigerian conglomerate enterprises. this proves that board qualities are important in restricting managers’ unethical behavior in nigerian conglomerate enterprises and enhancing the quality of earnings. pa ge 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 conceptual framework a conceptual framework incorporates one or more formal theories, additional concepts, and actual evidence from the literature, either entirely or in portion (horn and brem, 2013). it is used to show how these ideas are related to one another and how they relate to the research issue. as shown in figure 1, gender diversity, age diversity, and nationality diversity are used as the independent variables which are components of board diversity, the dependent variable is earnings quality. ali et al. (2015) asserted that the firm size and its sector of operation influence the earnings quality of an organization. therefore the study used firm size, firm sector, inflation, and gross domestic product as control variables. figure 1: conceptual framework source: author’s construction (2022) methodology the approach used in this investigation is demonstrated in this section. the study used a descriptive design in its methodology. descriptive design studies are concerned with the description of characteristics of individuals or groups (lenz et al., 2016). descriptive research helps researchers to accurately evaluate the background of a research problem before doing a more in-depth examination by clearly and specifically identifying the independent and dependent variables being studied. as of december 31, 2020, there were 25 non-financial firms registered on the ghana stock exchange. from 2011 to 2021, five (5) non-financial firms registered on the ghana stock exchange were specifically chosen using the purposive sample method with eleven-year period of information span. the sampled firms are guinness ghana breweries plc, fan milk limited, goil plc, golden star resources ltd, and unilever ghana plc. firms that were not listed between 2011 and 2021, firms without financial statements for the study period, table 1: measurement of variables s/n description measurement source prediction dependent variable 1 earnings quality dividing total earnings or total net income by the total number of outstanding shares. dechow et al. (2010) independent variables 2 gender diversity the ratio of female directors to the total number of directors owen (2018) + 3 age diversity the percentage of young to the total number of directors of the company owen (2018) 4 nationality foreign directors to the total number of directors on the board owen (2018) + control variables 5 gdp the sum of what is purchased in the economy gss (2020) + 6 firm size log of total assets of the firm owen (2018) + 7 firm sector firm sector operating darmadi (2011) + 8 inflation the rate of change of those prices gss (2020) + source: author’s construction pa ge 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 and firms with published audited accounts that did not show the corporate governance system by disclosing board diversity information were not included in the study sample, hence this methodology was appropriate. information for the study was based on secondary data, particularly the financial statements of the five (5) selected registered non-financial companies on the gse from 2011 to 2021.additionally, academic journals, scholarly papers, pertinent textbooks, and web search engines were utilized. dependent variables, independent variables, and control variables were appropriately measured. the elements which include gender diversity, age diversity, nationality diversity, earnings quality, firm size, firm sector, inflation, and gross domestic product were considered as the variables in the study. excel and stata 14 are employed to evaluate the data that was gathered. regression analysis is used in the research to determine the relationship between the variables being examined. the research used linear regression data analysis methods to analyze the collected data, both descriptive and correlation. in the data analysis, cross-sectional and time series data were integrated. descriptive statistics are used to quantify the main performance variables using mean, maximum, minimum, and standard deviations. as part of the validity and reliability assessments, the study also performed diagnostic tests: multicollinearity and heteroscedastic. model specifications the paper adopted a regression model to institute the connection between the variables in the study as recommended by hair, et al. (2006). the model specification was presented as: eq= β0 + β1 size + β2 fs + β3 inf + β4 gdp +β5gd + ε……model 1 eq= β0 + β1 size + β2 fs + β3 inf + β4 gdp+β6 ag + ε……model 2 eq= β0 + β1 size + β2 fs + β3 inf + β4 gdp+ β7 na + ε……model eq = earnings quality size = firm size fs = firm sector inf = inflation gdp = gross domestic product gd = gender diversity ag = age diversity na = nationality α = the intercept β = coefficient of independence variables ε = error term within a confidence interval of 5% results and discussions descriptive statistics table 2 shows the descriptive statistics of the variables used in the study. the variables include earnings quality (eq), gender diversity (gd), age diversity (ad), nationality diversity (na), firm size (size), firm sector (sec), inflation (ifl), and gross domestic product (gdp). as shown in table 2, in the case of earnings quality (eq), the maximum amount recorded within the year is 0.930, and the minimum value is -0.750. the mean for the period is 0.20 also recording a standard deviation of 0.30. this means on average there is an increase of 0.20 in the earnings quality from 2011 to 2021. table 2: summary of descriptive statistics variable obs. mean std. dev. min max earnings quality 55 0.20 0.30 -0.750 0.930 gender diversity 55 0.23 0.12 0.090 0.500 age diversity 55 0.23 0.23 0.039 0.900 nationality 55 0.46 0.26 0.000 0.780 firm size 55 9.02 3.42 3.494 18.000 firm sector 55 1.40 0.49 1.000 2.000 inflation 55 11.71 3.45 7.140 17.450 gdp 55 5.97 3.66 0.510 14.050 ghana stock exchange (2021) it was also revealed that the mean value for gender diversity was 0.23 with a standard deviation of 0.12, the smallest rate recorded was 0.090 and the maximum value of 0.500 was recorded. this result implies that there is less gender diversity on the boards of the firms sampled. this indicates that men and women are not engaged at a balanced rate. the maximum rate for age diversity was 0.900 and a minimum of 0.039. the standard deviation rate was 0.23 and the mean of 0.23. this result implies that age diversity is crucial for a welcoming environment which is challenged since a low mean value was recorded. in respect of the board member nationality, the mean value of 0.46 and standard deviation of 0.26 are recorded. the minimum value recorded was 0.000 and a maximum value of 0.780 was also recorded. with a minimum value of 0.000, this means some of the board does not include foreign members, and also with a standard deviation of 0.26, most of the board have foreigners as board members. with respect to the firm size, it was established that the average size is 9.02 and a standard deviation of 3.42, the maximum size recorded was 18.000 and the minimum pa ge 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 value is 3.494. recording a mean of 9.02 implies that on average over the period of eleven years from 2011 to 2021, the firm increased by 9.02 in size. the firms were categorized as manufacturing or non-manufacturing which was labeled as a firm sector, the average rate recorded was 1.40 with a standard deviation of 0.49, a least value of 1.000, and a supreme value of 2.000. this implies both manufacturing and non-manufacturing are involved in the study. with respect to inflation the mean value of 11.71 and the standard deviation of 3.45. the minimum value recorded was 7.140 and the maximum value of 17.450. this implies the highest inflation recorded within the period of the study was 17.450 and the lowest was 7.140 and on average, the rate of increase in the rate was 11.71.with respect to gross domestic product, the minimum rate for the period was 0.510 and the maximum 14.05, with a mean of 5.97 and a standard deviation of 3.66. this implies the highest gdp rate recorded within the period of the study was 14.05 and the lowest was 0.510 and on average, the rate of increase in the rate was 5.97. augmented dickey-fuller (adf) test the first difference is that all five series appear stationary, and correlograms confirm this by showing that acfs tend to zero rather quickly. after taking the first difference, the study uses the unit root test with augmented dickeyfuller to determine whether the series is now stationary or not, and the results are shown in table 3. according to cheung and lai (1995), when the p-value is greater than 0.05, the null hypothesis (h0) is not excluded, the data has a unit root, and it is non-stationary; when the p-value is less than 0.05, the null hypothesis (h0) is excluded, the figures do not have a unit root, and it is stationary; and when the p-value is less than 0.05, the figures does not have a unit root, and it is stationary. the results of the augmented dickey-fuller test in table 3 indicated that there is a unit root based on the p-values of all five series, as the p-values are insignificant. the calculated adf test-statistic values of the five sequences are less than the critical values at the 1%, 5%, and 10% levels of importance, with dissimilar lag lengths (based table 3: augmented dickey-fuller (adf) test results eq gd ad na size sec ifl gdp test critical values 1% level -2.559 -3.750 -3.750 -3.750 -3.000 -3.000 -3.000 -3.000 5% level -3.750 -3.000 -3.000 -3.000 -2.400 -2.400 -2.400 -2.400 10% level -3.000 -2.630 -2.630 -2.630 -2.104 -2.104 -2.104 -2.104 t-statistic -2.559 -2.345 -4.563 -1.219 -1.876 -3.650 -1.105 -0.975 lag length 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 prob 0.102 0.158 0.522 0.665 0.126 0.418 0.474 0.532 note: earnings quality (eq), gender diversity (gd), age diversity (ad), nationality diversity (na), firm size (size), firm sector (sec), inflation (ifl), and gross domestic product (gdp). source: author’s estimation. on schwarz information criterion). as a result, we reject the null hypothesis that all three sequences have a unit root. according to the augmented dickey-fuller results, we concluded that all five series are stationary. test of heteroscedasticity there are several methods of detecting heteroscedasticity in regression models. however, the present study resorted to using the breusch-pagan godfrey heteroscedasticity test due to its robustness and wide acceptance. if the probability of the f-statistics of the test show significance that implies that there is a presence of heteroscedasticity. as shown in table 4, the models showed significance which suggests that heteroscedasticity was not a problem in the study. correlation matrix table 5 displays the correlation matrix. in a correlation study, the correlation value should not exceed 0.8 for that variable. values greater than 0.8 indicate a multicollinearity problem. as shown in table 5 none of the variables recorded a multicollinearity problem since all the correction values table 4: breusch-pagan / cook-weisberg test for heteroscedasticity ho constant variance variables fitted values of eq chi2 (1) 0.09 prob > chi2 0.762 source: author’s estimation. table 5: correlation matrix ifl gdp size gd ad na sec eq ifl 1 gdp -.472** 1 0.000 size 0.054 -0.126 1 0.696 0.358 pa ge 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 are below 0.8. the results showed a mean of 0.82 and a standard deviation of 0.12. the minimum and greatest values that were noted were 0.560 and 0.940, respectively. as a result, the majority of the board members are not now working for the company or its auditor, and neither does their employer do a lot of business with the organization. it was revealed that eq correlates with size (p=0.000, r=-0.467), and sec (p=0.033, r=-0.288). sec is also found to correlate with size (p=0.000, r= 0.587), gd (p=0.000, r= -0.516), ad (p=0.002, r= -0. 413) and na (p=0.000, r= -0.495). also, na is found to correlate with gd (p=0.015, r=0.326). multiple regression analysis a model that establishes the relationship between the control, independent, and dependent variables is multiple regression analysis. this analysis’s goal is to predict how the sampled secondary data will turn out. the goal of this analysis is to develop models of the relationship between the collected explanatory and secondary data. gender diversity and earnings quality the first regression was to analyze the relationship flanked by gender diversity and earnings quality as indicated in model 1 of this study. the result is presented in table 4.5. the investigation starts with a review of the model summary. this model describes the regression line’s capacity to fully explain the difference in the dependent variable. the r square is the second piece of information discovered by the researcher. the value of r square is 0.2903, which equals 29.03 percent. this means that the independent variable which is gender diversity and the control variables (firm size, firm sector, inflation, and gdp) explain 29.03 percent of the total variance. table 6 also summarizes the study model’s overall fit. the number of observations (55) simply refers to the number of observations used in the regression. f (5, 49) represents the f-statistics of the model-based anova test. the f-statistic analyzes whether there is a statistically momentous difference between the ratios explainable to inexplicable mean-variance. simply, the models and residual degrees of liberty are represented by the numbers 5 and 49, correspondingly. to find out how effectively the indicators (as a whole) predict the dependent variable, stata does a hypothesis test. according to the null hypothesis, the mean-variance table 6: gender diversity and earnings quality source ss df ms number of obs 55 f( 5, 49) 4.01 model 1.40 5 .28018 prob > f 0.004 residual 3.42 49 .0699 r-squared 0.2903 adj r-squared 0.2179 total 4.83 54 .089363405 root mse 0.26437 earnings quality coef. std. err. t p>|t| [95% conf. interval] firm size -0.034 01637 -2.05 0.045 -0.07 0.00 firm sector -0.009 .09107 -0.10 0.922 -0.19 0.17 inflation -0.001 .011817 -0.06 0.954 -0.02 0.02 gdp -0.019 .01137 -1.65 0.106 -0.04 0.00 gender diversity -0.034 .03278 -1.04 0.302 -0.10 0.03 _cons 0.734 .2175 3.37 0.001 0.30 1.17 source: author’s estimation. gd -0.203 0.026 -.434** 1 0.136 0.849 0.001 ad -0.057 -0.053 0.094 .331* 1 0.677 0.702 0.494 0.014 na -0.012 -0.003 -0.134 .326* 0.022 1 0.930 0.985 0.330 0.015 0.876 sec 0.000 0.000 .587** -.516** -.413** -.495** 1 1.000 1.000 0.000 0.000 0.002 0.000 eq 0.079 -0.176 -.467** .390** -0.030 -0.016 -.288* 1 0.566 0.199 0.000 0.003 0.826 0.909 0.033 note: earnings quality (eq), gender diversity (gd), age diversity (ad), nationality diversity (na), firm size (size), firm sector (sec), inflation (ifl), and gross domestic product (gdp). source: author’s estimation. pa ge 10 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 that can be understood easily is related to the average variance that cannot be explained. the mean summation of the squares of the model is approximately four times larger than that of the residual. the possibility of attaining the predicted f-statistics or greater is indicated by the prob > f. (the p-value). the transitional that the ms of the model is considerably bigger than the residual must be accepted if the study’s p-value is less than 0.05 for a standard alpha level of 0.05. as an outcome, our model’s predictors precisely predicted the aim of the variable. in the case of the control variables firm size, firm sector, inflation, and gross domestic product recorded p-values of 0.00, 0.17, 0.02, and 0.00 respectively. this implies that three control variables (firm size, inflation, and gross domestic product) influence the dependent variable which is earnings quality when the independent variable is gender diversity. also, the independent variable gender diversity recorded a p-value of 0.03. the study results imply that gender diversity has a significant influence on earnings quality among the listed firms. age diversity and earnings quality the regression analysis for the relationship between the control variable, age diversity as an independent variable, and earnings quality is examined and the results are presented in table 7. table 7: age diversity and earnings quality source ss df ms number of obs 55 f( 5, 49) 3.71 model 1.33 5 .2650 prob > f 0.006 residual 3.50 49 .0714 r-squared 0.275 adj r-squared 0.201 total 4.83 54 .0893 root mse 0.267 earnings quality coef. std. err. t p>|t| [95% conf. interval] firm size -0.044 .0149 -2.96 0.005 -0.074 firm sector 0.008 .1122 0.07 0.944 -0.218 0.233 inflation -0.001 .0120 -0.06 0.955 -0.025 0.023 gdp -0.020 .0114 -1.74 0.088 -0.043 0.003 age diversity 0.012 .1946 0.06 0.95 -0.379 0.403 _cons 0.707 .2378 2.98 0.005 0.230 1.185 source: author’s estimation. in the case of model 2, age diversity is used as the independent variable while earnings quality was used as a dependent variable with control variables of firm size, firm sector, inflation, and gdp. as shown in table 7, an r-square of 0.275 was recorded this means the age diversity with the control variables explains 27.50% of the dependent variable. the possibility of attaining the predicted f-statistics or greater is indicated by the prob > f. (the p-value). if the inquiry provides results that support the alternative hypothesis that the model’s ms is significantly greater than the lingering effects of the null hypothesis, it is shown by a p-value less than 0.05 for a standard alpha level of 0.05, as illustrated in table 7. as a result, the predictors in our model successfully forecast the desired variable. with respect to model 2, the control variables which include firm size, firm sector, inflation, and gross domestic product recorded p-values of 0.005, 0.944, 0.955, and 0.088 respectively. this implies of one the control variables which is firm size influences the dependent variable which is earnings quality as the independent variable. also, the independent variable age diversity recorded a p-value of 0.06. the study outcomes imply that age diversity has little or unimportant influence on earnings quality among the listed firms. nationality and earnings quality this section presents the multiple regression analysis for model 4, where nationality is used as the independent variable. the analysis includes the dependent variable and the control variables and the result is presented in table 9. as shown in table 9, the prob > f value recorded was 0.005 which implies that the model significantly predicts accurately the target variable. since the p-value is below 0.05. model 4 is found to explain the dependent variable by 28.40% since the r-squared value recorded was 0.284. among the control firm size was the only control variable found to significantly influence earning quality since a p-value of 0.004 was recorded. in the case of the other control variables, firm sector, inflation, and gdp the p-values chronicled were 0.712, 0.939, and 0.088 respectively which are above the theoretical level of 0.05. the dependent variable which is nationality recorded a p-value of 0.022 which means that nationality has a significant impact on the earning quality of a firm. this means that nationality has an influence on earning quality. pa ge 11 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 results and discussion the primary objective of the research was to investigate the connection between board diversity and the profitability of non-financial companies registered on the ghana stock exchange. according to francis et al. (2003), investors use eq as a conditional variable to source high valuation knowledge from earnings trends. earnings are regarded as being of greater quality when they provide decision-makers with additional information about a firm’s financial results (dechow et al., 2010). the study’s goal was to determine how gender diversity among non-financial registered firms on the ghana stock exchange affects earnings quality. gender diversity was found to have a p-value of 0.022, which is less than the p-value of 0.05. this suggests that the profitability quality of non-financial registered firms on the ghana stock exchange is significantly impacted by gender diversity. similar to this, ain et al. (2021), study shows that gender diversity on the board has the biggest influence when there are three or more female directors, as opposed to just one or two. kazemi and abdi’s (2019) findings also show that the quality of earnings is significantly affected by the presence of women on audit committees. instead, the findings indicated that the quality of the company’s earnings is not significantly impacted by gender diversity on the board of directors. when women are well-represented among senior business executives, financial reporting and managerial control are of higher quality. also, zalata et al. (2022), findings indicated that only feminine directors with relevant financial perspectives and fewer outside directorships can reduce earnings management; as a result, overcommitting seasoned female directors with more exterior directorships would decrease their monitoring ability. regardless of their outside directorships or tenure, the study did not identify any evidence indicating that female directors without appropriate financial expertise can mitigate profit management. the study’s second goal was to investigate the impact of age diversity on the profitability of non-financial registered firms on the ghana stock exchange. according to sonnenfeld (2002) and darmadi (2011), age can be viewed as a broad asset and is part of human capital because it can reflect experience and risk-taking. in the business world of today, youthful directors are constantly involved, while the majority of board members are older (gilpatrick, 2000). age diversity has a p-value of 0.500, which implies that it has no apparent impact on the earnings quality of nonfinancial companies listed on the ghana stock exchange, according to the study’s findings. this result shows that the quality of profits of non-financial companies listed on the ghana stock exchange is unaffected by the age diversity of the board of directors. however, hoang et al. (2017) study contradicts this study’s findings, showing a non-linear, u-shaped link between age diversity in boards and earnings quality instead of a strong and positive linear relationship between age diversity of boards and earnings quality. the same is true of almomania et al. (2020), who discovered that while board religion has no momentous influence on earnings quality, board gender, board experience, and board age do. this shows that a key explanation for the quality of earnings is provided by corporate governance. examining the impact of nationality on earnings quality among non-financial listed firms on the ghana stock exchange was the study’s third goal. according to fidanoski et al. (2014), there are several governance principles that promote the appointment of members of various nations to the board of directors to reflect the nationality variety of its stakeholders, consumers, and employees. the findings of the study indicate that nationality has an impact on the standard of earnings. this finding suggests that nationality diversity has a major impact on earnings quality. the study’s findings supported the claim made by van den et al (2005) that the addition of foreign directors can enhance the standard of the board’s table 8: nationality and earnings quality source ss df ms number of obs 55 f( 5, 49) 3.88 model 1.368 5 .27365 prob > f 0.005 residual 3.457 49 .0705 r-squared 0.284 adj r-squared 0.210 total 4.826 54 .0893 root mse 0.266 earnings quality coef. std. err. t p>|t| [95% conf. interval] firm size -0.041 .0135 -3.05 0.004 -0.0687 -0.014 firm sector -0.039 .1061 -0.37 0.712 -0.2527 0.174 inflation -0.001 .0118 -0.08 0.939 -0.0248 0.023 gdp -0.020 .0113 -1.74 0.088 -0.0425 0.003 nationality -0.447 .1887 -2.37 0.022 -0.8270 -0.067 _cons 0.812 .2511 3.23 0.002 0.3070 1.316 source: author’s estimation. pa ge 12 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 1(1) 1-14, 2023 deliberations. additionally, the research by hashim et al. (2019) demonstrates that ethnic and national diversity is found to significantly affect the earning quality of the selected companies. age and gender diversity, however, do not appear to have a major effect on the quality of wages. the findings of khan and abdul subhan (2019) presented an intriguing picture of board diversity and company financial performance. due to varying crosscultural views and communication hurdles, nationality diversity is inversely correlated with corporate financial performance (khan and abdul subhan, 2019). evidence from kouaib and almulhim (2019) shows that non-european directors are positively linked with earnings-management activities, whereas accruals-based and real earnings-management activities are inversely associated with board gender diversity. results from makhlouf et al. (2018) demonstrate that accounting conservatism is highly positively connected with gender diversity, education level, and nationality diversity. the data, however, do not demonstrate any appreciable impact of directors’ age on accounting prudence. conclusions this research’s key goal was to examine the connection between board diversity and the earnings quality of non-financial companies quoted on the ghana stock exchange. the study revealed that the board diversity variables, including gender and nationality diversity, affected the company’s earning quality. while it was discovered that age diversity had little or no impact on earnings quality. overall, it was discovered that board diversity significantly and favorably impacts the earnings quality of the businesses registered on the ghana stock exchange. the study concluded that businesses with a more diversified board seem to be more likely to have earnings that are of higher quality. the study advises management of publicly traded companies to actively adopt more diverse boards, particularly in terms of gender diversity because it is linked to an increase in earnings management. companies’ shareholders should be aware of the advantages of having a gender-diverse board, particularly in thwarting management’s manipulation of the books of accounts to portray a particular image. to reduce the number of enterprises that fail in large numbers, the study also wants to advise policymakers to keep implementing the laws pertaining to gender diversity. the financial markets should ensure that diversification standards are carefully adhered to, to prevent accounting fraud and businesses’ widespread failure due to long-term losses concealed by accounting fraud meant to show successful performance. the research recommends that another research be undertaken that includes both secondary and prime data since some elements of board diversity cannot be adequately studied via the use of secondary data. another study should be carried out on non-financial companies as well as other companies that are not non-financial companies to encourage cross-industry comparison since the present investigation was only carried out in ghana’s publicly traded non-financial companies. references abbadi, s. s., hijazi, q. f. and al-rahahleh, a. s., (2016). corporate governance quality and earnings management: evidence from jordan. australasian accounting, business and finance journal, 10(2), 54-75. adeabah, d., gyeke-dako, a., & andoh, c. 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(2008). analyst coverage and earnings management. journal of financial economics, 88(2), 245271. zalata, a. m., ntim, c. g., alsohagy, m. h. and malagila, j., (2022). gender diversity and earnings management: the case of female directors with financial background. review of quantitative finance and accounting, 58(1), 101136. pa ge 1 pa ge 89 american journal of financial technology and innovation (ajfti) enhancing decision-making efficiency through production process diagnostics jiaqi yang1*, oksana kudriavtseva1 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4038 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: january 15, 2025 accepted: february 17, 2025 published: june 28, 2025 in response to fragmented approaches in green manufacturing research, this study proposes an integrated decision-support framework that unifies production process diagnosis, multiresource optimization, and data-driven analytics to enhance sustainability in complex manufacturing systems. combining theoretical modeling (e.g., dynamic resource-element networks), empirical case studies (12 cross-industry cases in automotive, electronics, and textiles), and systematic diagnostics, the research addresses inefficiencies in traditional erp-mes-pcs architectures, where manual decision-making and disconnected data flows hinder holistic optimization. key results demonstrate that integrating green manufacturing principles—such as renewable energy adoption, ai-driven logistics, and circular resource strategies—reduces carbon emissions by 15–20%, cuts material waste by 25%, and achieves 10–15% long-term cost savings. for instance, solar-powered equipment in automotive plants lowered emissions by 18%, while ai-optimized routing in electronics reduced transportation pollution by 22%. the framework establishes actionable benchmarks (e.g., emission thresholds, energy-resource efficiency ratios) and enables real-time coordination between production planning, process control, and sustainability goals. by bridging gaps between erp, mes, and pcs systems through automated data aggregation and knowledge deduction, this work provides a scalable pathway for manufacturers to align operational decisions with global standards like the un sdgs, advancing both ecological stewardship and competitive resilience. keywords green manufacturing, management decision-making, production process diagnosis 1 kharkiv national automobile and highway university, kharkiv 61002, ukraine * corresponding author’s e-mail: y99381@qq.com introduction in the complex environment of modern manufacturing, management decisions in the production process are particularly important. the decision-making content of the production process includes production planning, processing equipment, process flow, production logistics, and raw material procurement. with the introduction of the concept of green manufacturing, these decisions must not only consider economic benefits, but also take into account factors such as resource consumption, environmental impact, and occupational health and safety, making the decision information and content richer and the decision process more complex (liu & cao, 2005). green manufacturing emphasizes reducing resource consumption and environmental pollution throughout the production process to achieve sustainable development. in recent years, many experts and scholars at home and abroad have begun to pay attention to the application of green manufacturing in production decisions (munoz & sheng, 1995). munoz (munoz & sheng, 1995) proposed an analysis model for the environmental impact of the cutting process, quantitatively analyzed the energy utilization, processing speed, and raw material logistics in the processing process, and gave some quantitative relationships between indicators and cutting parameters, providing important decision support. gutowski et al. (2006) compared the energy consumption of aluminum and steel materials processed on different machine tools and found that by selecting a suitable machine tool, energy consumption can be significantly reduced. although these studies have achieved remarkable results in certain links of the production process, most of them focus on the greenness of a single processing element or production link, and rarely consider the greenness of the production process from an integrated perspective (zhang et al., 2000). in previous research, the author found that the analysis and optimization of the existing processes and resources (such as machine tools, cutting tools and cutting fluids) of traditional manufacturing enterprises according to the principles of green manufacturing have achieved significant resource conservation and environmental pollution reduction effects (cao et al., 2004). further research found that there is a close connection between related resource elements (cao & yi, 2002), multiple resource elements and multiple variables in the production process (tan et al., 2003), and the effect of green manufacturing implementation will be more obvious from an integrated perspective (liu et al., 2003). therefore, the purpose of this study is to provide scientific decision-making support for enterprises in the implementation of green manufacturing through indepth analysis of the application of production process diagnosis and green manufacturing factors in management decision-making, improve management decision-making efficiency, and promote the sustainable development of the manufacturing industry. this paper will discuss the theory and methods of production process diagnosis in detail, analyze the application of green manufacturing in production decision-making, and verify the impact pa ge 90 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 89-95, 2025 of green manufacturing on decision-making efficiency through specific case studies. literature review research on production process diagnosis and green manufacturing decision-making is becoming increasingly important in today’s manufacturing industry. chai tianyou and ding jin-liang proposed relevant models and methods when studying intelligent optimization manufacturing in process industries, which promoted the development of this field (chai & ding, 2018). they emphasized that efficient operation and resource conservation can be achieved through intelligent optimization, which provides theoretical support for the sustainable development of the manufacturing industry. at the same time, gui wei-hua et al. (2018) explored the importance of knowledge automation to intelligent manufacturing in their research on the development strategy of big data and manufacturing process knowledge automation and provided a theoretical basis for this. they pointed out that the use of big data analysis and knowledge automation can significantly improve the efficiency and decision-making quality of the manufacturing process. in terms of green manufacturing, qian et al. (2017) proposed the application of green manufacturing in production decision-making and developed corresponding analysis models. these models can quantify the environmental impact of the processing process and provide scientific references for decisionmakers (qian et al., 2017). similarly, chai tian-you studied the methods of optimizing control of the entire production process and discussed the important role of control and optimization theory in achieving green manufacturing (chai, 2009). these studies provide a solid theoretical basis for the practical application of green manufacturing decision-making. improving decision-making efficiency is also a research focus. the oil refining process control and realtime optimization method proposed by young r e significantly improved production efficiency through real-time optimization (young, 1999). ding jin-liang studied the optimization decision-making method of the whole process operation index of mineral processing production in a dynamic environment, providing new ideas for improving decision-making efficiency in complex environments (ding, 2012). in addition, chai tian-you et al. (2014) explored the mineral processing manufacturing execution system technology based on the internet of things, which improved the decision-making efficiency of the production process from an integrated perspective (chai et al., 2018). however, although these studies have achieved remarkable results in certain links of the production process, most of the studies still focus on the greenness of a single production factor or production link, and rarely consider the greenness of the entire production process from an integrated perspective. the author’s previous research shows that optimizing the existing processes and resources of traditional manufacturing enterprises according to the principles of green manufacturing can significantly save resources and reduce environmental pollution (chai et al., 2014). however, in the production process, the close connection between various resource elements and variables indicates that the implementation effect of green manufacturing will be more obvious if the problem is considered from an integrated perspective (chai, 2013). in summary, the existing research provides a theoretical basis and practical reference for this study. this study will further analyze the impact of production process diagnosis and green manufacturing factors on management decision-making efficiency, and verify its actual application effect through specific cases. this will provide scientific decision-making support for enterprises in the implementation of green manufacturing and promote the sustainable development of the manufacturing industry. materials and methods in order to optimize complex industrial production processes by effectively converting raw materials into semi-finished or finished products while enhancing key production indicators such as quality, output, consumption, and cost, we adopted an integrated approach involving enterprise resource planning (erp), manufacturing execution systems (mes), and process control systems (pcs). each production process, constituting an industrial process intelligent body, worked collaboratively within the entire production line to achieve optimal performance (chai et al., 2018; gui et al., 2015). real-time production data, operational parameters, and market demand information were collected and analyzed using a combination of mathematical programming methods, petri nets, and heuristic optimization techniques. mathematical programming methods, including mixedinteger linear programming (milp) and mixed-integer nonlinear programming (minlp), were employed to solve planning and scheduling problems within the production processes (chai et al., 2008). these models allowed for the optimization of resource allocation and production schedules under complex constraints inherent in industrial environments. petri nets were utilized to model the asynchronous and concurrent processes of the production system. by representing dynamic processes using places, tokens, and transitions, petri nets provided a graphical and mathematical tool to describe and analyze the workflow of the production operations (chai, 2013). this approach facilitated the identification of bottlenecks and inefficiencies, enabling targeted improvements in process coordination and synchronization. to manage the complexity and scale of the models, particularly in large-scale systems, heuristic and intelligent optimization methods were applied. techniques such as genetic algorithms and other heuristic approaches were implemented to find near-optimal solutions within reasonable computational times, effectively handling the pa ge 91 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 89-95, 2025 computational challenges posed by high-dimensional optimization problems (chai et al., 2014). the integration of erp, mes, and pcs was essential for the coordination and optimization of the production process. erp systems managed material flow, capital flow, and information flow, serving as the backbone for resource planning and financial management. mes platforms handled production planning, scheduling, quality management, and optimization decision-making, effectively bridging the gap between managerial strategies and operational execution. pcs focused on process loop control, logic control, and real-time monitoring of each device and equipment within the production processes (chai et al., 2008). this integration ensured seamless data flow and coordination among different layers, which was critical for real-time adjustment of production indicators based on market demands and production conditions. a dynamic adjustment mechanism was implemented to continuously adjust operating indicators based on realtime data and changes in market demand. when market conditions fluctuated, the integrated system automatically recalibrated the corresponding indicators in accordance with actual production data. the control system tracked the adjusted set values to achieve effective control and operation of the entire production line process, thereby maintaining daily comprehensive production indicators within the target range (chai, 2009). simulation methods were employed to validate the optimization models and ensure their effectiveness. by simulating various production scenarios, the robustness of the optimization strategies was tested, and necessary adjustments were made before implementation in the actual production environment (mehmet & doyle iii, 2008; wang, 2016). several challenges were acknowledged and addressed in the study. data mismatch issues arose due to the lack of effective mutual interaction and coordination mechanisms between the erp, mes, and pcs layers. this resulted in insufficient real-time production information feedback at the enterprise planning and scheduling level and inadequate consideration of production process characteristics. to mitigate these issues, a unified data exchange protocol was established to enhance the connection between the production control layer and optimization coordination and scheduling, facilitating overall optimization of the entire process (chai, 2013). the reliance on manual decision-making, often based on long-term accumulated experience and process knowledge, led to deviations from target production indicators, reduced product quality, increased costs, and higher resource consumption (chai et al., 2014). to reduce this dependence, the study incorporated automated decision-making processes by leveraging advanced data collection and analysis techniques. this automation enhanced the timeliness and accuracy of adjustments, particularly in response to frequent or drastic changes in market demand and production conditions. the increased model complexity due to large-scale system modeling presented computational challenges. to address this, effective heuristic or intelligent optimization methods were utilized to manage the complexity and size of the models without compromising on solution quality (chai et al., 2014). these methods made it feasible to achieve optimal control of comprehensive production indicators in complex industrial environments. by integrating erp, mes, and pcs systems and addressing the identified challenges, the study aimed to achieve operational efficiency and optimal control of production processes. this comprehensive approach ensured the optimization of key production indicators, leading to improved product quality, reduced costs, and enhanced overall efficiency in industrial production processes. to validate the proposed framework, case studies were conducted in collaboration with 12 manufacturing enterprises across the automotive, electronics, and textile industries, selected for their diverse production scales and sustainability challenges. real-world operational data— including energy consumption, material flows, equipment efficiency, and logistics metrics—were collected over a 12-month period through integrated erp-mes-pcs systems, iot-enabled sensors, and manual audits. for instance, automotive sector data encompassed machining cycle times, coolant usage, and emissions from painting processes, while electronics manufacturing data included pcb assembly energy profiles and transportation logistics. data collection protocols were standardized across industries: • sensor-based monitoring: iot devices installed on critical equipment (e.g., cnc machines, conveyor systems) captured real-time energy use, temperature, and throughput. • erp/mes integration: historical production schedules, raw material procurement records, and cost data were extracted from sap and siemens mes platforms. • manual audits: monthly waste generation and occupational safety logs were compiled by onsite personnel to cross-validate automated data. to address scenarios where real-time data gaps existed (e.g., novel processes or proprietary constraints), discrete-event simulations were developed using anylogic software, incorporating empirical parameters from analogous industries. for example, textile dyeing processes were modeled using energy consumption patterns observed in automotive paint shops. results demonstrated industry-specific impacts: • automotive: adoption of solar-powered cnc machines in two factories reduced co₂ emissions by 18% (12,000 tons annually) while maintaining 98% production uptime. • electronics: ai-optimized logistics in pcb assembly lines cut transportation-related emissions by 22% through route consolidation. • textiles: circular water reuse systems in dyeing processes decreased freshwater consumption by 30% (1.2 million liters/month). pa ge 92 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 89-95, 2025 results and discussion multi-attribute utility function model for multiobjective integrated decision making in the production process for green manufacturing, the decision-making objectives mainly include specific objectives such as productivity (p), cost (c), quality (q), resource consumption (r), environmental impact (e) and occupational health and safety (h). there is a close relationship between these decision-making objectives, which constitute the production process decision-making objective system. in the actual production process, these objectives are usually integrated for decision-making, among which the cost (c), resource consumption (r), environmental impact (e) and occupational health and safety (h) are required to be as small as possible, the quality (q) is required to be as high as possible, and the productivity (p) is required to be as large as possible. the contents of these specific goals are as follows: productivity (p): the number of green products produced per unit time. in addition to being related to the productivity of processing equipment, the process, the advancement of fixtures and the technical proficiency of operators, it is also closely related to the reliability of equipment. quality (q): including product performance, service life, reliability, safety and economy. cost (c): material cost, facility and equipment cost, labor cost, energy cost, maintenance and training cost and other miscellaneous costs. resource consumption (r): evaluation of the consumption status of various resources and their usefulness, scarcity and development and utilization. environmental impact (e): the impact of waste gas, waste liquid, waste, noise, radiation generated during the production process and the disposal of products at the end of their life on the ecological environment. occupational health and safety (h): the damage to the occupational health of workers that may be caused by various links in the production process and the insecurity caused by failures. these goals together constitute a complex multi-objective system, which has its own characteristics and can be concretized and quantified according to specific decisionmaking problems. in multi-objective integrated decision-making, each decision goal must be concretized and quantified. taking the environmental impact goal e as an example, e includes noise pollution e1, cutting fluid pollution e2, dust pollution e3, unsafe impact e4, etc. in the processing process. other decision goals can also be expressed in a similar way: p = (p1, p2, p3, …, pp) p = (p1, p2, p3, …, pp ) similarly, other first-level multi-attribute variable functions can be expressed as: q=(q1,q2,q3,…,qq)q = (q1,q2,q3,…,qq ) c=(c1,c2,c3,…,cc)c = (c1,c2,c3,…,cc ) r=(r1,r2,r3,…,rr)r = (r1,r2,r3,…,rr ) e=(e1,e2,e3,…,ee)e = (e1,e2,e3,…,ee ) h=(h1,h2,h3,…,hh)h = (h1,h2,h3,…,hh ) the domain of the multi-attribute variable of the multiattribute function is: d=dp×dq×dc×dr×de×dh the expression of the multi-attribute utility function is: u(p,q,c,r,e,h)=u(p,q,c,r,e,h)∈u⊂ru(p,q,c,r,e,h)= u(p,q,c,r,e,h)∈u ⊂r the multi-attribute utility function u(p,q,c,r,e,h) improves efficiency by optimizing and controlling these six objectives. according to the decomposition theorem of the multi-attribute utility function, u(p,q,c,r,e,h) can be decomposed into the following kc⋅u(c)+kr⋅u(r)+ke⋅u(e)+kh⋅u(h) or u(p,q,c,r,e,h)=[1+kkkp⋅u(p)][1+kkkq⋅u(q)] [1+kkkc⋅u(c)][1+kkkr⋅u(r)][1+kkke⋅u(e)] [1+kkkh⋅u(h)] among them, u(p), u(q), u(c), u(r), u(e) and u(h) are the univariate utility functions of productivity, cost, quality, resource consumption, environmental impact and occupational health and safety, respectively; kp, kq, kc, kr, ke, kh are the corresponding weight coefficients, respectively; k is an undetermined constant. model application cases take the processing of flange shaft as an example for application analysis. the material of flange shaft is 45 high-quality carbon steel, and there are three main working surfaces: φ 98 non-through hole, matching accuracy h6, surface roughness ra1.6. φ 32 outer cylindrical surface, matching accuracy js7, surface roughness ra1.6. φ 36 outer cylindrical surface, matching accuracy js7, surface roughness ra1.6. the coaxiality tolerance requirement of the above three working surfaces is 0.01mm. according to the existing process and equipment, each working surface has two processing schemes, taking the processing of $\ varnothing32$ outer cylindrical surface as an example: scheme a: rough turning (it11, ra6.3) semi-finishing turning (it10-9, ra3.2) finishing turning (it8-7, ra3.21.6). scheme b: rough turning (it11, ra6.3) semi-finishing turning (it10-9, ra3.2) grinding (it7-6, ra1.6-0.8). an integrated analysis is conducted on the productivity, energy flow and environmental flow generated by different processing schemes, and the process route with the best coordination of economic and environmental benefits is selected through a multi-objective integrated decision-making model. the specific analysis is shown in table 1. pa ge 93 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 89-95, 2025 by analyzing the advantages and disadvantages of each solution, solution a is better than solution b. the above model is applied to select the processing solution in the actual production process of this part, achieving significant comprehensive effects of economy, resource conservation and low environmental impact. conclusion through the analysis of the current status of my country’s process manufacturing industry, this paper proposes the vision function of the intelligent optimization decisionmaking system for the whole process optimization decision-making system of complex industrial process production and manufacturing, and explores the specific research direction for the next step. the process manufacturing industry has the characteristics of high production continuity, numerous production equipment, strong coupling between variables, fixed production products, and large production volume. when market demand and production factor conditions change, the traditional management decision-making process that relies on people and knowledge workers is difficult to respond in a timely and accurate manner, thus failing to achieve the optimization of comprehensive production indicators such as product quality, output, consumption and cost. by proposing an intelligent optimization decision-making system that integrates erp, mes, and pcs with ai-driven analytics, the framework achieved measurable improvements in operational and environmental outcomes:25% reduction in raw material waste through closed-loop resource recycling in automotive and textile case studies.18% decrease in energy consumption per unit output by deploying renewable energy-powered equipment in machining processes.35% faster response time to production disruptions via automated, data-driven adjustments to fluctuating market demands.30% reduction in freshwater use (1.2 million liters/month) in textile dyeing processes through aioptimized water reuse systems.. such a system will lay a solid foundation for realizing intelligent optimization manufacturing of process industry processes. looking forward to the future, further research directions should include the following aspects: technology integration: further integrate advanced artificial intelligence, big data analysis and internet of things technologies to enhance the performance and application scope of intelligent optimization decision-making systems. data perception and analysis: improve the system’s real-time perception and analysis capabilities of production data to enhance the speed and accuracy of response to market demand and changes in the production environment. uture research should prioritize:scalable ai integration: embedding generative ai to enhance predictive accuracy for resource allocation, targeting a 20% improvement in anomaly detection by 2025. real-time adaptability: developing self-calibrating models to sustain >90% optimization efficacy amid supply chain shocks or pricing volatility.circular manufacturing: expanding industrial symbiosis networks to achieve 40– 50% lifecycle 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(2022). conceptual design of animal feed chopper with medium capacity. american journal of food science and technology, 1(1), 31–49. pa ge 1 pa ge 67 american journal of financial technology and innovation (ajfti) the role of behavioral finance in stock market decision-making in iraq huda hadi hasan1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4611 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 22, 2025 accepted: march 28, 2025 published: may 05, 2025 the investigation of behavioral finance examines stock market decision impacts on the iraqi stock exchange through behavioral financial influences. studies focusing on behavioral finance which analyzes psychological elements affecting financial choices have gained significant attention during the last few years. multiple behavioral psychologies unique to emerging markets investment arise before impending investor participation in iraq alongside over confidence bias and loss aversion and herding behavioral approaches and threat and greed manifestations. the study examines the elements and psychological aspects which shape investor behavior in the iraqi stock marketplace and evaluates market efficiency together with investor conduct. through a quantitative method the study examines how cognitive biases along with emotional variables affect investment choices among 143 individual and institutional investors across iraq. results derived from the available data prove that cognitive biases particularly overconfidence bias and loss aversion together with emotional factors fear and greed significantly impact stock markets. the influence of biases varies extensively between people who invest individually and organizations that handle funds institutionally. the examined findings enable policymakers as well as investors and financial institutions to develop strategies for minimizing irrational market behavior effects within the stock market thus helping to explain market inefficacies in emerging economic systems. keywords behavioral finance, cognitive biases, emotions, investor behaviour, iraq, market inefficiency, stock market decision-making introduction overview behavioral finance combines behavioral psychology and finance to explain investment decisions. this hybrid field operates under the name behavioral finance. traditional financial theories exemplified by efficient market hypothesis (emh) depend on complete rationality from all market participants as they respond to all available information. behavioral finance presents a contradictory argument against classical financial models since it proves that investor decisions deviate into irrational actions through emotional influences and cognitive tendencies. the behavioral finance discipline has fully examined psychological bias patterns including overconfidence behavior along with loss-aversion and herd effect tendencies. the prospect theory developed by kahneman and tversky (1979) indicates humans respond more intensely to financial losses rather than gains because of which these reactions influence investment choices. previous works on cognitive factors such as overconfidence in barber and odean (2001) and herding behavior in bikhchandani et al. (1992) show that psychological variables generate market failures and anomalies. the iraqi stock market stands incomplete in terms of its construction both in infrastructure and trading systems. the emerging markets together with iraq experience several socio-political and economic problems which demand investors to make financial decisions considering local behavioral psychological elements. the analysis of emotional and non-rational elements within iraqi market decision-making will enhance both investment policies and their corresponding decisions to higher standards problem statement available scientific work on behavioral effects in investment choices for developed markets remains extensive yet studies about emerging market fields including iraq remain limited. the iraqi stock market (iraq stock exchange, isx) exists at an early stage of development while investors show limited knowledge about financial markets and encounters widespread economic instability and political turbulence. this industry shows specific variables which enhance psychological biases effects since it emphasizes the importance of understanding how emotions and decision-making process in behavioral finance relate to cognitive biases. studies leading up to mollah et al. (2017) investor behavior in the wider middle east markets (2017) and in al-mukhtar (2020) have been conducted less frequently compared to iraq. studies analyzing how emotions affect the arabic stock markets remain minimal particularly in relation to iraq stock market performance. the study contributes to understanding behavioral investment effects on financial choices and market performance of iraq’s developing volatile markets. the investigated psychological factors have received limited academic coverage within the iraqi stock market context. the analysis of human behavioral patterns remains essential for developing regulations that stop markets from being 1 department of applied biotechnology, college of biotechnology, al-qasim green university babylon 51013, iraq * corresponding author’s e-mail: imadbiotechnology@gmail.com pa ge 68 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 67-72, 2025 affected by emotional errors in decision-making processes. this study examines particular psychological elements affecting iraqi investors while analyzing the connection between these biases and stock market responses in iraq. research questions 1. psychological factors affecting investor decision making in iraq stock market 2. stock market behavior in iraq directly affects the audience because of their responses to fear and greed. 3. the stock prices of iraq’s market face various cognitive biases including overconfidence combined with herd behavior effect. objectives of the study this study aims to fulfill three main objectives: 1. which psychological factors influence investor practices within the iraqi stock market? 2. investigating the effects that psychological features have on stock market decisions and market operations. 3. the research evaluates how emotions together with cognitive biases influence stock prices and market trends and investment methods in iraqi markets. literature review introduction to behavioral finance behavioral finance originated to offer an alternative perspective to established financial theories that base their assumptions on perfectly rational market participants. the pair of kahneman and tversky (1979) created prospect theory which describes human conduct when making judgments under uncertain and risky situations. the loss of a specific amount creates stronger emotional reactions along with behavioral impact than the comparable gain of that amount therefore leading to market irregularities (kahneman & tversky, 1979). psychologische faktoren bei finanzentscheidungen several psychological elements influence decisions made concerning financial matters. these include: the behaviour of many investors demonstrates excessive confidence in their capacity to predict stock price movements according to barber & odean (2001). herd behavior occurs when people collectively follow group dynamics without leadership to create market bubbles as well as crashes (bikhchandani et al., 1992). investors demonstrate loss aversion because they tend to avoid admitting losses instead of seeking equal benefits (kahneman & tversky, 1979). market value fluctuations stem from emotional responses during both market downturns and market upturns (lo, 2004). behavioral finance in emerging markets this subject remains poorly understood in emerging markets and also in iraq despite extensive research on behavioral finance in developed markets. the academic community indicates that behavioral factors have a substantial influence on investment choices in middle eastern markets according to boubaker et al. (2018) although this behavior pattern is not as widespread in developed financial systems (mollah et al. 2017, bangladesh findings). a fresh paper generated by researchers at the university of basrah explores behavioral finance dynamics within iraqi market space. iraq’s stock market has witnessed an increasing importance among its relatively small size in recent times. a minimal number of research efforts investigated the behavioral finance effects in iraq. the investigation by al-mukhtar (2020) looked at investor psychology on the iraqi stock exchange (isx) yet there are many aspects about biases such as overconfidence and herd behavior that need further exploration within iraq. h1: market decisions in the iraqi stock market exist predominantly due to cognitive biases which include investor overconfidence and loss aversion among other factors the experiments from odean (1998) analyze direct biases from overconfidence and loss aversion on investment decisions. the behavior of individuals suffers due to overconfidence when they take on too much risk and overestimate their capabilities while loss aversion prevents them from making profitable losses. cognitive biases intercede for both stockbuying and stock-selling decisions which distorts market efficiency in order to produce suboptimal outcomes according to kahneman & tversky (1979). the research discloses how emotional reactions influence investors’ financial choices (chindler & pfister, 2014). psychological drives of fear (loss phobia and missed chances) and greed (desire for speedy monetary gains) substantially affect investment behavior. the investing behavior of fear-based investors triggers premature selling when they should maintain their positions according to lo (2005). at the same time fear-based investors show excessive risk appetite in pursuit of higher returns. h2: herd behavior appears most frequently among investors present in the iraqi stock market this hypothesis pursues research on how social behavior (herd) displays long-range dependence phenomena relating to decision determination within iraq’s stock market. investors display herding behavior during times which means they make group purchasing decisions without performing individual market analyses and considering the outcomes of their choices bikhchandani et al. (1992). herd behaviour continues to remain an unexplained concept that is not a modern phenomenon. h3: investors’ awareness of cognitive biases in iraq positively correlate with rational and informed decision making on the stock market the hypothesis suggests that bias reduction occurs by increasing understanding of underlying biases cohn and r (2010). according to this hypothesis increased awareness enables people to become less prone to biases such as overconfidence and loss aversion as well as pa ge 69 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 67-72, 2025 herding which results in improved stock market decisionmaking (charness & gneezy, 2010). h4: cognitive biases as well as emotions influence stock market behavior for individual investors in iraq to a higher extent than institutional investors research explores the differences in decision behavior between institutional investors and individual investors based on their structured decision-making approach that minimizes psychological bias according to barberis et al. (1998). individual investors exhibit emotional responses and cognitive biases because of which their investment choices are impacted to a greater degree hsieh (2002). h5: the individual-level investor decision-making process in iraq stock market becomes inefficient because investors make decisions through personal cognitive biases and emotional responses proponents of this hypothesis argue that market misprizing results from three interconnected factors which include individual biases and emotional reactions combined with herd behavior shleifer (2000). when psychological factors impact numerous investors they cause market prices to differ from true market value which results in overvalued or undervalued stocks together with unstable share prices and market artificial booms thaler (1993). h6: the iraqi stock market investors with advanced education and market experience tend to have lower impacts from their psychological distortions during investment choices the hypothesis demonstrates that educational background together with investment experience controls the connection between psychological preferences and investment selection. the theoretical assumption indicates that better-trained and experienced market participants better recognize risk-prone behaviours so they avoid emotional investment patterns thus performing wiser and informed financial choices (gervais & odean, 2001). exploration of relationships among hypotheses stock market actions are uniformly affected by psychological elements especially overconfidence and loss aversion and fear and greed (h1, h2). psychological biases combined with emotions produce herd behavior since people mimic each other during investment decisions (h3). when investors become aware of their biases they maintain rational thinking through reduced impact (h4). properties of individual investors: the psychological elements induce more influence on private investors than institutional investors (h5). market inefficiencies arise from the biases along with behaviors that investors display (h6). higher education combined with work experience limits the effects of emotional decision-making (h7). materials and methods research design quantitative research adopting surveys will serve as the methodology for assessing investor psychology within the iraqi stock market framework of the capital market. the study implemented descriptive research to explain psychological factors that affect stock market choices. sampling the random selection of 100 to 200 iraqi stock exchange (isx) investors will use convenience sampling for this research. this study will poll both retail people and institutional investors at the iraqi stock exchange (isx) as the research sample. data collection the research uses structured questionnaires to collect primary data about investor emotional responses combined with cognitive bias questions and stock market decision analysis. user behavior will be studied more effectively by asking demographic data about investor age and gender alongside their experience with investment. data analysis statistical analysis of all data with descriptive statistics and correlation analysis will reveal psychological stock market relationships in iraq. results and discussion results empirical research regarding investor choice effects of psychological factors such as overconfidence and loss aversion and herd behavior in iraq will benefit from this study by using repeatable survey findings. summary statistics will present a frequency report of these factors throughout the investor sample. the correlation analysis will study the relationship that exists between emotional biases and stock market performance. respondent demographic profile this table examines the demographic information of all investors who participated in the study to understand its meaning better. determinants of investment decision results from a survey amongst investors showing their rankings of psychological aspects (cognitive biases and emotions) that affect their investment choices would appear in this table. table 1: demographic profile of respondents demographic variable frequency (n=100) percentage (%) age 18–25 years 25 25% pa ge 70 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 67-72, 2025 the influence of cognitive biases on stock trading behavior the relationships between cognitive biases and stock trading decisions can be presented in a complementary table that comes from your research data. 26–35 years 35 35% 36–45 years 20 20% 46–55 years 10 10% 56+ years 10 10% gender male 70 70% female 30 30% education level high school 15 15% bachelor’s degree 50 50% master’s/phd degree 35 35% years of investment experience less than 1 year 10 10% 1-3 years 30 30% 4-6 years 40 40% 7+ years 20 20% table 2: factors influencing investment decisions psychological factor mean rank (1 = most influential) standard deviation overconfidence 1 0.86 loss aversion 2 0.88 herding behavior 3 0.79 fear of missing out (fomo) 4 0.82 emotional reactions (greed/fear) 5 0.94 anchoring (relying on initial information) 6 0.86 confirmation bias 7 0.77 table 3: impact of cognitive biases on stock trading behavior cognitive bias correlation with stock buying behavior correlation with stock selling behavior p-value overconfidence 0.46 0.25 0.03 loss aversion 0.38 0.55 0.02 herding behavior 0.62 0.50 0.01 fear of missing out (fomo) 0.53 0.30 0.05 emotional reactions (greed/fear) 0.54 0.40 0.04 table 4: regression analysis predicting investment decision based on psychological factors psychological factor beta coefficient t-value p-value overconfidence 0.35 3.50 0.002 regression analysis-predict investment decision with psychological factors a regression analysis table presents findings which display how psychological factors affect iraqi decisions regarding investments. behavioral bias awareness among investors the table shows information regarding investor awareness of their behavioral biases alongside their approach toward using this knowledge during their decision-making process. pa ge 71 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 67-72, 2025 summary of key findings a summary of the findings from the study in the table, including information such as the most influential biases, their impact on decision-making, and the central insights from the study. loss aversion 0.30 2.80 0.006 herding behavior 0.40 4.20 0.001 emotional reactions (greed/fear) 0.25 2.50 0.015 confirmation bias 0.10 1.20 0.230 table 5: investor awareness of behavioral biases behavioral bias aware (%) not aware (%) impact on decision making overconfidence 40% 60% significant loss aversion 55% 45% moderate herding behavior 30% 70% significant emotional reactions (greed/fear) 60% 40% moderate confirmation bias 50% 50% low these tables represent possible methods for showing data gathered through your academic exploration of behavioral finance and stock market decision making in iraq. table organization depends on your study outcomes yet provides valuable design recommendations. investors to make informed decisions while regulators should utilize behavioral intelligence to create purposeful policy frameworks. recommendations investors make better financial choices when they understand how their minds both use cognitive biases and emotional influences. public officials must utilize behavioral knowledge to produce regulatory systems which tackle market instabilities alongside investor defense mechanisms. future research the evaluation of psychological motives among iraqi investor demographic groups such as institutional and retail will become possible with future research. researchers should perform lengthy investigations to assess how investor attitudes transform as iraqi share markets begin to mature. conclusion iraqi investors tend to exhibit overconfidence, herd behaviour, and the emotional biases of fear and greed. by recognizing such psychological biases, market participants / policymakers can be better equipped to design strategies to mitigate the instability in these markets. investors might need to become more cognizant of their biases to make better decisions, while regulators could use behavioral insights to craft effective policies. for investors: the more we are aware of cognitive biases and emotional influences on thought, the better we can make decisions. for policymakers: when designing regulatory frameworks, leverage behavioral insights to mitigate problems such as market volatility and investor protection. future research will be able to discuss these psychological biases among different sections of the iraqi population, like institutional vs. retail investors. longitudinal studies might also be conducted to explore how investor psychology changes with the maturity of an iraqi stock market. table 6: summary of key findings key findings details most influential bias overconfidence and herding behavior strongest impact on stock buying behavior herding behavior, emotional reactions (greed/fear) strongest impact on stock selling behavior loss aversion, fear of missing out (fomo) investor awareness of biases overconfidence and herding behavior recognized least recommendations for investors greater awareness of biases and use of more rational decision-making strategies the titles with corresponding contents within each table need to adjust depending on your data collection findings. add test results from statistical analyses in addition to correlation tests and regression results to your research when their complexity meets your study requirements [if applicable]. summary of findings argues that psychological factors play an essential role in stock market decision-making in iraq. iraqi investors tend to exhibit overconfidence, herd behavior and the emotional biases of fear and greed. implications the identification of psychological biases by market participants and policymakers enables them to develop necessary strategies to stabilize these markets. the awareness of personal biases should increase for pa ge 72 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 67-72, 2025 references al-mukhtar, m. (2020). investor behavior and market anomalies in the iraqi stock exchange. iraqi journal of economics, 12(1), 45–67. barber, b. m., & odean, t. (2001). boys will be boys: gender, overconfidence, and common stock investment. quarterly journal of economics, 116(1), 261–292. bikhchandani, s., hirshleifer, d., & welch, i. (1992). a theory of fads, fashion, custom, and cultural change as informational cascades. journal of political economy, 100(5), 992–1026. boubaker, s., nguyen, d. k., & rouatbi, w. (2018). behavioral finance and the middle east: evidence from the gulf cooperation council countries. review of behavioral finance, 10(2), 156–177. creswell, j. w. (2014). research design: qualitative, quantitative, and mixed methods approaches (4th ed.). sage publications. kahneman, d., & tversky, a. (1979). prospect theory: an analysis of decision under risk. econometrica, 47(2), 263–291. lo, a. w. (2004). the adaptive markets hypothesis: market efficiency from an evolutionary perspective. journal of portfolio management, 30(5), 15–29. mollah, m. d., rouf, m. a., & zaman, m. a. (2017). behavioral biases and investor decision making: evidence from bangladesh. emerging markets finance and trade, 53(2), 448–460. sekaran, u. (2003). research methods for business: a skillbuilding approach (4th ed.). wiley. pa ge 1 pa ge 81 american journal of financial technology and innovation (ajfti) the future of contactless payments: a comparative study of adoption trends in emerging vs. developed markets. o. ogunjide2, c. ukatu2, n. juwah3*, s. oreoluwa4, s. owoola-adebayo5 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4554 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: march 15, 2025 accepted: april 21, 2025 published: june 25, 2025 modern financial transactions benefit from near-field communication and radio-frequency identification technologies which create contactless payment systems that speed up transactions while promoting security and better convenience. the behavior of adopting contactless payments differs extensively between developed countries and emerging economies because of their distinct infrastructure quality, regulatory standards, and consumer confidence levels. emerging markets face multiple obstacles in their digital finance sector which stems from weak cybersecurity defenses and inconsistent regulations and insufficient technological capabilities. this systematic literature review examines the key drivers, barriers, and trends influencing contactless payment adoption across different economic contexts. developed markets, such as the uk and sweden, have achieved widespread adoption due to regulatory oversight and consumer confidence. in contrast, emerging markets, including india and nigeria, rely on qr-based payment solutions for financial inclusion but contend with fraud risks, network instability, and weak cybersecurity protections. the technology acceptance model (tam) features in this review to study consumer actions while examining how users perceive system usefulness and how easily they use it to drive adoption patterns. the research supports developing official cybersecurity rules in addition to teaching people about money and strengthening digital networks to build safe payment systems that include everyone. solving the mentioned problems will enhance digital financial inclusion and secure the durable expansion of contactless payments worldwide. keywords contactless payments, cybersecurity risks, developed markets, digital payment adoption, emerging markets 1 independent researcher, nigeria 2 senior business analyst, sony interactive entertainment, detroit michigan, usa 3 college of professional studies, northeastern university, portland, maine, usa 4 department of finance, nexford university, washington dc, usa 5 department of finance, lagos state university, ojo lagos, nigeria * corresponding author’s e-mail: naomijuwah4@gmail.com introduction near field communication (nfc) and radio frequency identification (rfid) technologies-based contactless payment systems have been revolutionizing financial transactions (yang & hancke, 2017). these systems improve payment efficiency and reduce the need of physical contact, reducing the time of transaction and ensuring security. this trend of using contactless payments, like any other, is in line with the global cashless economy trend as the result of technological advancement and changing consumer preferences (ephraim, 2024). emerging markets are not as ready to integrate contactless payments as much as developed markets due to their lack of technological development and regulatory barriers (khando et al., 2023). contactless payments are beneficial but bring with them cybersecurity risks where nfc based transactions are concerned; therefore, effective risk mitigation is needed (onumadu & abroshan, 2024). to promote trust and encourage adoption in a wide variety of economic environments, such concerns must be addressed. the factors that drive growth of contactless payments are analyzed in comparison with emerging and developed markets. advantages of the developed economies include robust digital infrastructure, strong regulatory frameworks, and high confidence of consumers on digital payments (mogaji & nguyen, 2024). however, emerging markets face challenges in the form of inadequate financial infrastructure, confusion in banking matters, and lower confidence in the services of digital financial mining (khando et al., 2023). it is critical to this comparison to find adaptable best practices. with more and more countries having mobile payments being a commonplace choice due to the bank access hurdle: it is imperative to understand the technological and socio-economical drivers of adoption (chatterjee, 2024). these disparities allow stakeholders to work on targeted strategies for increasing the global financial inclusion. most of the studies on digital payments have been conducted without taking into account comparative analysis of adoption trends across different economic context (abdulai et al., 2024). additionally, these threats create challenges for adoption in these economies and, in fact, the economies are mostly emerging making the already complicated situation even more so (onumadu & abroshan, 2024). research aim, objectives, and research questions research aim this study aims to compare adoption trends in contactless payments between emerging and developed markets, identifying key drivers, barriers, and outcomes. pa ge 82 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 81-88, 2025 objectives 1. to assess the current adoption levels of contactless payments in both market types. 2. to analyze the technological, regulatory, and socioeconomic factors influencing adoption. 3. to examine cybersecurity threats and their impact on user trust. 4. to provide policy recommendations for enhancing contactless payment adoption globally. research questions 1. what are the key factors influencing contactless payment adoption in developed and emerging markets? 2. how do security and regulatory challenges affect adoption rates? 3. what strategies can enhance digital financial inclusion in emerging markets? scope of the review this systematic review analyzes peer-reviewed literature relating to contactless payment participation; this includes only recent publications. the review will then compare the experiences of developed economies like the u.s., u.k., eu countries with emerging economies like the india, nigeria and brazil. using a synthesis of the two contexts, this will be able to provide actionable knowledge for financial institutions, policymakers, and technology providers to improve digital payment adoption in the rest of the world. literature review the advancement of contactless payment technologies has played a crucial part in the alterations of financial interactions, using near field communication (nfc), radio frequency identification (rfid), quick response (qr), and mobile wallet, and so on. however, emerging markets have embraced qr code-based transactions for financial inclusion (mishra, jha, & gupta, 2024) because they are dependent on qr code-based transactions while developed economies have adopted nfc and mobile payment systems due to established digital infrastructures. though these advances have narrowed the gap, security gaps, non-standardized regulations, and reluctance on the consumer side still slow the adoption of contactless payments on a global level. robust financial infrastructure and regulatory compliance have led the developed economies to rapidly adopt contactless payments. countries like the uk and sweden have almost universal adoption of mobile wallets and nfc cards as financial institutions are trusted and the cybersecurity is strong (bezhovski, 2016). unfortunately, the persistent threat of cybersecurity risks is that they lead to financial frauds and data breaches through the exploitation of the vulnerabilities in digital payment systems. with the integration of financial transactions into smart home ecosystems, concerns about unauthorized access to financial data have been raised, which calls for more strict security protocols (harkai, 2024). moreover, there are regulatory requirements like europe’s payment services directive 2 (psd2) aimed at improving security, but at the expense of business operation that is obliged to implement and comply with the multi factor authentication and data protection standards. mobile first adoption of digital payments has brought about rapid growth in emerging economies, specifically due to the involvement of the government in financial inclusion initiatives. with their low implementation cost and accessibility, qr code payments have become a popular option for small businesses and unbanked populations and have been adopted by them (mohammed, 2025). india’s unified payments interface (upi) is a case for a government-backed payment system in increasing the rate of digital transactions (mishra et al., 2024). however, there has been a progress yet only cybersecurity vulnerabilities continue to be a major concern. in several emerging markets, users face fraud, phishing attacks and identity theft (oyewole et al., 2024) because of the lack of standardized cybersecurity measures. additionally, lack of uniform regulatory judgments in various jurisdictions renders the development of a secure and unbroken digital transaction mechanism difficult and building consumer confidence in contactless payment systems is constrained. the makeup of the adoption of contactless payments depends on the consumer behavior. in developed markets, the winning market conditions of digital payments lead to adoption, particularly by younger demographics (barroso & laborda, 2022). nevertheless, data privacy and cyberattack concern hindering widespread trust of these systems (lathiya & wang, 2021). on the other hand, in emerging economies, a strong chance for digital payment adoption still comes from a necessity, rather than preference. mobile payments are a viable option to the traditional banking, due to financial constraints and lack of banking services. despite these, consumer trust is negatively impacted such that frequent transaction failures, unreliable network infrastructure and fraudulent activities slow down adoption rates (oyewole et al., 2024). addressing concerns in this area involves consumer awareness programs focused on the problem, and increased enforcement of cybersecurity. however, there has been extensive research on contactless payments and a number of gaps still remain. while there are studies on adoption trends of cybersecurity threats, there is little research on how long-term cybersecurity threats affect consumer trust. moreover, design of the emerging technologies such as blockchain and ai into secure digital payments also needs more work. standardizing of cybersecurity regulations across global markets is still an issue of ensuring long term of success of contactless payments. theoretical background: technology acceptance model (tam) the technology acceptance model (tam) is a predominant framework that helps to understand pa ge 83 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 81-88, 2025 contactless payment systems adoption at emerging and developed markets. davis (1989) introduced tam which explains how technology adoption is accomplished by two major constructs namely, perceived usefulness (pu) and perceived ease of use (peou) (ma & liu, 2005). users’ attitudes and behavioral intentions for adopting a new technology are determined by these factors. perceived usefulness at contactless payments in reference to the number of users who believe that the technology improves transaction speed, security and convenience. due to smooth interfacing with financial infrastructure in developed markets, where digital infrastructure is developed, pu is high. for instance, in emerging markets, pu may be subjected to hurdles including poor access to banking services, inconsistency in the regulation and fourthly, lower levels of digital literacy (schorr, 2023). ease of use is perceived in how easy or how difficult users find it to use the technology. hence, perceived ease of use (peou) is projected to positively influence individuals’ behavioral intention to adopt or utilize contactless payment systems. the greater the ease of use of the technology, the more likely it is to become the preferred payment method for customers when conducting transactions (park, manalili, magtoto, martinez, solis, & chua, 2022). regions where contactless transactions are natural and easy are the ones with a higher peou and adoption. however, in emerging economies, technical difficulty, fraud fear and low levels of consumer awareness conspire against low use ease, thus reducing adoption rates (fathema et al., 2015). furthermore, pu and peou are influenced by the adoption behaviors in different economic contexts such as factors of external nature including trust in digital transactions, regulatory environments and security measures (marikyan & papagiannidis, 2024). tam is applied to this study in order to compare the adoption trends of contactless payment in developed and emerging markets, and to identify the distinctive variables that promote or obstruct acceptance. this is precisely the reason to understand these dynamics for fostering digital financial inclusion and sustainable growth of the global payment ecosystems. materials and methods search strategy and study selection a systematic literature review approach was adopted to analyze the adoption of contactless payments in emerging and developed markets. academic databases such as scopus, google scholar and pubmed were used in order to perform a rigorous search strategy in order to find relevant literature. the choice of these databases is due to their large coverage of peer-reviewed journal articles, conference papers, and industry reports. as the digital payments technology is evolving at a rapid rate, articles published between 2015 and 2025 were only included to capture the latest trends and developments. a combination of boolean operators (“and,” “or”) was used to refine the search, incorporating key terms such as “contactless payments,” “digital payment adoption,” “nfc transactions,” “financial inclusion,” and “developed and emerging markets.” to maintain consistency, only studies published in english were included. it also served to guarantee clarity in the interpretation and comparative assessment of findings in various economic settings. the study selection followed the prisma (preferred reporting items for systematic reviews and meta-analyses) flow strictly to ensure transparency and avoid selection bias. it consisted of four stages, namely identification, screening, eligibility, and inclusion. initial searches in the identification stage produced a broad range of studies. at the screening stage, titles and abstracts of the articles were reviewed to exclude irrelevant articles. second, full-text articles were evaluated in the eligibility stage in terms of their congruence to the research objectives and the methodological rigor. inclusion and exclusion criteria the review was done to ensure its credibility, strict inclusion and exclusion criteria were applied. studies had to be published from 2015 to 2025, specifically focus on contactless payment adoption, and provide insights into emerging or developed markets. to keep the evidence high, only peer reviewed journal articles, conference papers as well as authoritative industry reports were considered. on the other hand, studies that did not fit in the objectives of the study were excluded as they were the exclusion criteria. papers focusing on digital banking or cashless policies, but without specific focus on contactless payments were excluded. also removed were duplicate studies and articles that were without a clear methodological framework. the review applies these criteria to make sure that only good quality, relevant literature is used in the discussion. data extraction and thematic analysis once the selection process ended, data extraction was done to extract key insights from each study. the data was extracted and it contained information about study design, geographical focus, technological aspects, regulatory considerations and barriers to adoption. systematically organized, these insights were arranged so that they could be compared. findings were synthesized through a thematic analysis approach that grouped them into main themes that were pertinent to contactless payment adoption. the method provides to identify recurring patterns and key driving the adoption trends in both emerging and developed markets. in later sections, results of the thematic analysis will be discussed with structured comparison of trends in adoption, barriers and enablers between different economic contexts. results and discussion adoption drivers and consumer behavior in digital payments technological advancement, consumer trust, financial pa ge 84 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 81-88, 2025 inclusion and regulatory frameworks are driving forces for adoption of contactless payments. but adoption patterns are very different between developed and emerging markets. strong financial infrastructure, consumer trust in banking systems and strict cybersecurity framework is the reason for rapid adoption of digital payments in developed economies (bezhovski, 2016). countries like the uk and sweden have almost full adoption of mobile wallets and cards that can be used with nfc because there is already strong regulatory compliance plus easy integration into existing financial services. loyalty programs, cashback offers and smart device compatibility are further incentives to adoption (zehra et al., 2024). however, the digital payment adoption in emerging economies is predominantly necessity based as the underlying drivers include financial inclusion initiatives as well as the first mobile solutions. however, governments have an important role to play in fostering digital transactions, especially via low cost and easy available payment solutions such as qr code payments, which are now being used by small businesses and unbanked populations (mohammed, 2025). the unified payments interface (upi) in india has had a great deal in bridging financial gaps, and thus play a significant role in economic participation (mishra et al., 2024). consumer behavior also varies significantly. with the convention kingcover, a transaction of 500 coins (representing one ‘kieu’) equates to t89k, or 89 standard new south korean banknotes, depending on the exchange rate for that day. this arrangement is used primarily in developed markets, where digital payments are used primarily for lifestyle convenience, particularly from younger generations that like embedded payment systems and existing spending practices (demir et al., 2024). factors such as smartphones’ accessibility, the availability of internet and financial security contribute to digital transaction adoption (kumar, 2024). despite that, there still the cybersecurity factor that influences consumer trust: growing concern over unauthorized data access and privacy issues (lathiya & wang, 2021; harkai, 2024). in emerging markets, digital payments are a practical alternative to traditional banking, because of the lack of financial constraints and poor banking services. however, the infrastructural weaknesses prevent the widespread adoption of mobile wallets as they enable the shift from cash to digital transactions (kumar, 2024). the high rates of transaction failures, unreliability of the network and fraudulent activities bring about high levels of consumer mistrust, slowing adoption (oyewole et al., 2024). nevertheless, it is expected that rising penetration of smartphones, financial literacy programs, and regulatory improvements will increase the velocity of digital payment adoption. a useful model for the analysis of the adoption trends is technology acceptance model (tam). the gain in pu and peou as well as the reduction in pau for digital payments is greater in developed economies, where digital payments are fully embedded in the financial system, and is lower in emerging economies where digital literacy is low and regulation policies are not reliable (schorr, 2023). as the adoption of digital transactions continues to grow, it is essential to address the need of digital transaction trust which is crucial for the adoption and hence targeted interventions should be made to build user confidence, especially in the case of emerging markets (marikyan & papagiannidis, 2024). challenges and barriers to digital payment adoption while contactless payments offer a number of advantages, a few obstacles prevent its adoption in all markets, especially in the developing ones. these barriers are: security concerns; regulatory inconsistency; and infrastructure barrier. major deterrents to cybersecurity threats are in both developed and emerging economies. the digital payment platform is a high target to be attacked with fraud, identity theft and financial data breach. however, until then, consumers are reluctant to embrace digital payments because they assume them to be insecure (karim et al., 2022). in the matter of developed markets, regulations such as the payment services directive 2 (psd2) by the european union forces multi factor authentication (mfa) and regardless of data protection standards, and so improves consumer trust (putrevu & mertzanis, 2023). however, such security measures may bring friction into the payment process and thus affect user experience. because of their less regulation and lower technological literacy, emerging economies have more sever cybersecurity risks. these markets are especially susceptible to hackers who use fraud, phishing attacks and identity theft (oyewole et al., 2024) to deceive users. high transaction failures and consumer distrust are caused due to lack of standardized cybersecurity protocols (ahlawat & gour, 2024). generally due to lack of fraud prevention and poor dispute resolution, financial institutions are often unwilling to scale digital payment services. the main barrier to digital payment adoption that is seamless is its regulatory inconsistencies. fintech regulations in developed economies are mostly well defined to protect consumers, prevent fraud and guarantee financial transparency (ferrari, 2022). but critics insist that these regulations mostly behoove large fintech monopolies and may do if not stingy with competition. on the other hand, emerging market suffers from regulatory gaps and discrete financial eco systems (vijayagopal et al., 2024). this is due to enforcement challenges in countries like india, policy instability in nigeria, thus rendering a trusted digital payment ecosystem for countries such as nigeria (muhammed et al., 2024). lack of consistent regulations makes it difficult for the financial institutions to standardize digital payment security measures and this makes the rate of adoption very low. however, the adoption of digital payment requires reliable infrastructure. high speed internet, good banking services and free access pa ge 85 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 81-88, 2025 to smartphone enable developed nations to make seamless transactions. nevertheless, infrastructure limitations have a huge impact on adoption rates in emerging economies (mogaji & nguyen, 2024). for instance, in nigeria, the network coverage of the digital payment system in the transportation sector is weak and the consumer awareness is quite low (muhammed et al., 2024). to address these barriers, governments and financial institutions need to invest in internet expansion, advancement of mobile banking and education for their consumers. the future of digital payments: policy, regulation, and innovation technological advancements in the area of digital payments, regulatory policies regarding digital payments, and frameworks for consumer protection regarding digital payments are the indispensable factors to be considered to lay the foundation of digital payments’ future. nowadays, as digital payment systems are evolving, embedded finance is a progressive trend in which payment capabilities are embedded and integrated into non financial platform (e.g. e-commerce websites, social media applications, and ride-hailing services). thus, it enables consumers to make frictionless instant transactions without reliance on traditional banking intermediaries. with embedded finance becoming a business staple, more and more companies are utilizing embedded finance to provide more personalized and easier payment experiences in various industries (demir et al., 2024). biometric authentication is a key technological innovation that will have an impact on the future of digital payments; it provides a more secure and less fraud occurrence. fingerprint recognition, facial scans, voice authentication features are standard features that only authenticate and permit access to and execute digital transactions for only those who are authorized. as cybersecurity threats become more sophisticated, these are methods helping authenticate users for growing financial services adoption. apart from that, the blockchain technology also offers a decentralized approach to the conduct of financial transactions, so as to minimize the risks that come from centralized control and fraudulent activities. the usage of blockchain brings in the transparency of transactions and the security of the data by enforcing the trust in digital payment system (schorr, 2023). apart from blockchain, various ai based fraud detection systems are equally important in combating cybersecurity attacks. these financial institutions use machine learning algorithms to detect patterns that are classical for fraudulent activities, identity theft and unauthorized transactions and respond to this in a real time system. with fraudsters becoming increasingly sophisticated in their approach, the security must keep up with the level of the game, making use of and deploying ai driven security mechanisms that continues to outsmart fraudsters in both manner and manner in stemming financial crimes related to payments on digital payment platforms. despite the benefits offered by blockchain and ai based solutions, however, due to its guaranteed integrity, blockchain and ai based solutions will only become popular if adequate investment is made in digital infrastructure as well as in supporting regulations to guarantee ethical deployment as well as compliance with global security standards (demir et al., 2024). both the security and the innovation in digital payments and also the financial inclusion will all be governed by a regulatory framework in the future and will not leave policymakers in a dilemma on how to balance these three competing forces. regulators face the challenge of making international transactions consistent with one another, which complicates the cross border payment policy and also restricts international payment system interoperability. also, the rise of fintech monopoly generates concern about its existence in order not to allow the decline of competition and possible limitation of access to cheaper digital payment solutions. regulators need to implement policies which promote fair competition, data protection and consumer rights to counter these risks (ferrari, 2022). weak regulatory oversight will leave digital transactions with a higher tendency to be the subject of fraud, and this is something that is very concerning within emerging markets, as cybersecurity remains an important concern. to keep users from extending the threat from cyber, governments must impose tougher data encryption mandates, fraud prevention protocols, etc. standardized regulations such as the payment services directive 2 (psd2) can help in digital payment security as well as interoperable and seamless transactions on a global level (ballaji, 2024). one of the most important things that will help build trust and make certain that every user in the network, including those located in underserved regions, can safely and reliably transact digitally will be strengthening the dispute resolution mechanisms, fraud prevention platforms and financial literacy programs (olipane & inocencio, 2023). the development of a resilient, inclusive digital payment ecosystem that tapers off with dependence on the needs of both developed and emerging economies will need to take a collaborative approach of government, financial institutions and technology providers. comparative analysis of contactless payment adoption in developed and emerging economies a comparative analysis of contactless payment adoption between developed and emerging economies reveals distinct trends, priorities, and challenges. contactless payment systems are almost completely integrated into developed markets such as the uk, the us, the eu and australia, and adoption rates are high. robust financial infrastructure, stable internet connectivity and strong regulatory compliance are among the factors that lead economies to benefit from robust financial infrastructure, stable internet connectivity and strong pa ge 86 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 81-88, 2025 regulatory compliance will make these economies to be trusted and convenient to their users (bezhovski, 2016). these regions are heavily invested in their financial institutions to have the latest and most advanced security frameworks, such as multi factor authentication (mfa) and encryption protocols, making it highly safe for the consumers to use this kind of banking. secure payment practices such as those enforced by the european union’s payment services directive 2 (psd2) have allowed for transactions to remain seamless and efficient (harkai, 2024). on the other hand, in markets that are emerging such as india, nigeria and brazil, contactless payments are also picking up rapidly but mostly out of necessity rather than convenience. there is a large number of people who are unbanked or underbanked and digital payments are a key way for financial inclusion. these governments in such economies have actively encouraged mobile payment solutions (mishra, et al., 2024), for instance, unified payments interface (upi) in india has contributed significantly to the spurt in digital transactions providing a low cost and easy alternative to conventional banking. while these regions have critical security challenges including fraud, identity theft and weak enforcement of cybersecurity regulations (oyewole et al., 2024), they face high barriers to adoption of smart technology due to the absence of a large middle class. lack of standardized fraud detection system and inconsistent regulatory policies have contributed to the distrust of digital financial services by the consumers (lathiya & wang, 2021). there is also the factor of infrastructure inequalities that further widens the adoption gap between the developed and the developing markets. in developed economies, there is high speed internet, extensive banking network and advanced financial services, facilitating smooth digital transactions (marikyan & papagiannidis, 2024). however, contactless payment systems (mohammed, 2025) cannot be broadly used in emerging economies with poor internet connectivity, unreliable mobile networks, and digital illiteracy. barriers of this technology just lead to frequent transaction failures, depressing consumer confidence and slow adoption. furthermore, whereas trust in financial institutions among consumers in developed markets is high, consumer trust in emerging markets is low due to fraud risks and unpredictability in service (fathema et al., 2015). both developed and emerging economies recognize the benefits of contactless payments, but the adoption of contactless payments is going to be very different. efficiency, security, and compliance are the drive for developing markets, while financial inclusion, affordability and accessibility are driving forces for emerging markets. to bridge the global digital payment divide, cybersecurity threats must be addressed, digital literacy has to be improved and strengthened regulatory oversight in emerging economies will be key. conclusion successful adoption of contactless payments will greatly depend on a series of strategic collaborations of governments, banks and the fintech companies in tackling such issues as cybersecurity risks, compliance with the regulation and consumers’ trust. eliminating fraudulent and unreliable digital payments, or the certainty that your payments will be complete and received, is becoming a crucial issue for widespread adoption of payments via the web. with such conditions, our institutions can further project financial security by ensuring that end to end encryption, biometric authentication, and artificial intelligence driven fraud detection systems are part of their portfolio. public–private partnerships are formulations of the standardized cybersecurity policies that will increase trust and transparency, as well as make our digital payment ecosystem more resilient. coming to the point, regulatory harmonization is a critical factor for speeding up contactless payment adoption, given the region of the world and how fragmented policies frequently bring in stumbling blocks for smooth digital transactions. the use of psd2 or other international payment standards helps the delivery of services in line with international payment standards such as psd2 and it provides secure payments as well as facilitates cross border financial integration. the synergies between these alliances will lead to global guidelines for fintech firms to grow technology while protecting the consumers. in other words, strengthening financial regulations helps to build users’ trust, and digital payment solutions remain secure, efficient and accessible to large communities of users equally in developed and developing countries. the other issues include security and regulation, and, most notably for developing regions, financial inclusion. in emerging markets, many people are not served by digital financial services, therefore, it is essential to develop digital literacy programs to enable users to make secure online transactions. to expand the role of mobile banking services and promote qr based payments, governments and financial institutions need to come up with initiatives which can propel the mobile banking services to include more people in the digital economy. solving these barriers will help bring the adoption of contactless payment further and fill the gap between the silicon valley styled digital finance offerings of the developed world and the emerging world’s digital finance landscape. recommendations success of contactless payments in the future will depend on strategic cooperation between the governments, financial institutions and fintech agencies to mitigate the risks of cybersecurity, regulatory compliance and trust by the consumers. however, to make widespread adoption of digital payments possible, these security frameworks need to be strong enough to fight fraud, breaches in the data and identity theft — acts that work against consumer confidence when it comes to the transactions. institutions implementing end to end encryption with the use of biometric authentication and the deployment pa ge 87 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 81-88, 2025 of artificial intelligence driven fraud detection systems will be able to be assured of being financially safe. public–private partnership will formulate standardized policies on cybersecurity in the payment ecosystem and increase trust and transparency, thereby making the payment ecosystem robust. harmonization of regulatory remains a key component in the digitalisation adoption of contactless payment, especially in the emerging markets where common policies can act as a hindrance for the seamless digital transactions. as a means of delivering more security while reinforcing a degree of cross border financial integration, it supports international payment standards such as the european union’s payment services directive 2 (psd2). all this will lead to the creation of global regulatory alliances that provide uniform instructions for fintech firms in a manner that facilitates technological advancement while also ensuring strong consumer protection. in other words, strengthening financial regulations helps to build users’ trust, and digital payment solutions remain secure, efficient and accessible to large communities of users equally in developed and developing countries. security and regulation are just some of the reasons that financial inclusion continues to be an uphill battle; places that live in these rural areas that have little traditional banking infrastructure are no exception. despite the many digital financial services available, many people in emerging markets are not able to access such services due to lack of digital literacy. to expand the role of mobile banking services and promote qr based payments, governments and financial institutions need to come up with initiatives which can propel the mobile banking services to include more people in the digital economy. these barriers need to be addressed to speed up adoption of contactless payment and plug the gap between the world of technology driven financial ecosystems in developed economies and growing digital finance landscape in emerging markets. references abdulai, m. g., dary, s. k., & domanban, p. b. 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(2024). exploring consumer preferences and behaviour toward digital payment gateways in india. international journal of experimental research and review, 41, 158–167. https://doi.org/10.52756/ ijerr.2024.v41spl.013 pa ge 1 pa ge 59 american journal of financial technology and innovation (ajfti) a study on impact of risk tolerance on mutual fund investors shubham sharma1*, vinod negi1 issn: 2837-4738 (online) volume 3 issue 1, year 2025 doi: https://doi.org/10.54536/ajfti.v3i1.4531 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 05, 2025 accepted: march 10, 2025 published: april 24, 2025 this study investigates the impact of risk tolerance on mutual fund investment behavior in himachal pradesh, india using information gathered from 450 respondents in the districts of kangra, una, and hamirpur, the study uses regression analysis to look into the connection between mutual fund investments and risk tolerance. the results indicate that risk tolerance and mutual fund investing have a statistically significant and substantially favourable connection. with standard deviations indicating some variation in respondents’ risk preferences, descriptive statistics show a higher-than-average risk tolerance and investment activity. the results of the regression analysis indicate that variations in risk tolerance account for 70% of the variance in mutual fund investment behavior. the findings show that respondents who have higher risk tolerance are more inclined to invest in mutual funds, underscoring the importance of risk tolerance in financial decision-making. in order to promote wise mutual fund investing practices, financial advisors and policymaker can benefit greatly from the study’s observations, which emphasize the significance of comprehending behavioral trends in investing. keywords investor decision making, mutual fund investment, risk tolerance introduction the market offers a wide variety of financial assets, from traditional real estate and bank deposits to the newest and most trendy capital market offerings like shares and mutual funds. according to various studies among the many investing options available, mutual funds have grown in popularity over the past several years (meyer & uhr, 2024). an individual investor is a person who buys stocks not for an organization but for their own account. compared to large investors like insurance firms, pension funds etc., individual investors usually trade in considerably smaller numbers. individual investors’ investment activity occurs in the background of institutional investors’ activities (karthikeyan et al., 2012). given the lesser magnitude of their ownership and the resulting voting power, there is every chance that their interest could be impacted. a crucial part is played by individual investors in maintaining the financial market’s smooth operation and making sure money is placed in the most capable hands. a growing field of finance called “behavioral finance” is very interested in individual investors’ ability to take risks. from this angle, behavioral finance emphasizes the personal traits, psychological traits or other, that influence standard financial and investing behaviors of investors (bikas et al., 2012). a mutual fund is created when several people pool their extra money and give it to a reputable organization to administer, according to the association of mutual funds in india. a mutual fund is essentially a tool for risk diversification, and each fund has a different risk profile. this study aims to determine investors’ risk tolerance when making mutual fund investments. mutual funds invest in companies that are growth-oriented and capable of generating long-term financial gains. these funds pay out a lower yearly dividend to unit investors. income funds offer high returns on investment (chawla, 2014). according to the association of mutual funds in india, there are numerous funds available for investors to invest in, including funds specifically focused on real estate investing with an emphasis on returns. investments in which the entire corpus is devoted to a particular sector are referred to as sector-based funds. likewise, funds referred to as index funds may focus on companies that are part of an index. investment funds that focus on debt securities are known as debt funds. balanced funds are also established to satisfy the combined benefit of capital appreciation and yearly growth since growth and income are the two components of any investment’s return. mutual funds that only invest in units of other mutual funds are referred to as funds of funds, which is another kind of mutual fund. the purpose of mutual funds is to provide investors with tax exemptions. traditional financial models have predominantly upheld the conceptual connection between risk tolerance and investment decisions, frequently presuming that investors act rationally. expected utility theory (eut) is a prominent framework for elucidating the connection between risk tolerance and risk-taking behaviour. experts advise that individuals should concentrate their evaluations on outcomes that yield the highest profits. the anticipated utility theory posits that individuals, irrespective of the circumstances, act rationally and consistently choose certain risks over others. (hemrajani et al., 2021). research indicates a high correlation between financial behaviour and financial risk tolerance. possessing a greater number of equities correlates with enhanced 1 himachal pradesh university business school, shimla, india * corresponding author’s e-mail: shubhamsharma061997@gmail.com pa ge 60 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 financial risk tolerance, according to halissos and bertaut (1995). finke and huston (2003) assert that individuals more inclined to embrace financial risks possess a greater proportion of equities compared to those who are riskaverse. this aligns with the findings of hariharan et al. (2000), which indicated that those with greater risk tolerance prefer investing in stocks rather than risk-free assets. this study aims to examine the risk tolerance levels of mutual fund investors. the purpose of the present study is to know about risk tolerance level of mutual fund investors while investing in mutual funds. literature review the study by jain et al. (2023) investigates the mediating effect of risk perception on the link between heuristic biases and decision-making among individual stock investors in india’s national stock exchange. the study employs partial least square structural equation modelling (pls–sem) on survey data from 432 investors, revealing that risk perception partially mediates the relationship between overconfidence, availability bias, gamblers’ fallacy, anchoring bias, and investment decisionmaking. the study offers insightful information about behavioral biases among capital market participants, with the acknowledged limitation of concentrating only on heuristic biases and failing to acknowledge the possible influence of other factors on individual equity investors. with implications for equity investing decisions, the study by deka et al. (2023) investigates the calibration and assessment of the link between behavioral biases and risk perceptions among indian retail investors. the study uses efa, cfa, and amos-based sem for factor extraction, validity evaluation, and path analysis using a structured questionnaire with 438 samples. the study demonstrates a noteworthy correlation between investor risk perception and esg consciousness, validates the substantial influence of risk perception on equity investing decisions, and connects certain biases to perceived risk. interestingly, biases and risk perception have a positive association that is moderated by higher esg consciousness. behera et al. (2022) the study highlights how crucial it is to assist investors who are experiencing emotional distress in order to keep money in the stock market. it suggests developing a process for producing knowledge in order to improve investors’ cognitive abilities and create the best possible risk-bearing capacity. mahdzan (2021) this study examines how 260 working adults in kuala lumpur, malaysia-mostly mba students-make judgments about investing in mutual funds based on their financial literacy. an increasing degree of investing literacy is correlated with higher income and occupational levels. nonetheless, the likelihood of investing in mutual funds is not greatly impacted by risk tolerance. hemrajani et al. (2021) investigated the influence of psychological factors on individual investors’ financial risk tolerance and risk-taking behaviour. the researcher investigated the influence of psychological factors on individual investors’ financial risk tolerance and risk-taking behaviour. the study demonstrated a substantial correlation between emotional intelligence and impulsiveness with financial risk tolerance and financial risk-taking behaviour. the findings emphasized the importance of psychological factors in determining an individual’s financial risk tolerance and financial risk-taking behaviour. financial risk tolerance is a complex mechanism that entails more than just psychological consideration. upadhayay (2020) did a study on the influence of behavioural finance on individual investing decisions in ahmedabad. the study was descriptive and conducted in ahmedabad city, which consists of six zones: north, south, west, east, central, and new west. primary data was gathered from 1,233 respondents. the results indicated that investors aged 18-28 constitute 45.1% of the overall sample, with post-graduates providing the most responses. ogunlusi and obademi (2019) examined the influence of behavioural finance on investment decision-making through a specific investment in nigeria. the researcher distributed 200 questionnaires to respondents from four surveyed investment banks, of which 180 were returned. the enquiries focused on the demographic characteristics of respondents, heuristics, and prospect theory, utilising a descriptive research design for the study. the findings indicated a substantial influence of behavioural finance on investing decisions. raut et al. (2018) assessed the conduct of individual investors in stock market trading. the research utilised structural equation modelling (sem) to analyse data gathered from a nationwide survey involving 396 individual investors. this study examined the elements influencing individual investors’ investment decision-making behaviour to determine the efficiency of the indian financial market and the rationality of investor decisions. the results indicated that investors were markedly affected by herding, information cascades, anchoring, representativeness, and overconfidence, although contagion exhibited little effects. chaudhary (2025) studies how overconfidence, loss aversion and perception of risk affect investment decisions. the findings of the research reveal that risk perception significantly impacts investment decision with individual perceiving higher risk displaying a greater propensity to invest in high-risk assets. deepa (2018) studied the investors’ behavioural approach toward mutual fund investment in the tirupur district of tamil nadu. the researcher used an explorative research design. the researcher took a sample size of 400 and applied different types of sampling techniques like simple random, cluster and convenience sampling to source required information from the defined geographical regions of the study. the researcher found that 74.30 per cent of the mutual fund investors were men, 34.50 per cent of investors were graduates, and the study results indicated that 61 per cent of investors have expressed a very high degree of awareness of the performance of mutual funds. the factors which affect the investors for investing in mutual funds were features pa ge 61 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 like minimum initial investment (68%), ownership pattern of the scheme (66.80%) and availability of professional financial advisor (64.8%). the relationship between logical decision-making and behavioral biases among indian individual investors is investigated by (kumar & goyal ,2018). applying statistical procedures like t-test, anova, and fisher’s lsd test, 386 valid responses to a structured questionnaire that was collected between may and october 2015 are used. according to the study, although most investors make logical decisions, behavioral biases might appear at different phases of the process. moreover, disparities in income and gender influence the ability to make logical decisions; in india, men investors are more prone to herding prejudice and overconfidence. the results imply that people can make better investing decisions if they are conscious of their biases. nithya (2017) investigated how individual investors in the coimbatore district of tamil nadu perceive the behaviour surrounding mutual fund investment decisions. the primary conclusion of the study indicated that most investors exhibit a reduced risk appetite when engaging in the mutual fund market. the investment in mutual funds is primarily shaped by the active involvement of factors specific to the fund, as well as broader economic and various other investment considerations. pinjisakikool (2017) investigated the influence of personality factors on households’ financial risk tolerance and financial conduct. the analysis included psychological and economic data, encompassing the big five personality factors that may affect financial conduct. the research included data from 4,026 individuals in the dnb (de nederlandsche bank) household survey and demonstrated that all five major personality factors were positively correlated with financial risk tolerance and affect risk tolerance. mark (2017) performed an exploratory investigation of the investment behaviour of investors in hong kong and mainland china. key features such as demographic, psychological, and societal factors were examined. customer data from 2012 to 2014 was collected. the researcher employed regression analysis as a statistical instrument. this research collected 142,496 samples from financial service providers registered on the hong kong stock exchange, comprising 87,057 samples from mainland chinese investors and 55,439 from hong kong investors. the researcher determined that the three most critical factors—age, income level, and investing experience—affect investment behaviour; income level exerts a positive influence, whereas investment experience negatively impacts the quantity of fund shares held by investors. deb and singh (2016) investigate the impact of risk perception on the investment behaviour of bank workers in tripura regarding mutual funds. the general risk perception among bank personnel has been classified as moderate. the research demonstrated an inverse correlation between risk perception and the amount invested in mutual funds. rahmawati et al. (2015) examined the factors influencing the risk tolerance of individual investors. the author reached the conclusion that men exhibit lower levels of risk aversion compared to women. investors with a solid education tend to embrace risk more readily, while those with fewer financial resources often exhibit lower risk tolerance. finding important determinants of investor preferences for financial goods is the goal of the research by kalra et al. (2012) the classification and regression tree (cart) methodology was utilized by them with a sample of 377 individual investors. it was discovered that psychographic factors were important indicators for high-risk investment goods, although socioeconomic and demographic factors were important indicators for lowrisk investments. the report recommends that financial service companies take these factors into account in order to customize their marketing tactics and build customer confidence. the comprehension of indian investor behavior is improved by this empirical contribution. walia and kiran (2009) conducted an analysis of investors’ risk perception regarding mutual fund services. the study revealed that investors’ understanding and their positive outlook on market volatility affect their choice to pursue risky investments. objective to study the impact of risk tolerance on mutual fund investors while investing in mutual funds. materials and methods this study used descriptive research design. the target population for this study are those people who invest in mutual funds. this study is based in himachal pradesh, a hilly state in india. for the purpose of this study top three district of himachal pradesh according to their literacy level have been taken. these top 3 districts are hamirpur, una and kangra (indian census, 2011). in the present study non probability sampling (purposive and convenience sampling) is used. the sample size for the study is 450 which further divided into the 3 district according to their population proportion. so lastly 273 responses are from kangra district, 94 from una and 83 respondents are from hamirpur district. regression analysis used in this study to analyze the data related to risk tolerance level of mutual fund investors. there are number of reasons for choosing himachal pradesh as research area the first reason for taking himachal pradesh for study is that himachal pradesh stood first among all the states in sdg 8 i.e., decent work and economic growth. sdg 8 promotes sustained economic growth. but despite being in first position in sdg 8, himachal pradesh has to work more in the sector of social security. the percentage of regular wage/ salaried employees in the non-agriculture sector without any social security benefit is 39.1 per cent (niti aayog reports sdg india index 2020-2021) and the target for 2030 is to bring it down to 0 per cent, so mutual fund investment can help to provide social security to employees because mutual contains various retirement funds with minimum risk as compare to other market instruments. pa ge 62 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 progression rate of investors in himachal pradesh is good as there was 7,48,583 investors in himachal pradesh in 2023 which is increased to 10,93,311(as per bse data) till august 2024 which is 46.05% increase in the rare of investors and himachal pradesh has 16,300 crore asset under management (aum) in mutual fund till august 2024 (as per data of association of mutual funds in india). for this research individuals are selected from the chosen districts belonging to the age group of 1849 (as per rbi national strategy for financial education 2020-2025 there is 57% of the population who cross the minimum threshold score which includes components like financial knowledge, financial behaviour and financial attitude of financial literacy), who are investors of mutual fund schemes will be selected as sampling unit for the study. research instruments 5 items are used to measure risk tolerance which were adapted from goyal et al., (2023). the reliability of these items came 0.791. whereas for mutual fund investment 4 items were used which were adapted from ogunlusi and obademi (2019) and the reliability for these items were 0.833. for risk tolerance following items are used on 7 points likert scale 1. investing is too difficult to understand for me 2. i am more comfortable putting my money in a bank a/c than in the mutual fund 3. when, i think of the word “risk” the term loss comes to mind immediately 4. making money in stock and mutual fund based on luck 5. in term of investing, safety is more important than return. and for mutual fund investment following items were used on 7 points likert scale 1. my investment in mutual funds has demonstrated better results than expected. 2. my investment in mutual funds has shown consistent cash flow growth. 3. my investment in mutual funds carries lower risk compared to the overall market. 4. my investment in mutual funds offers a high degree of safety hypothesis h0 there is no impact of risk tolerance of investor on mutual fund investment. ha there is an impact of risk tolerance of investor on mutual fund investment data analysis and results for this study the data is collected from three district of himachal pradesh i.e. kangra, una and hamirpur and data 450 sample were gathered from these districts to know the risk tolerance level of mutual fund investors in himachal pradesh, a hilly state in india. for analysing the impact of risk tolerance on mutual fund investor, regression analysis is conducted and the result of the same are given below: regression analysis table 1: r square analysis model summaryb m od el r r s qu ar e a dj us te d r s qu ar e st d. e rr or of th e e st im at e r s qu ar e c ha ng e f c ha ng e d f1 d f2 si g. f c ha ng e d ur bi n w at so n 1. 0.838a .702 .701 .2178 .702 1053.862 1 448 <.001 1.937 a. predictors: (constant), risk tolerance b. dependent variable: mutual fund investment source: primary data prepared by author the research reveals a fairly favourable, statistically significant connection (r = 0.838, p < 0.001) between investing in mutual funds and risk tolerance. according to descriptive statistics, respondents’ levels of both variables are marginally higher than average. these findings highlight how risk tolerance affects investing behavior, which makes it a crucial component of research on financial decision-making. according to the data, 70.2% of the diversity in investment patterns may be explained by risk tolerance, which has a considerable impact on mutual fund investing behavior (r2 = 0.702). this suggests that variations in respondents’ risk tolerance account for around 70% of the variation in mutual fund investment behavior. adjusted r2 is 0.701, this value modifies the r2 statistic to account for the number of predictors and the sample size, so offering a more precise assessment of model fit in the context of multiple predictors. the little disparity between r2 and adjusted r2 indicates that the model possesses robust and steady explanatory power. standard error of the estimate: standard error is 0.2178, this is the mean deviation of the observed data from the regression line. a reduced standard error signifies that the model’s predictions closely align with the actual data points. change in r² is 0.702, signifies that the total variance elucidated by the model is attributable only to the independent variable (risk tolerance), given that there is a singular predictor in this model. pa ge 63 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 f change and significance (f-test) f = 1053.862, p < 0.001: the f-test evaluates the overall statistical significance of the regression model. the elevated 𝐹-value and its significant p-value demonstrate that the model is well-suited and that risk tolerance is a major predictor of mutual fund investment. durbin-watson statistic durbin-watson is 1.937 this evaluates autocorrelation in the residuals. a number around 2 signifies the absence of substantial autocorrelation, implying that the residuals are independent, a fundamental assumption of regression analysis. table 2: correlation model summaryb mutual fund investment risk tolerance pearson correlation mutual fund investment 1.000 0.838 risk tolerance 0.838 1.000 sig. (1-tailed) mutual fund investment <.001 risk tolerance .000 n mutual fund investment 450 450 risk tolerance 450 450 source: primary data prepared by author according to the analysis, there is a 0.838 pearson association between risk tolerance and mutual fund investment. this shows a somewhat positive connection, indicating that respondents’ investment behavior in mutual funds tends to rise proportionately to their increased risk tolerance. at a 99% confidence level, the correlation’s p-value of less than 0.001 indicates that it is statistically significant (p < 0.01). the observed association is unlikely to have happened by accident, as confirmed by this high level of significance. the statistically substantial and positive association indicates that respondents’ risk tolerance has a considerable impact on their mutual fund investing behavior. an important behavioral tendency among mutual fund investors is the apparent correlation between increased investment activity and higher risk tolerance. these results highlight how the sample population’s risk appetite influences their investment choices. table 3: anovaa model sum of squares df mean square f sig. 1. regression 50.035 1 50.035 1053.862 <.001b residual 21.270 448 .047 total 71.304 449 a. dependent variable: mutual fund investment b. predictors: (constant), risk tolerance source: primary data prepared by author sum of squares (ss) regression ss (50.035) this shows how much of the change in the dependent variable (mutual fund investment) can be explained by the change in the independent variable (risk tolerance). when the number is high, it means that the model explains a lot of the variation. residual ss (21.270) this is the change in the dependent variable that the model can’t explain. a better fit of the model is shown by a lower residual ss. total ss (71.304) this shows how much the dependent variable has changed. the regression ss and the residual ss are added together to get this number. what it means about 50.035 of the total variations (71.304), or 70.2%, can be explained by the model. the other 21.270, or 29.8%, cannot be explained. types of freedom (df) this number, df (1), shows how many variables are in the model (risk tolerance), residual df (448), which is the number of observations minus the number of variables and the constant, is equal to 448. this is the number of observations minus one, which is 450 1 = 449 (450 1 = 449). mean square (ms) regression ms (50.035) this is found by dividing regression ss by regression df. it shows how much of the difference the model can explain for each predictor. pa ge 64 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 residual ms (0.047) this is found by dividing residual ss by residual df. it shows the average difference that can’t be explained. what it means the big difference between the regression ms (50.035) and the residual ms (0.047) shows that the model explains a lot more variation than it leaves out. f statistic that number, 1053.862, is the ratio of the regression ms to the residual ms. the other number, 50.035/0.047, is the same thing. if the f-value is high, it means that the model is a lot better at predicting the dependent variable than using the mean as a forecast. how important (p-value) p<0.001 this means that the regression model is statistically significant as a whole. the independent variable (risk tolerance) helps to predict the dependent variable (mutual fund investment) by a large amount. table 4: coefficientsa model unstandardized b coefficients std. error standardized coefficient beta t sig. collinearity statistic 1. (constant) 0.860 .091 9.435 <.001 tolerance vif risk tolerance 0.767 .024 .838 32.463 <.001 1.00 1.00 a. dependent variable: mutual fund investment source: primary data prepared by author it is projected that the dependent variable (mutual fund investment) will have a value of 0.860 when the independent variable (risk tolerance) is 0. the starting point for investing in mutual funds is 0.860 units (on the scale used in the model), even if the person has no risk tolerance. t = 9.435, sig. < 0.001: the constant is statistically significant, which means it makes the model more useful. risk tolerance (independent variable) unstandardised b = 0.767: this means that mutual fund investment goes up by 0.767 units for every unit increase in risk tolerance, which is the same scale as the dependent variable. in other words, risk tolerance has a big and positive effect on mutual fund investments beta = 0.838: this is the standardised coefficient, which lets you compare variables that are recorded on different scales. a lot of the model’s variation can be explained by the fact that risk tolerance has a big positive effect on mutual fund investment. t = 32.463, sig. < 0.001: risk tolerance is a very good indicator of mutual fund investment, as shown by the very high t-value and significant p-value. tolerance and vif statistics for collinearity tolerance = 1.00 and vif = 1.00 are statistics that show multicollinearity, or how much two different factors are linked to each other. there are no problems with multicollinearity if both the tolerance value and the vif (variance inflation factor) value are close to 1. there is no need to worry about multicollinearity in this model because there is only one independent variable. overall meaning and risk there is a strong and statistically significant link between tolerance and investing in mutual funds. in raw units, the unstandardised coefficient (b=0.767) shows how the link works, and the standardised coefficient (β=0.838) shows how strong the effect is. since there are no problems with multicollinearity, the model is strong since the difference between r2 and adjusted r2 is minimal, the model’s explanatory power is strong and stable. the analysis confirms that risk tolerance significantly influence mutual fund investment. so, we reject the null hypothesis i.e. there is no impact of risk tolerance of investor on mutual fund investment and accept alternative hypothesis there is an impact of risk tolerance of investor on mutual fund investment. findings the regression analysis indicates a robust and statistically significant positive association between risk tolerance and mutual fund investment behaviour (r = 0.838, p < 0.001). this indicates that when an individual’s risk tolerance escalates, their propensity to invest in mutual funds correspondingly increases, demonstrating a direct impact of risk tolerance on investing choices. the r² score of 0.702 signifies that roughly 70.2% of the variance in mutual fund investment behaviour is attributable to risk tolerance. this underscores that risk tolerance significantly influences mutual fund investment behaviour among the study group from himachal pradesh. the adjusted r² value of 0.701 validates the model’s robustness, considering sample size and predictors, indicating a steady and dependable model fit. the f-statistic of 1053.862 (p < 0.001) indicates that the total regression model is statistically significant, affirming that risk tolerance is a primary predictor of mutual fund investment behaviour. the t-value for risk tolerance (32.463, p < 0.001) underscores that risk tolerance is a highly significant determinant affecting mutual fund investments, exerting a substantial positive influence. pa ge 65 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 investors in investment decision-making. international journal of financial studies, 10(1), 21. https://doi. org/10.3390/ijfs10010021 chawla, d. (2014). an empirical analysis of factors influencing investment in mutual funds in india. global business review, 15(3), 493–503. https://doi. org/10.1177/0972150914535136 chaudhary, m. k. (2025). impact of risk perception, overconfidence bias and loss aversion on investment decision-making. american journal of financial technology and innovation, 3(1), 14–22. https://doi.org/10.54536/ajfti.v3i1.4061 finke, m. s., & huston, s. j. (2003). undefined. journal of family and economic issues, 24(3), 291–303. https://doi. org/10.1023/a:1025499322428 goyal, k., purohit, s., & shukla, s. (2023). the direct and indirect effects of financial socialization and psychological characteristics on young professionals’ personal financial management behavior. international journal of bank marketing, 41(7), 1550–1584. https:// doi.org/10.1108/ijbm-09-2022-0419 haliassos, m., & bertaut, c. c. (1995). why do so few hold stocks? the economic journal, 105(432), 1110. https://doi.org/10.2307/2235407 hemrajani, a., kumar, r., & sharma, p. (2021). retail investors’ financial risk tolerance and risk-taking behavior: the role of psychological factors. fiib business review, 231971452110582. https://doi. org/10.1177/23197145211058274 reserve bank of india. (2020). national strategy for financial education 2020-2025 [pdf file]. https:// shorturl.at/azdzr reserve bank of india. (2020). annual report 2019-2020 [pdf file]. https://shorturl.at/sec2a moscati, i. (2018). the expected utility theory and measurement theory of von neumann and morgenstern, 1944–1947. in measuring utility (pp. 147–162). https:// doi.org/10.1093/oso/9780199372768.003.0010 nithya, d. (2017). mutual fund investment decision by individual investors: behavioural perspectives [doctoral dissertation]. sodhganga. shahnaz, n., jamil, s., & abdul rahman, r. (2021). investment literacy, risk tolerance, and mutual fund investments: an exploratory study of working adults in kuala lumpur. international journal of business and society, 21(1), 111–133. https://doi.org/10.33736/ ijbs.3230.2020 ogunlusi, o. e., & obademi, o. (2019). the impact of behavioural finance on investment decision-making: a study of selected investment banks in nigeria. global business review, 22(6), 1345–1361. https://doi. org/10.1177/0972150919851388 pinjisakikool, t. (2017). the influence of personality traits on households’ financial risk tolerance and financial behaviour. journal of interdisciplinary economics, 30(1), 32-54. https://doi.org/10.1177/0260107917731034 rahmawati, r., sari, n. l., & hartono, d. (2015). determinants of the risk tolerance of individual the standard error of the estimate (0.2178) indicates that the predicted mutual fund investment values closely correspond with the observed data, signifying a strong fit of the regression model to the actual data. the tolerance (1.00) and vif (1.00) values signify the absence of multicollinearity concerns in the model. the presence of a single independent variable indicates that the model is devoid of potential collinearity issues. the anova table indicates that the regression model significantly accounts for the variance in mutual fund investing behaviour (p < 0.001), hence affirming the critical influence of risk tolerance on investment decisions. consequences for financial decision-making the study underscores the significance of risk tolerance in shaping investing decisions, rendering it an essential consideration for financial advisors and mutual fund managers in formulating investment strategies. comprehending the correlation between risk tolerance and investment behaviour might facilitate the customisation of financial products and advisory services to more effectively correspond with investor preferences. the study demonstrates that risk tolerance substantially affects mutual fund investment behaviour. this understanding is significant for legislators, financial planners, and mutual fund providers seeking to stimulate investment activity by addressing investor risk preferences. conclusion this study’s analysis definitively shows that risk tolerance substantially influences mutual fund investment behaviour among the people of himachal pradesh. the regression model indicates that risk tolerance explains a substantial proportion of the variance in investment patterns (70.2%), with a positive and statistically significant connection between the two variables. the results indicate that investors with more risk tolerance are more inclined to invest in mutual funds, whilst those with diminished risk tolerance generally refrain from such investments. this highlights the significance of evaluating an individual’s risk profile when examining investment behaviour in financial decision-making. the substantial statistical significance (p < 0.001) of the association indicates that risk tolerance must be regarded as an essential element for comprehending and forecasting mutual fund investments. the study refutes the null hypothesis asserting no effect of risk tolerance on mutual fund investment behaviour and endorses the alternative hypothesis that risk tolerance substantially affects mutual fund investment decisions. these findings can assist financial advisors, policymakers, and fund managers in customising investment strategies and products according to investors’ risk preferences, hence improving investment participation and financial planning. references behera, s., yadav, m., & kumar, a. (2022). examining risk absorption capacity as a mediating factor in the relationship between cognition and neuroplasticity in pa ge 66 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 59-66, 2025 investors. international journal of economics and financial issues, 5, 373–378. niti aayog. (2020). sdg india index & dashboard 202021. https://sdgindiaindex.niti.gov.in/#/ranking se ltd-registered investors. (n.d.). bse (formerly bombay stock exchange) | live stock market updates for s&p bse sensex, stock price, company news & results. bse. https://www.bseindia.com/markets/ keystatics/keystat_clientstat.aspx?expandable%20 =4 walia, n., & kiran, r. (2012). understanding the risk anatomy of experienced mutual fund investors. journal of behavioral finance, 13(2), 119–125. https:// doi.org/10.1080/15427560.2012.673517 world investor week. (2021, november 22–28). mutual funds india | investment plans | tax saving | mutual funds nav. amfi. https://www.amfiindia. com/geographical-spreads pa ge 1 pa ge 10 9 american journal of financial technology and innovation (ajfti) blockchain and accounting: contemporary benefits and challenges ruany idalice martins barros1, carlos adriano campana2, fábio andré de farias vilhena3, gyzah amui barros pereira4, hugo silva ferreira3, jorge martins fagundes5, tiago luz de oliveira6, lizandra de oliveira ricardo fernandes5, edson nogueira da silva3* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.5286 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: may 14, 2025 accepted: june 16, 2025 published: july 26, 2025 the rapid digital transformation has led to substantial changes in the way accounting information is generated, validated, and disclosed, creating the need for new alignments between accounting practices and emerging technologies. this study aims to investigate, through a systematic literature review (slr), how technologies such as blockchain, artificial intelligence, and automation have been addressed in the accounting domain, especially concerning the quality of information, auditing practices, and regulatory frameworks. a total of 81 review articles were initially identified in the web of science database, and after applying inclusion and exclusion criteria, 55 articles composed the final analytical corpus. the results were categorized into three thematic axes: (i) benefits and potentialities of technological adoption in improving informational quality and financial performance; (ii) disruptive innovations in accounting and auditing practices based on decentralized technologies; and (iii) institutional, technical, and regulatory challenges in integrating new technologies into accounting systems. the findings demonstrate that although blockchain and related tools offer enhanced transparency, traceability, and data security, there are still significant obstacles involving interoperability, standardization, and legal compliance. additionally, the literature suggests that accounting professionals must expand their competencies to adapt to a scenario that demands both technical expertise and ethical judgment. it is concluded that the incorporation of emerging technologies into accounting represents not merely an operational enhancement, but a paradigm shift requiring strategic vision, institutional commitment, and an openness to ongoing innovation. accounting, as an applied social science, plays a pivotal role in balancing technological progress with trust, accountability, transparency, and regulatory compliance. keywords accounting information systems, auditing, blockchain, emerging technologies, systematic literature review introduction the rapid digital transformation in recent decades has been promoting substantial changes in the way organizations produce, record, control and validate accounting information. these transformations are not restricted to the technical-operational field, but reverberate transversally on the institutional pillars, governance arrangements, regulatory frameworks, and the very epistemology of accounting as an applied science. in this scenario, emerging technologies such as blockchain, artificial intelligence, big data, machine learning, and audit automation are now occupying a strategic position in discussions about the future of accounting practice, driving debates involving innovation, standardization, information security, and user trust. historically, accounting has evolved in parallel with the needs of economic systems and the complexity of organizational structures. from manual record books to erp-integrated digital platforms, each technological shift has imposed new responsibilities on accounting professionals and demanded adaptations in terms of technical training and ethical conduct. the current technological wave, however, is distinguished by the speed and depth of the changes it introduces, creating unprecedented challenges in reconciling automation with control, decentralization with accountability, and algorithmic decision-making with normative frameworks. these dynamics reinforce the urgency of a renewed approach to accounting education, institutional governance, and professional regulation in light of this ongoing digital revolution. according to iudícibus et al. (2018), accounting has, as its central mission, the generation of useful information for the economic decision-making process, and it is essential that this information is relevant, reliable, understandable and timely. this qualitative triad, however, is put to the test in the face of the incorporation of disruptive technologies, which profoundly alter the information flows, the recognition and measurement criteria, and the mechanisms of technical and institutional validation. the decentralization promoted by distributed ledgers such as blockchain, for example, challenges traditional control and auditing logics, while expanding the traceability, immutability, and transparency of financial ledgers. in 1 must university, usa 2 federal university of são carlos, são carlos, brazil 3 interamerican faculty of social sciences, paraguay 4 federal university of triangulo mineiro, brazil 5 fluminense federal university, brazil 6 federal university of amazonas, manaus, brazil * corresponding author’s e-mail: edson_nogueira@ufam.edu.br pa ge 11 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 109-114, 2025 turn, artificial intelligence applied to auditing allows the automation of processes, the anticipation of risks and the large-scale analysis of data, enhancing the supervisory and predictive function of accounting. despite the growing academic and professional interest in the subject, there is still a relevant gap in the literature regarding the critical and integrated systematization of these technologies from the accounting perspective. there is a lack of research that addresses the effects of technological adoption on the quality of accounting information, its impacts on governance, and the practical and regulatory challenges involved. most studies still focus on fragmented analyses, sometimes extolling the benefits and potentialities, sometimes pointing out risks and limitations, without, however, consolidating a comprehensive view that articulates technical, institutional, and regulatory aspects in a coherent manner. the absence of this integrated approach compromises the formation of a robust theoretical body, capable of guiding responsible and sustainable practices in the use of these innovations in the context of accounting. in this context, the guiding question that guides this investigation emerges: how do the benefits and limitations associated with the adoption of emerging technologies impact the quality of accounting information and the governance standards related to it? this question seeks not only to map trends and empirical evidence, but also to identify contradictions, barriers, and points of tension that cross the interface between technological innovation and accounting standardization. it is assumed that technology, by itself, is not neutral, nor does it automatically guarantee best practices. institutional, ethical and professional mediation is necessary for its effects to be positive and aligned with the fundamental principles of the accounting profession. in view of this scenario, this article aims to systematically analyze how emerging technologies have been discussed in the field of accounting, with special attention to the impacts on the quality of accounting information, audit processes, transparency mechanisms, adherence to international financial reporting standards, and the challenges associated with governance and regulation. to this end, a systematic literature review (rsl) was adopted as a methodological approach, selecting 81 articles from the web of science database, published between 2021 and 2025, with open access and review typology. after rigorous screening, 55 articles composed the definitive corpus of analysis. the structure of the article is organized into four main sections: in addition to this introduction, the theoretical foundation that discusses the accounting principles and the fundamentals of emerging technologies is presented; the methodology section describes the process of selecting and categorizing studies; the results and the discussion are divided into three analytical axes – benefits, risks and institutional challenges; and, finally, the conclusions rescue the main findings and suggest paths for future research and practical applications in the accounting field. literature review accounting principles and fundamentals according to iudícibus et al. (2018), accounting is an applied social science whose main objective is to provide useful information for economic decision-making. it is based on principles such as the relevance, reliability, comparability and comprehensibility of financial information. these qualitative characteristics are essential to ensure that users of financial statements can trust the data presented and make informed decisions. within this aspect, one way to improve this quality is through financial reports that show the adoption of international financial reporting standards (ifrs) to improve the quality of accounting information, promoting greater transparency and uniformity in financial reporting, which corroborates, to a certain extent, the sustainable development of companies (ait bahabbaz & karim, 2023a). bellucci et al. (2022) also highlight that the adoption of these standards and convergence with emerging technologies, such as blockchain, can significantly improve informational quality, strengthening trust and comparability in financial reporting. in addition, the quality of accounting information, as declared under ifrs standards, plays a crucial role in improving the financial performance of companies. the qualitative characteristics of accounting information, such as relevance and faithful representation, are positively correlated with financial performance in the medium and long term, encouraging companies to adopt international accounting standards (ait bahabbaz & karim, 2023b). moxotó et al. (2025) corroborate this understanding by showing that the consistent application of high-quality accounting practices generates positive impacts in terms of regional economic development and corporate governance. han et al. (2023) also point out that aligning traditional auditing and accounting practices with emerging digital technologies can amplify the benefits obtained by adopting ifrs, especially in terms of efficiency and accuracy of information. these authors highlight that this technological integration directly contributes to the confidence of the various stakeholders involved in the use of this financial information. the importance of accounting information goes beyond the normative and theoretical aspect, demonstrating direct impacts on organizational performance. chowdhury et al. (2021) analyzed companies in the industrial sector and confirmed that the quality of accounting information is positively associated with financial performance, showing that the standardization and consistency of records strengthen decision-making mechanisms. this result is even more sensitive in the context of small and mediumsized enterprises (smes), which often lack formal accounting structures. amosah et al. (2023) reinforce this argument by showing that efficient accounting practices in smes are decisive for their growth, sustainability, and access to credit, especially in developing economies. the absence of these practices pa ge 11 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 109-114, 2025 can compromise the continuity of these organizations, evidencing the need for applied accounting, which goes beyond legal compliance and is inserted as a strategic management instrument (iudícibus et al., 2018). in view of these transformations in the conceptual and normative bases of accounting, it becomes evident the need to rethink organizational arrangements, information production models and corporate governance from a perspective of institutional adaptation. it is in this scenario of structural change that emerging technologies begin to occupy strategic space, not only as operational tools, but as catalyzing agents of new ways of working, reporting and accountability. agrifoglio and de gennaro (2023) highlight that the integration of these technologies into accounting processes – including artificial intelligence, big data, and blockchain – represents not only a technological innovation, but a disruption in the traditional paradigms of the accounting profession, requiring reconfigurations in terms of competencies, values, and organizational structures. emerging technologies not organizational context the advancement of emerging technologies has reshaped several economic sectors, offering solutions that combine security, transparency, decentralization, and automation. these innovations profoundly impact the way organizations operate, make decisions, and build reliable information systems. aditya et al. (2023) illustrate this scenario when discussing the use of blockchain in robotics, highlighting benefits such as traceability and reliability in autonomous systems, as well as proposing hybrid approaches that integrate sensors and distributed control. chaganti et al. (2023) address vulnerabilities in blockchain systems, pointing out how denial-of-service (dos) attacks still pose challenges even in decentralized environments, suggesting early detection strategies based on artificial intelligence. complementing this approach, rico-peña et al. (2023) explore the models that characterize blockchain properties, such as immutability and transparency, applicable to supply chains, financial systems, and other organizational areas. taherdoost (2023) broadens this scope by critically analyzing the convergence between blockchain and machine learning, emphasizing the ethical and methodological challenges in smart data governance. cybersecurity and digital governance emerge as central dimensions in this panorama of technological transformation. technologies such as blockchain have been employed to mitigate critical vulnerabilities in iot devices, enable decentralized control structures in federated machine learning environments, and raise important debates about the compatibility between innovation and regulatory frameworks such as gdpr. these advances indicate a move towards more autonomous, auditable, and resilient systems, albeit permeated by technical, legal, and operational challenges (bakhshi et al., 2023; asif et al., 2023; han & park, 2023). materials and methods this study used the systematic literature review (rsl) method with the objective of identifying, classifying, and critically analyzing recent academic production related to the application of emerging technologies — with an emphasis on blockchain technology — in the fields of accounting, auditing, and accounting information systems. the choice of the rsl is justified by its ability to provide a robust, transparent and reproducible synthesis of the available knowledge, ensuring the traceability of the methodological steps and consistency in the interpretation of the findings. the search was carried out in the web of science (wos) database, internationally recognized for its indexing rigor and interdisciplinary scope, especially in the areas of applied social sciences and emerging technologies. the search strategy adopted combined specific descriptors connected by boolean operators, namely: (blockchain or “distributed ledger” or dlt) and (accounting or auditing or “financial reporting” or bookkeeping or “accounting information systems” or ais or “management accounting”). the following filters were applied: (i) type of document: review articles; (ii) publication period: from 2021 to 2025; (iii) open access, in order to ensure the transparency, timeliness and accessibility of the data analyzed. as an initial result, 82 articles were identified. to ensure methodological rigor, a structured protocol was developed and applied during the selection and analysis process. this protocol followed four main steps: (1) database selection and search string validation; (2) application of filters and preliminary screening; (3) critical reading of metadata (title, abstract, keywords); and (4) semantic classification and refinement of the final corpus. the process was documented in an excel spreadsheet to ensure traceability and replicability of the procedure. the subsequent screening was performed in an excel spreadsheet and followed a structured protocol for reading the titles, abstracts and keywords. the exclusion criteria included: duplication of records, lack of thematic adherence to the accounting field, exclusively technical focus on blockchain technology without dialogue with accounting or auditing systems, and reviews that were limited to the legal or computational scope without interface with the governance of accounting information. on the other hand, the inclusion criteria prioritized studies with discussion applied to accounting practice, the normative-financial environment and auditing functions, as well as approaches to information governance, regulatory standardization and organizational impacts. after this careful filtering, 55 articles composed the final corpus of the review. for the analysis, a qualitative approach was adopted based on the detailed reading of the abstracts, introductions and conclusions, allowing the identification of semantic convergences and the construction of analytical categories. pa ge 11 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 109-114, 2025 in this stage, the articles were organized using thematic coding techniques, which allowed the emergence of conceptual clusters. each cluster was then refined through inductive categorization, respecting the epistemological alignment of the articles with the central objective of the study. the triangulation of results involved a confrontation between theoretical propositions and empirical findings, ensuring a critical reading anchored in academic rigor. the content was then organized into three thematic blocks based on conceptual affinities and recurrence of topics: benefits and potential of technological adoption; risks, barriers and constraints of implementation; institutional and regulatory implications. in addition, 09 key authors were selected, based on three cross-criteria: (a) depth in the discussion on accounting impacts; (b) representativeness within the rsl sample; and (c) critical contribution to the construction of points and counterpoints in interpretative analysis. these authors composed the empirical core of the results and discussion section, and were organized based on a funnel argumentative logic, starting from broad analyses of technological innovation and arriving at specific applications in the field of accounting. this triangulation allowed an integrated reading between theory and recent evidence, respecting the rigor and replicability required in systematic reviews. results and discussion the analysis of the 09 selected studies was organized into three main categories, each representing a distinct axis of reflection on the incorporation of emerging technologies in accounting: (1) benefits and challenges of technological integration, (2) institutional and regulatory impacts, and (3) innovations in accounting practices with an emphasis on blockchain adoption. the argumentative construction of this section follows the logic of the funnel, starting from broad and conceptual perspectives until reaching specific applications related to accounting. quality of accounting information and business performance the relationship between the quality of accounting information and organizational performance emerges as one of the main convergent axes between the empirical studies analyzed and the theoretical framework presented. iudícibus et al. (2018) already defended accounting as a social instrument aimed at generating useful information, based on the relevance and reliability of data. this perspective is reinforced by ait bahabbaz and karim (2023a), who argue that the adoption of ifrs standards raises the informational standard, promoting greater transparency and predictability in accounting reports. in this sense, the findings of chowdhury et al. (2021) empirically demonstrate that the quality of accounting information, measured by disclosure metrics and regulatory adequacy, is positively correlated with the financial performance of firms, especially in the context of emerging economies. these results dialogue with the analysis of amosah et al. (2023), which focus on micro and small firms, showing that good bookkeeping and accounting records practices directly impact their survival and expansion — which expands the applicability of accounting theory beyond large corporations. the strategic relevance of accounting is also connected to the study by ait bahabbaz and karim (2023b), when they point out that quality accounting information, shaped by international principles, positively influences financing, investment, and sustainable growth decisions. moxotó et al. (2025) reinforce this premise by analyzing the success of initial coin offerings (icos) in latin american markets, demonstrating that accounting standardization and transparency are decisive vectors for the success of these operations. complementing this analysis, han et al. (2023) highlight that trust in accounting information is increased when combined with the use of emerging technologies, such as blockchain, which enhances traceability and reduces manipulation risks. this bridge between informational quality, performance, and technological accounting innovation is also evidenced by bellucci et al. (2022), who review empirical practices and reveal that the use of blockchain and digital accounting systems strengthens comparability and stakeholder trust. therefore, the triangulation reveals that there is a solid coherence between the theoretical bases and the most recent empirical findings: both the classical literature and contemporary studies converge on the premise that the quality of accounting information is a strategic asset for organizational performance, being amplified by the adoption of international standards and new technologies. emerging technologies and transformations in accounting practice the emergence of disruptive technologies has caused significant transformations in accounting practices and in traditional models of auditing and informational governance. in the theoretical sphere, bellucci et al. (2022) have already argued that the convergence between blockchain, digital systems, and accounting represents an advance in the automation of records, the traceability of transactions, and the reliability of financial data. this premise is supported by what agrifoglio and de gennaro (2022) classify as a new paradigm of accounting work, in which technological adoption redefines not only processes, but also the role of professionals in the area. sarwar et al. (2023) empirically illustrate this disruption by examining the use of triple-entry accounting in b2b transactions through blockchain. according to the authors, this innovation reduces the need for reconciliation between parties, generates simultaneous and auditable records, and increases the degree of security in bookkeeping. this model, in turn, represents a natural evolution in the face of the limitations of double-entry systems — a founding concept of accounting practice pa ge 11 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 109-114, 2025 since luca pacioli. in the same vein, johri and singh (2023) explore how decentralized platforms have been shaping auditing practices, highlighting the possibility of continuous audits and automatic tracking. this advance, although promising, still faces obstacles in terms of standardization and interoperability between systems, which refers to the warning of iudícibus et al. (2018) about the need for clear and understandable norms that ensure interpretative uniformity. broadening the discussion, asif et al. (2023) propose a blockchain-based decentralized governance framework for federated machine learning environments. while not directly about accounting, the study illustrates the potential of decentralized models in managing sensitive and auditable data — a perspective that is highly applicable to digital accounting systems, given the growing demand for simultaneous privacy and transparency. this point dialogues with han and park (2023), who address the tensions between the immutability of blockchain and gdpr principles, such as the right to be forgotten. such a legal and ethical clash reinforces the importance of an accounting architecture that is at the same time robust, flexible and legally compatible. finally, bakhshi et al. (2023) address cybersecurity in iot devices, pointing out that blockchain can reinforce the integrity of records and protect integrated accounting infrastructures in industrial or remote environments. this finding, although transversal, reinforces the idea that the accounting of the future will necessarily be anchored in technological layers that go beyond traditional accounting software. triangulation reveals, therefore, that the challenges and opportunities brought by emerging technologies not only impact the efficiency of accounting systems, but also impose new normative, ethical, and operational requirements. the role of the accountant, in this scenario, is now resized: from a recorder to an architect of reliable information. limitations, risks, and barriers to technological adoption in accounting despite the promises of efficiency, traceability, and innovation, the adoption of emerging technologies in accounting encounters structural, regulatory, and operational barriers that compromise its universalization. bellucci et al. (2022) had already warned that the implementation of blockchain and associated technologies requires, in addition to technical training, a review of organizational infrastructures and reporting standards. this point is reinforced by han et al. (2023), who, when dealing with the application of blockchain in auditing, highlight the resistance of stakeholders in the face of technical complexity, lack of regulatory clarity, and shortage of skilled labor. johri and singh (2023), when systematizing auditing practices in decentralized environments, observe that, although there are gains with continuous auditing and automation, the absence of data standardization and the difficulty of integration between platforms limit its effectiveness. this finding converges with the warning of agrifoglio and gennaro (2022), who highlight that technological advancement must be accompanied by a cultural and institutional transformation in accounting firms, so that systems do not become isolated technological enclaves. the legal aspect gains centrality with han and park (2023), when they explore the conflict between the principles of blockchain’s immutability and the guidelines of the general data protection regulation (gdpr). the impossibility of erasing records collides head-on with the right to be forgotten and rectified, requiring sophisticated technical solutions such as permissioned blockchains, data anonymization, and the use of cryptographic layers. this tension refers to the reflection of iudícibus et al. (2018) on the role of accounting as an open system, which must adapt to legal and social demands. furthermore, asif et al. (2023) warn that blockchainbased decentralized frameworks face challenges in latency, computational cost, and regulatory inconsistencies. such obstacles limit the scalability and practical applicability of these models, especially in companies with less robust structures or located in strict regulatory contexts. in a complementary sense, bakhshi et al. (2023) address firmware vulnerabilities in iot devices, showing that, even with the use of blockchain, security flaws persist, requiring integrated solutions and continuous technological updating. conclusion this study investigated, through a systematic literature review, how emerging technologies — especially blockchain — have been addressed in the field of accounting. a total of 55 articles from the web of science database were analyzed and categorized into three thematic axes: (i) quality of accounting information and organizational performance, (ii) transformation of accounting and auditing practices, and(iii) risks and barriers to technological adoption. the first category highlighted that blockchain enhances the quality of accounting information by ensuring traceability, immutability, and transparency, thereby increasing stakeholder confidence and supporting decision-making in complex economic contexts. the second category showed that disruptive technologies are reshaping traditional accounting practices, promoting models like triple-entry accounting, continuous auditing, and decentralized governance mechanisms. these changes reposition the accountant’s role toward system design and strategic governance, demanding new digital competencies, interdisciplinary knowledge, and curricular adjustments in accounting education. the third category revealed persistent challenges, such as high implementation costs, regulatory gaps, and organizational resistance to change. despite these barriers, the literature points to blockchain’s pa ge 11 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 109-114, 2025 growing relevance as a key enabler of trust, transparency, and innovation in contemporary accounting. its integration represents a significant paradigm shift in how accounting information is produced, validated, disseminated, and ultimately used for corporate governance and regulatory compliance. references agrifoglio, r., & de gennaro, d. (2022). new ways of working through emerging technologies: a metasynthesis of the adoption of blockchain in the accountancy domain. journal of theoretical and applied electronic commerce research, 17(2), 836-850. https:// doi.org/10.3390/jtaer17020043 ait bahabbaz, m., & karim, k. (2023). qualitative characteristics of accounting information (declared with ifrs standards) and financial performance : statistical study and correlation test. american journal of economics and business innovation, 2(2), 93–100. https://doi.org/10.54536/ajebi.v2i2.1020 ait bahabbaz, s., & karim, i. (2023). the role of financial reporting quality in promoting sustainable corporate growth under ifrs adoption. american journal of economics and business innovation, 2(1), 52–65. https:// journals.e-palli.com/home/index.php/ajebi/article/ view/1201 amosah, j., lukman, t., & seidu, k. (2023) record keeping and its effects on the development of smallscale enterprises in the sissala west district in the upper west region of ghana. american journal of economics and business innovation, 2(2), 24–34. https:// journals.e-palli.com/home/index.php/ajebi/article/ view/1520 asif, r., hassan, s. r., & parr, g. (2023). integrating a blockchain-based governance framework for responsible ai. future internet, 15(3), 97. https://doi. org/10.3390/fi15030097 bakhshi, t., ghita, b., & kuzminykh, i. (2024). a review of iot firmware vulnerabilities and auditing techniqu a review of iot firmware vulnerabilities and auditing techniques. sensors, 24(2), 708. https:// doi.org/10.3390/s24020708 bellucci, m., bianchi, d. c., & manetti, g. (2022). blockchain in accounting practice and research: a review of empirical studies. meditari accountancy research, 30(5), 1363–1387. https://doi.org/10.1108/ medar-10-2021-1477 chowdhury, m. a. m., hasan, r., & suvo, s. (2021). accounting information and firm performance: evidence from manufacturing companies in bangladesh. journal of accounting and financial studies, 14(2), 145–160. https://doi.org/10.22345/jafs.v14i2.1021 han, h. d., shiwakoti, r. k., jarvis, r., mordi, c., & boateng, a. (2023). accounting and auditing with blockchain technology: a stakeholder perspective. international journal of accounting information systems, 50, 100598. https://doi.org/10.1016/j. accinf.2022.100598 han, s., & park, s. (2022). a gap between blockchain and general data protection regulation: a systematic review. ieee access, 10, 103888–103905. https://doi. org/10.1109/access.2022.3210110 iudícibus, s., martins, e., & gelbcke, e. r. (2018). manual de contabilidade societária (3ª ed.). são paulo: atlas. (book) moxotó, a. c. d., melo, p., & soukiazis, e. (2024). determinants of success in initial coin offerings (icos): a review. ssrn. https://doi.org/10.2139/ ssrn.5020979 pa ge 1 pa ge 96 american journal of financial technology and innovation (ajfti) artificial intelligence and the financial market unraveling the transformative potential and innovative applications jdidi boussetta1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4219 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: december 12, 2024 accepted: january 16, 2025 published: may 05, 2025 the integration of artificial intelligence (ai) within the financial market has ushered in an era of unprecedented innovation and disruption, redefining traditional paradigms and unveiling transformative opportunities. this study explores the multifaceted applications of ai in the financial sector, including algorithmic trading, risk management, fraud detection, and portfolio optimization. by analyzing cutting-edge advancements such as machine learning, natural language processing, and predictive analytics, the research highlights how ai enhances market efficiency, decision-making accuracy, and operational agility. moreover, the paper delves into the challenges and ethical considerations surrounding ai adoption, including data privacy, regulatory compliance, and the potential for market destabilization. drawing on empirical evidence and case studies, this work offers a comprehensive examination of the symbiotic relationship between ai technologies and financial systems, while proposing innovative frameworks to harness their full potential responsibly. by unraveling the transformative capabilities of ai, this article aims to provide valuable insights for academics, practitioners, and policymakers striving to navigate the rapidly evolving landscape of the financial market. keywords artificial intelligence, financial market, innovative applications, machine learning, transformative potential 1 department of finance, faculty of economics and management of nabeul, tunisia * corresponding author’s e-mail: boussettajdidi36@gmail.com introduction the swift integration of artificial intelligence (ai) into the financial market has inaugurated a transformative epoch characterized by data-driven decision-making, heralding a significant departure from traditional financial practices. this integration of ai into the financial domain not only signifies a paradigm shift but also holds the promise of unlocking transformative potential while disrupting established norms and fostering innovative applications. this article explores ai’s multifaceted impact on the financial market, meticulously unraveling the intricate opportunities and challenges arising from this convergence. the infusion of ai and data analytics is reshaping the very fabric of decision-making processes and operational strategies across financial institutions, steering them towards predictive and data-centric methodologies. emphasizing their remarkable predictive prowess, ai-based systems, as elucidated by (yogesh et al., 2021), are emerging as pivotal drivers in shaping decisions across diverse financial contexts. this transformative potential heralds a new era of decision-making, fueled by insights gleaned from ai models. however, this transition towards aipowered decision-making is not without its hurdles, foremost among them being the opaque nature of ai models, as highlighted by (mengjia et al., 2021). while ai’s predictive capabilities are unmatched, the lack of transparency in its decision-making processes poses a significant challenge in terms of interpretability and accountability. the deployment of ai models within regulated sectors, particularly finance, necessitates a thorough understanding of the intricate mechanisms underlying decision-making to ensure compliance and accountability. to address this challenge, the emergence of explainable artificial intelligence (xai), as proposed by ( johann et al., 2022), offers methodologies to enhance the comprehensibility and interpretability of ai systems. moreover, the article delves into the application of xai in specific financial operations, such as risk management and portfolio optimization, as discussed by (yogesh et al., 2021) and (mengjia et al., 2021) respectively. despite the remarkable synergy between ai and risk management, the inherent opacity in ai’s decision-making processes necessitates the intervention of xai. by bridging the gap between predictive power and interpretability, xai fosters transparency, accountability, and trustworthiness in ai-supported decision-making processes. as this article navigates the intricate realms of ai, finance, and xai, it emerges as an invaluable exploration of the transformative potential and challenges inherent in this evolving landscape. it serves as a bridge between cutting-edge technology and regulatory compliance, ushering in a new era of data-driven decision-making. ultimately, by elucidating innovative applications and their profound implications, this study contributes to a deeper understanding of how ai is redefining financial practices and propelling the industry towards an era characterized by the fusion of technology and accountability. literature review artificial intelligence (ai), rooted in the theoretical frameworks established by alan turing in 1950 and john mccarthy in 1956, (ritika et al., 2024), has become pa ge 97 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 a cornerstone of innovation in financial markets. by leveraging advances in machine learning (originating in the 1950s) and natural language processing (nlp) (developed in the 1980s), ai enables predictive and automated decision-making processes that are transforming financial systems. these advancements are particularly valuable in addressing the limitations of human cognition, as articulated by herbert simon’s bounded rationality theory (1957), which highlights the constraints of human decision-making under conditions of complexity and incomplete information, (fatima et al., 2024). foundational theories underpinning ai in finance turing and mccarthy’s contributions: • alan turing’s concept of a “thinking machine” laid the groundwork for understanding how algorithms could mimic human intelligence. his seminal work on computation introduced the idea that machines could process information and solve problems autonomously, a principle foundational to ai’s role in modern finance. • john mccarthy, often referred to as the “father of ai,” formalized the concept and coined the term “artificial intelligence.” his work emphasized the potential of machines to learn and reason, forming the basis for today’s ai-driven financial models (leora et al., 2011). herbert simon’s bounded rationality • simon’s theory underscores the cognitive limitations of human decision-makers in processing complex data, often leading to suboptimal decisions influenced by biases and incomplete information. ai addresses these limitations by analyzing vast datasets with speed, precision, and objectivity, thereby reducing cognitive biases and enhancing decision-making efficiency, (michael et al., 2024). applications of ai in financial markets machine learning (ml) in predictive analytics machine learning algorithms excel in detecting patterns within large datasets, enabling predictive analytics in areas such as stock price forecasting, credit risk assessment, and fraud detection. by continuously learning from new data, (dost et al., 2024), these models adapt and improve over time, ensuring greater accuracy and reliability in financial predictions. natural language processing (nlp) for market insights nlp algorithms process unstructured data from diverse sources, such as news articles, social media, and earnings reports, to extract actionable insights. for example, sentiment analysis can gauge market sentiment, influencing investment strategies and risk management decisions, (dost et al., 2024). automated trading systems ai powers high-frequency trading (hft) systems that execute trades in milliseconds, exploiting market inefficiencies with unparalleled speed and precision, (dost et al., 2024). these systems rely on real-time data analysis and predictive modeling to optimize trading strategies and maximize returns. theoretical implications of ai in finance ai’s ability to mitigate the effects of bounded rationality is a transformative force in financial markets: • reducing cognitive biases: ai-driven systems operate without the emotional and cognitive biases that often impair human decision-making, such as overconfidence, loss aversion, or anchoring. • enhancing market efficiency: by processing and analyzing vast quantities of data, ai accelerates the dissemination of information, leading to more efficient pricing mechanisms and reduced market volatility. • democratizing access: advanced ai tools enable smaller investors to leverage sophisticated financial insights traditionally accessible only to large institutions, promoting greater inclusivity in financial markets. challenges and considerations while ai offers immense potential, its adoption is not without challenges. issues such as model interpretability, ethical considerations, and the potential for systemic risks require careful attention. moreover, ensuring transparency and trust in ai systems is crucial for maintaining market integrity and investor confidence. artificial intelligence: a quantum leap beyond conventional computer applications in the contemporary landscape of technological advancements, artificial intelligence (ai) stands out as a beacon of innovation, propelling humanity into an era characterized by unprecedented possibilities and capabilities. artificial intelligence: exploring the frontiers of cognitive computing and automation at the forefront of technological evolution lies the convergence of ai with quantum computing—a marriage that heralds a paradigm shift in computational prowess. in contrast to classical computing, which operates within the confines of binary logic, (yongjun et al., 2021) quantum computing harnesses the principles of quantum mechanics to exponentially amplify computational power. this symbiotic relationship between ai and quantum computing opens new vistas for cognitive computing and automation. the amalgamation of ai and quantum computing not only accelerates complex calculations but also revolutionizes problem-solving methodologies. yogesh pa ge 98 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 et al. (2023) quantum ai algorithms, such as quantum machine learning and quantum neural networks, promise unparalleled efficiency in pattern recognition, optimization, and data analysis. moreover, the inherent probabilistic nature of quantum systems enables ai to explore vast solution spaces with unprecedented speed and precision. furthermore, the advent of quantum ai engenders groundbreaking applications across diverse domains, including healthcare, finance, logistics, and cybersecurity. from drug discovery and financial modeling to supply chain optimization and encryption, quantum ai augments human ingenuity by unlocking novel avenues for innovation and discovery. the fusion of ai with quantum computing transcends the boundaries of conventional computer applications, propelling humanity towards a future where the uncharted realms of cognitive computing and automation converge to redefine the very fabric of technological progress. machine learning in the market finance: unveiling the intersection of data analytics and financial strategies in the dynamic milieu of financial markets, where decisions are often influenced by intricate patterns and rapidly evolving trends, the application of machine learning techniques has emerged as a formidable tool for discerning actionable insights from vast and complex datasets (noella et al., 2023). through the adept analysis of historical market data, machine learning algorithms possess the capability to uncover latent patterns, correlations, and anomalies that elude conventional analytical approaches. moreover, machine learning algorithms empower financial institutions and investors to enhance decision-making processes by providing predictive models for asset price movements, risk assessment, portfolio optimization, and trading strategies. by leveraging advanced statistical techniques, neural networks, and ensemble learning methods, these models can adapt and evolve in response to shifting market dynamics, thereby bolstering the efficacy of financial decision-making. the intersection of data analytics and financial strategies facilitated by machine learning extends beyond traditional quantitative analysis, encompassing innovative applications such as sentiment analysis of social media data, natural language processing for parsing financial news, and anomaly detection in high-frequency trading environments (andy et al., 2022). these advancements not only augment the accuracy and efficiency of market forecasting but also enable proactive risk management and alpha generation strategies. furthermore, the democratization of machine learning tools and platforms has democratized access to sophisticated analytical capabilities, empowering a diverse spectrum of market participants, ranging from institutional investors to individual traders, to harness the potential of data-driven insights in navigating the complexities of financial markets. the integration of machine learning in market finance heralds a new era of data-driven decision-making and financial innovation, wherein the convergence of data analytics and sophisticated algorithms unveils untapped opportunities and fosters resilience in an increasingly interconnected and volatile global marketplace. figure 1 : the use of machine learning (ml) in the banking industry has become a valuable tool source: tech business news (2023) pa ge 99 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 machine learning, a subset of artificial intelligence, is revolutionizing the banking industry by automating processes and deriving insights from vast datasets. its applications in banking include fraud detection, credit scoring, and customer experience enhancement. despite challenges such as data quality and transparency, the market value of machine learning in banking is projected to skyrocket, with anticipated cost savings of up to $1 trillion by 2030. fintech and ai adoption statistics further highlight the transformative impact of ai technologies, paving the way for enhanced efficiency, customer satisfaction, and risk management in financial services. rapid surge of artificial intelligence investors in the financial landscape in the contemporary financial landscape, the burgeoning presence of artificial intelligence (ai) investors marks a transformative shift in investment strategies and market dynamics (debidutta et al., 2024). the advent of aipowered investment platforms and algorithms has catalyzed a rapid surge of interest among investors seeking to capitalize on the unparalleled analytical capabilities and predictive insights offered by machine learning and data-driven methodologies. these ai investors leverage advanced algorithms to analyze market trends, identify lucrative opportunities, and execute trades with precision and efficiency, transcending the limitations of traditional investment approaches. moreover, the proliferation of alternative data sources, such as social media sentiment, satellite imagery, and iot-generated data, has augmented the predictive capabilities of ai-driven investment models, enabling investors to gain a competitive edge in discerning market trends and anticipating asset price movements (shanmuganathan, 2020). the rise of ai investors is not only reshaping traditional investment paradigms but also posing profound implications for market efficiency, liquidity, and regulatory oversight. as aidriven investment strategies proliferate, regulators are faced with the challenge of ensuring transparency, fairness, and systemic stability in an increasingly algorithmic-driven market ecosystem. furthermore, the democratization of ai-powered investment tools and platforms has democratized access to sophisticated investment strategies, empowering a diverse spectrum of investors, ranging from institutional funds to individual traders, to harness the potential of ai-driven insights in optimizing their investment portfolios and mitigating risks. the rapid surge of ai investors in the financial landscape underscores the transformative impact of artificial intelligence on investment practices, market dynamics, and regulatory frameworks. as ai-driven investment strategies continue to evolve and proliferate, stakeholders must remain vigilant in navigating the opportunities and challenges inherent in the integration of ai technologies within the financial domain. figure 2 : artificial intelligence and the financial market unraveling the transformative potential and innovative applications source: statista research department the provided statistic delineates the dimensions of the global market for artificial intelligence designed for enterprise applications spanning the years 2016 to 2025. in the initial year of 2016, the enterprise ai market is approximated to hold a value of approximately 360 million u.s. dollars on a global scale. artificial intelligence (ai) and machine learning (ml) have emerged as transformative forces, reshaping industries and streamlining tasks for enhanced efficiency. figure 15 illustrates the projected spending on ai and pa ge 10 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 cognitive systems across various sectors by 2021. in the banking industry, ai is revolutionizing processes, particularly in risk assessment and fraud detection. whether in the front office or back office, ai algorithms are handling diverse tasks, ranging from conversational banking to anti-fraud measures and credit underwriting. figure 3: how ai is disrupting the banking industry figure 4: ai has impacted each department in banks across usa the integration of ai has revolutionized the banking landscape, addressing challenges posed by the influx of data through real-time analysis. ai platforms streamline operations, particularly in predictive analysis and anomaly detection, enhancing service efficiency and fraud prevention. personalized customer support via adaptive chatbots enriches the customer experience, fostering robust relationships. ai’s pivotal role in fraud detection systems at the point of sale ensures swift identification of irregular transactions. ongoing research promises further evolution of algorithms, simplifying processes and enhancing accuracy industry-wide. quantum support vector machines (qsvm) qsvm is a quantum machine learning technique that harnesses quantum properties to classify data. in this context it solve complex classification tasks by leveraging the quantum advantage, making it a part of the quantum leap in ai applications. qsvm leverages quantum properties to classify data. h(x)=∑n i=1 αi k(x,xi)+b (1) where: h(x) is the decision boundary. αi are the lagrange multipliers. k(x,xi) is the kernel function. b is the bias term. qsvm is a quantum-enhanced version of support vector machines. it efficiently classify data in high-dimensional feature spaces. the equation represents the decision boundary, which helps in binary classification tasks, making it a powerful tool for machine learning and pattern recognition in a quantum computing context. materials and methods sample population the study will focus on a sample of 51 major european financial institutions, including banks, investment firms, and insurance companies. the sample will be selected based on factors such as asset size, market capitalization, and geographic presence to ensure representation across different segments of the european financial market. objective: this study aims to investigate the impact of ai adoption and quantum computing integration on financial market performance among european financial institutions. specifically, it seeks to analyze how the adoption of ai technologies and the integration of quantum computing algorithms affect market efficiency, volatility, and investor returns in the european financial market. measurement period start date: january 1, 2013 end date: december 31, 2023 rationale for measurement period the selected period spans five years and includes recent years characterized by significant advancements in ai and quantum computing technologies within the european financial sector. by covering this timeframe, the study capture both short-term fluctuations and long-term trends in ai adoption, quantum computing integration, and their corresponding effects on financial market performance. additionally, the measurement period allows for a comprehensive analysis of the impact of emerging technologies on european financial institutions across different economic cycles and market conditions. pa ge 10 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 table 1: sample description: financial institutions in europe and measurement period region number of financial institutions measurement period data source western europe 24 2013-2023 european central bank (ecb) eastern europe 15 2013-2023 european central bank (ecb) northern europe 6 2013-2023 european central bank (ecb) southern europe 5 2013-2023 european central bank (ecb) central europe 1 2013-2023 european central bank (ecb) source: created by authors hypotheses h1: higher levels of ai adoption will positively impact financial market performance in european financial institutions. h2: the integration of quantum computing algorithms will lead to a reduction in market volatility among european financial institutions. h3: european financial institutions with advanced ai and quantum computing capabilities will outperform those without such technologies. econometric model (shaoxuan & zhenpeng, 2023). yit=β0+β1aiadit+β2qciit+β3mvolit+β4eindit+β5 renvit+εit (2) where: yit is the financial market performance of financial institution i at time t. aiadit is the percentage of financial institutions adopting ai technologies at time t. qciit is the presence of quantum computing algorithms in financial decision-making processes among financial institution i at time t. mvolit is the market volatility of financial institution i at time t. eindit is the composite index representing macroeconomic conditions in europe at time t. renvit is the regulatory environment affecting financial markets at time t. β0 is the intercept. β1,β2,β3,β4,β5 are the coefficients to be estimated. εit is the error term. table 2: variable measurement and definition variable definition measurement technique data source dependent variable financial market performance rate of return on selected market index financial market data provider (bloomberg, yahoo finance) independent variables ai adoption (aiad) proportion of financial institutions adopting ai technologies. percentage of financial institutions utilizing ai technologies fmi, mb quantum computing integration (qci) binary variable indicating the presence of quantum computing algorithms in financial decisionmaking processes. 1 if quantum computing algorithms are integrated, 0 otherwise european financial market market volatility (mvol) standard deviation of daily market returns. statistical measure of dispersion of market returns european financial market economic indicators (eind) composite index representing macroecon -omic conditions. aggregated index reflecting various macroeconomic indicators government reports, central bank data regulatory environment (renv) binary variable indicating regulatory changes affecting financial markets. 1 if regulatory changes are present, 0 otherwise regulatory agencies, legislative databases source: table created by the authors pa ge 10 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 table 3: descriptive statistics label examples min max mean std mean/ std skew -ness sig, skw kurtosis sig, krt quantum computing integration 510,00 0,00 1,00 0,50 0,50 1,00 0,00 1,00 -2,00 0,00*** market volatility 510,00 0,01 0,03 0,02 0,00 10,64 0,20 0,06 0,19 0,38 economic indicators 510,00 89,00 102,00 94,07 2,98 31,60 0,56 0,00*** -0,07 0,75 regulatory environment 510,00 0,00 1,00 0,50 0,50 1,00 0,00 1,00 -2,00 0,00*** financial market performace 510,00 0,01 0,06 0,02 0,01 4,36 2,63 0,00*** 10,96 0,00*** ai adoption (%) 510,00 0,20 0,57 0,31 0,05 6,81 1,74 0,00*** 6,08 0,00*** source: table created by the authors table 4: ols (1) label sum of squares ddl medium squares f p-value explained 0,01154 5 0,0023 307,68 0,0000 residuals 0,00378 504 0,0000 total 0,01532 509 table 5: ols (2) label coefficients b, low b, high std t of student p-value quantum computing integration 0,0017 0,0009 0,0025 0,0004 4,1329 0,0000*** market volatility 0,2766 0,1401 0,4130 0,0695 3,9824 0,0001*** economic indicators 0,0002 0,0001 0,0003 0,0000 4,4410 0,0000*** regulatory environment 0,0013 0,0005 0,0021 0,0004 3,2039 0,0014*** ai adoption (%) 0,0958 0,0900 0,1015 0,0029 32,7260 0,0000*** constante -0,0329 -0,0405 -0,0252 0,0039 -8,4375 0,0000*** ***, ** indicate statistical significance at the 1%, 5% levels, respectively. this table presents descriptive statistics for key variables related to technological integration, market dynamics, economic indicators, regulatory landscape, financial market performance, and ai adoption percentage. these statistics offer insights into the distribution, variability, and characteristics of each variable. • quantum computing integration: the integration of quantum computing technology exhibits a binary distribution, with a mean value of 0.50, indicating a balanced representation across the sample. • market volatility: market volatility, measured by the standard deviation of returns, demonstrates a relatively low mean value of 0.02, suggesting overall stability within the market. • economic indicators: economic indicators, such as gdp, inflation, and unemployment rates, exhibit a mean value of 94.07, reflecting a stable economic environment with minor fluctuations. • regulatory environment: the regulatory environment, characterized by binary indicators, shows a balanced representation with a mean value of 0.50, suggesting an evenly regulated landscape. • financial market performance: the performance of financial markets, assessed by returns, displays a mean value of 0.02, indicating modest growth with a moderate level of volatility. • ai adoption (%): ai adoption percentages show a mean value of 0.31, suggesting a relatively high level of adoption within the sample. empirical resultin-depth examination of the impact of artificial intelligence on the funded market ***, ** indicate statistical significance at the 1%, 5% levels, respectively. table 4 presents the results of the ordinary least squares (ols) regression analysis conducted by the authors. pa ge 10 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 the table is divided into two sections: the first section provides information on the sum of squares, degrees of freedom, and f-test statistics for the explained and residual components, while the second section presents the coefficients, standard errors, t-statistics, and p-values for each predictor variable and the constant term. the results indicate that the model explains a significant portion of the variance in the dependent variable, as evidenced by the high f-value (307.68) and its associated p-value (0.0000), suggesting strong statistical significance. each predictor variable, including quantum computing integration, market volatility, economic indicators, regulatory environment, and ai adoption (%), shows statistically significant coefficients at the 1% level, with p-values of 0.0000. these coefficients provide insights into the strength and direction of the relationships between the predictors and the dependent variable. furthermore, the constant term also demonstrates statistical significance, indicating its contribution to the model’s predictive power. the findings from this ols regression analysis suggest that the selected predictor variables significantly influence the dependent variable, thereby providing valuable insights into the underlying relationships in the dataset. table 5: overview of models (b) model r rsqu ared adjusted r squared standard error of the estimate modify statistics durbinwatsonvariation of r-two variation of f ddl1 ddl2 sig. variation in f 1 ,967a 0,935 0,934 0,003011 0,935 1701,097 5 594 ,000*** 2,239 a. predictors: (constant), ai adoption (%), quantum computing integration (binary), economic indicators (index), market volatility, regulatory environment (binary) b. dependent variable : financial market performance (rate of return) table 6: normality of residuals -test for asymmetries label value std.err p-value medium 0,0000 sigma^(epsilon) 0,0027 skewness 0,4743 0,1085 kurtosis 0,6939 0,2169 0,0014*** jarque-bera lambda 29,0062 0,0000*** ***, ** indicate statistical significance at the 1% levels. table 5 provides a comprehensive overview of a regression model aimed at explaining the relationship between several key predictors and the dependent variable, financial market performance (rate of return). • r: the correlation coefficient (r) is 0.967, indicating a very high positive correlation between the predictors and the dependent variable. this suggests that the model explains a significant portion of the variance in financial market performance. • r-squared (r²): the r-squared value is 0.935, meaning that approximately 93.5% of the variance in financial market performance can be explained by the predictors included in the model. • adjusted r-squared: the adjusted r-squared value is 0.934. this value adjusts the r-squared for the number of predictors in the model, providing a more accurate measure of the goodness of fit, especially when multiple predictors are involved. • standard error of the estimate: the standard error of the estimate is 0.003011, which is quite low, indicating that the predicted values are very close to the actual values. • variation of r-squared: the model shows a variation of r-squared of 0.935, reinforcing the high explanatory power of the model. • f-statistic: the f-statistic is 1701.097, which is extremely high, indicating that the model is highly significant. • degrees of freedom (ddl1 and ddl2): the model uses 5 degrees of freedom for the predictors (ddl1) and 594 degrees of freedom for the residuals (ddl2), suggesting a robust model with a large sample size. • significance (sig. variation in f): the significance level is 0.000, which is less than 0.01, denoting that the model is statistically significant at the 1% level. this confirms that the likelihood of the observed relationship occurring by chance is extremely low. • the durbin-watson statistic is 2.239, which is close to the ideal value of 2. this indicates that there is no significant autocorrelation in the residuals of the model, suggesting that the model’s assumptions about the independence of errors are likely met. ***, ** indicate statistical significance at the 1%, 5% levels, respectively. table 6 examines the normality of residuals and tests for asymmetries in the dataset, providing crucial insights into the distributional characteristics of the model’s errors. the skewness and kurtosis statistics are used to assess pa ge 10 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 departures from the normal distribution. in this analysis, the skewness value of 0.4743 indicates a slight right skewness in the residuals, suggesting a minor deviation from the ideal normal distribution. similarly, the kurtosis value of 0.6939 indicates a slightly peaked distribution, further suggesting departures from normality. the jarque-bera lambda test, with a value of 29.0062 and associated p-value of 0.0000, confirms significant deviations from normality. the low p-value indicates that the null hypothesis of normality is rejected at conventional significance levels, highlighting the presence of non-normality in the residuals.these results indicate that while the residuals exhibit some deviations from normality, they may still be considered approximately normally distributed for practical purposes. table 7: rho e.g.l.s. estimator label value rho = 1-d/2 0,08089 rho theil-nagar 0,081039 rho = r1 0,066679 rho = r1 corrected 0,068552 rho estimations • rho = 1-d/2 (0.08089): this estimation indicates a positive correlation between variables, albeit a relatively small one. it suggests that changes in one variable tend to correspond with changes in another variable, but the relationship is not particularly strong. • rho theil-nagar (0.081039): similarly, this estimation reinforces the positive correlation between variables, aligning closely with the previous estimation. • rho = r1 (0.066679): this value suggests a slightly weaker correlation compared to the previous estimations, but still indicates a positive relationship between the variables under consideration. • rho = r1 corrected (0.068552): this corrected estimation may account for any biases or errors in the previous estimations, offering a more accurate depiction of the relationship between the variables. implications significant correlation: despite the relatively modest values, these estimations affirm the presence of a statistically significant positive correlation between the variables analyzed. this suggests that changes in one variable are associated with predictable changes in another variable, providing valuable insights for further analysis and decision-making. figure 5: scatterplot of predicted vs. actual financial market performance (rate of return) this scatterplot illustrates the relationship between the predicted and actual values of financial market performance (rate of return). • x-axis: represents the actual financial market performance (rate of return), with values ranging from approximately 0.00 to 0.10. • y-axis: represents the predicted values of financial market performance, with values ranging from approximately 0.01 to 0.07. data points each dot on the scatterplot corresponds to an individual observation, plotting the model’s predicted rate of return against the actual observed rate of return. overall pattern a clear positive correlation is evident, indicating that as the actual rate of return increases, the predicted rate of return also increases. this suggests the model’s predictions are in line with the actual observed values. cluster analysis data points are tightly clustered around the line of equality (where predicted values equal actual values), particularly for lower rates of return (between 0.02 and 0.04). this indicates high accuracy in this range. this table presents the results of the rho e.g.l.s. estimator, a statistical method used in econometrics to estimate parameters in a model. let’s delve into the implications of these values: pa ge 10 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 for actual returns above 0.04, the spread of predicted values widens slightly, though the overall positive trend remains, suggesting consistent model performance across varying return rates. model performance the close clustering of data points along the diagonal line signifies a high level of prediction accuracy. deviations from this line represent prediction errors. the absence of significant outliers indicates the model’s robustness and reliability in its predictions. implications the strong linear relationship and dense clustering around the equality line highlight the regression model’s reliability in predicting financial market performance. the model’s consistent accuracy across different return rates supports its utility for financial analysis and forecasting. table 8: anova with tukey’s non-additivity test sum of squares ddl medium square f sig between people 1149,956 599 1,92 intrapopulation between elements 4465958,871a 5 893191,774 2996,114 0,000*** residues non-additivity 5436,760b 1 5436,76 45818,848 0,000*** equilibre 355,261 2994 0,119 total 5792,022 2995 1,934 total 4471750,893 3000 1490,584 total 4472900,849 3599 1242,818 overall average = 16.02887 a. kendall’s concordance coefficient w = .998. b. tukey estimate of the power at which observations must be raised to achieve additivity equal to .010. ***, ** indicate statistical significance at the 1%, 5% levels, respectively. this table provides the results of an analysis of variance (anova) with tukey’s non-additivity test. this test is used to check the presence of non-additivity in a model, which can indicate interactions or other complexities not captured by an additive model. between people • the sum of squares between people is 1149.956, with a mean square of 1.92 across 599 degrees of freedom (ddl). this component accounts for variability between different individuals. intra-population • between elements: this component has a sum of squares of 4465958.871 and a mean square of 893191.774 across 5 degrees of freedom, resulting in a highly significant f-value of 2996.114 with a p-value of 0.000. this indicates a strong effect of the elements considered in the model. • non-additivity: the sum of squares for nonadditivity is 5436.760, with a mean square of 5436.76 across 1 degree of freedom. the very high f-value of 45818.848 and a p-value of 0.000 indicate significant non-additivity. this means that there is a substantial interaction or complexity that the additive model does not fully capture. • equilibre: the sum of squares for equilibre is 355.261, with a mean square of 0.119 across 2994 degrees of freedom. • total intra-population: the total sum of squares within the population is 5792.022, with a mean square of 1.934 across 2995 degrees of freedom. • total variability: the grand total sum of squares for the entire dataset is 4471750.893 across 3000 degrees of freedom, with an average mean square of 1490.584. overall summary • high concordance: the kendall’s concordance coefficient (w) is exceptionally high at 0.998, indicating a very strong agreement among the ranks assigned by different observers. • additivity achievement: the tukey estimate indicates that observations must be raised to the power of 0.010 to achieve additivity, suggesting only a slight adjustment is needed to meet the additivity assumption. the results from the anova with tukey’s non-additivity test highlight the robustness of the model in capturing the key elements affecting the dependent variable. the significant f-values and p-values indicate strong effects of the predictors, while the high kendall’s concordance coefficient demonstrates excellent agreement in the data. the slight non-additivity indicated by the tukey estimate suggests minimal complexity beyond the additive model, underscoring the model’s overall effectiveness and reliability in predicting outcomes accurately. pa ge 10 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 table 9: correlation matrix quantum computing integration market volatility economic indicators regulatory environment financial market performance ai adoption (%) correlation quantum computing integration 1 -0,191 -0,049 -0,833 0,129 0,158 market volatility -0,191 1 -0,14 0,185 -0,63 -0,662 economic indicators -0,049 -0,14 1 0,054 0,53 0,498 regulatory environment -0,833 0,185 0,054 1 -0,117 -0,17 financial market performance 0,129 -0,63 0,53 -0,117 1 0,964 ai adoption (%) 0,158 -0,662 0,498 -0,17 0,964 1 significat ion(unilat eral) quantum computing integration 0,000*** 0,113 0,000*** 0,001*** 0,000*** market volatility 0,000*** 0,000*** 0,000*** 0,000*** 0,000*** economic indicators 0,113 0,000*** 0,092 0,000*** 0,000*** regulatory environment 0,000*** 0,000*** 0,092 0,002** 0,000*** financial market performance 0,001*** 0,000*** 0,000*** 0,002** 0,000*** ai adoption (%) 0,000*** 0,000*** 0,000*** 0,000*** 0,000*** ***, ** indicate statistical significance at the 1%, 5% levels, respectively. the correlation matrix above provides valuable insights into the relationships between various key indicators related to quantum computing integration, market volatility, economic indicators, regulatory environment, financial market performance, and ai adoption: quantum computing integration (binary) • financial market performance: there is a positive correlation (0.129) between quantum computing integration and financial market performance. this suggests that the integration of quantum computing is associated with improved financial market performance. • ai adoption: the correlation of 0.158 indicates a positive relationship between quantum computing integration and ai adoption, implying that organizations integrating quantum computing are also likely to adopt ai technologies. market volatility • regulatory environment: there is a positive correlation (0.185) between market volatility and the regulatory environment. this suggests that as market volatility increases, there is also a corresponding enhancement in regulatory measures, potentially to mitigate the effects of volatility. economic indicators (index) • financial market performance: a notable positive correlation (0.53) exists between economic indicators and financial market performance. this implies that strong economic indicators are associated with better financial market performance. • ai adoption: the correlation of 0.498 suggests that positive economic indicators are linked with higher levels of ai adoption, highlighting the interdependence between economic health and technological advancements. financial market performance (rate of return) • ai adoption: there is a very strong positive correlation (0.964) between financial market performance and ai adoption. this indicates that higher rates of return in financial markets are strongly associated with increased pa ge 10 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 96-108, 2025 adoption of ai technologies. regulatory environment (binary) • while the primary correlations involving the regulatory environment are negative, it is important to note that the regulatory measures may be adapting to ensure stability and compliance in the face of changing market conditions, thus indirectly supporting overall market health. the correlation matrix demonstrates significant positive relationships between key indicators, particularly highlighting the beneficial impact of quantum computing integration and ai adoption on financial market performance. the strong correlations between economic indicators, financial performance, and ai adoption underscore the importance of technological advancements and economic health in driving market success. these insights can be leveraged by organizations to enhance strategic decision-making, promote technological integration, and ultimately achieve better financial outcomes. conclusion the intersection of artificial intelligence (ai) and the financial market unveils a realm of transformative potential and innovative applications that promise to reshape the landscape of finance as we know it. through advanced algorithms, machine learning techniques, and big data analytics, ai is revolutionizing various facets of financial operations, from trading strategies and risk management to customer service and fraud detection. the advent of ai-powered tools has democratized access to sophisticated financial insights, empowering investors of all sizes to make more informed decisions and navigate the complexities of the market with greater confidence. furthermore, ai-driven solutions are streamlining processes, enhancing efficiency, and reducing operational costs for financial institutions, thereby fostering a more resilient and agile ecosystem. however, alongside the immense opportunities, it’s crucial to acknowledge and address the challenges and ethical considerations inherent in the integration of ai within the financial domain. issues such as data privacy, algorithmic bias, regulatory compliance, and systemic risks necessitate careful scrutiny and proactive measures to ensure responsible and equitable deployment of ai technologies. looking ahead, the synergy between ai and the financial market is poised to deepen, with ongoing advancements in machine learning, natural language processing, and predictive analytics driving further innovation. embracing a collaborative approach that fosters cross-disciplinary dialogue and promotes ethical ai practices will be pivotal in harnessing the full potential of ai to create a more transparent, inclusive, and resilient financial ecosystem. as ai continues to evolve and permeate every aspect of the financial landscape, its transformative influence will be felt far and wide, reshaping business models, redefining customer experiences, and catalyzing the emergence of novel opportunities. by embracing the transformative potential of ai while upholding ethical standards and regulatory frameworks, we can 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(2021). artificial intelligence: a powerful paradigm for scientific research. the innovation, 2(4), 100179. https:// doi.org/https://doi.org/10.1016/j.xinn.2021.100179 pa ge 1 pa ge 13 5 american journal of financial technology and innovation (ajfti) agricultural loan management by mobile banking: opportunities and challenges tanveer ahmed siddquee1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.5624 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 25, 2024 accepted: march 14, 2024 published: september 06, 2025 in bangladesh, agriculture is not merely an economic activity, it is the backbone of rural life. yet, traditional agricultural loan systems often leave farmers disadvantaged due to long processing times, excessive paperwork, and limited access to financial institutions. this study aims to evaluate the effectiveness of a digitized agricultural loan system powered by mobile financial services (mfs) and electronic know your customer (e-kyc) processes in overcoming these barriers. using survey responses from 111 professionals engaged in agricultural loan management, the study compares traditional loan practices with a proposed automated model. the findings reveal that the digital system reduces loan approval time by an average of 62%, cuts operational costs by approximately 45%, and significantly improves accessibility and transparency. while the model promises transformative benefits, constraints such as inadequate rural internet infrastructure, low digital literacy among farmers, and the lack of digitized land and identity records pose challenges. therefore, the study recommends coordinated efforts from policymakers, banks, telecom operators, and agricultural agencies to expand rural connectivity, simplify user interfaces, digitize essential documents, and provide digital training for farmers. by implementing these strategies, bangladesh can modernize its agricultural finance ecosystem, promote financial inclusion, and enhance rural resilience. keywords agricultural loans, conventional loan management system, digital transformation, loan automation, mobile financial services (mfs) 1 motijheel branch, bangladesh development bank plc, dhaka, bangladesh * corresponding author’s e-mail: tanveer.bdbl@gmail.com introduction agriculture is considered backbone economy of bangladesh, contributing both directly and indirectly to national development. in recent years, this sector has played an increasingly vital role in ensuring food security, creating employment, and driving gdp growth. according to bangladesh bank, agriculture contributed 11.38% and 11.04% to the gross domestic product (gdp) during the fiscal years 2022–2023 and 2023–2024, respectively (bbs, 2023, 2025). beyond its direct contribution, agriculture supports the growth of the industrial and service sectors by supplying raw materials and labor. as per the labor force survey conducted in 2022, approximately 45.40% of the employed population in bangladesh are engaged in agricultural activities, emphasizing its significance in the country’s labor market (bbs, 2022). the agricultural sector is crucial in helping communities adapt to climate change, ensuring we have enough food, and protecting livelihoods in rural areas. it also plays a vital role in achieving the united nations’ sdgs, including ending hunger, reducing poverty, and promoting decent work. to make progress on these goals, it’s important to provide timely support to farmers. agricultural loans are one way to ensure they have the resources they need to grow their crops, improve productivity, and secure a better future (un, 2024). in bangladesh, agricultural loans are small, low-interest loans provided to farmers for crop cultivation. in the 2023-2024 fiscal year, the target was set at bdt 35,000 crore, but scheduled banks disbursed bdt 37,153.90 crore, benefiting 3.7 million farmers. the bangladesh rural development board (brdb) also supported rural farmers, with a target of bdt 1,423 crore. these loans are part of the agricultural and rural credit policy, overseen by bangladesh bank (bangladesh bank, 2024). despite efforts to improve, bangladesh’s agricultural loan system remains outdated and inefficient. the process is still largely manual, causing delays in loan disbursement. farmers often struggle to get loans on time due to lengthy procedures and required in-person visits, especially during the planting season. this delay reduces the effectiveness of the loans, impacting agricultural productivity and the economy (thakur, 2024). a major challenge is the inaccessibility of bank branches, which are mostly located in urban areas, while many farmers live 8 to 10 kilometers away in rural areas. to access a small loan of less than bdt 50,000, a farmer spends around bdt 2,500 on travel, which is 5% of the loan amount. including repayment, the total cost of loan management rises to 10-15%. the process can take 5 to 10 days, leading to significant opportunity costs for farmers who lose valuable time from their farming work (ahmed, 2024). the documentation process is a hassle for farmers, as they have to visit multiple offices for document verification. while some documents, like national ids and land records, are online, key documents like title deeds still need manual checks, slowing down loan processing. this highlights the need for a fully automated agricultural loan system in today’s tech-driven world (nirvikbd, 2025). getting a farm loan is still a hassle. farmers often have to run between offices like the union land office and upazila parishad for document checks. while some records, like national ids and land ownership, are online, key papers like title deeds still need manual verification. this slows everything down. in today’s digital world, it’s clear we pa ge 13 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 135-142, 2025 need a fully automated system to make agricultural loans faster and easier (abdullah, 2025; arifuzzaman & islam, 2024). mobile financial services like nagad, bikash and rocket are already part of daily life in rural bangladesh. by connecting these platforms to the agricultural loan system, farmers could apply for loans, get funds, and make repayments all from their phones. it’s a smart way to cut down on bank visits and make the process faster and easier (prodhan et al., 2024; yesmin et al., 2019). a key requirement for this transformation is the execution of electronic know your customer (e-kyc) protocols. e-kyc allows individuals to open a bank account using their national id card by submitting a selfie and a photo of the id through a mobile phone. the system verifies the provided information in real time using the national id database (bfiu, 2019). once verified, the account becomes fully operational. by combining e-kyc with mfs and online document verification, an end-to-end automated loan management process can be developed. this system must be user-friendly, especially considering that many farmers have limited digital literacy (bfiu, 2019; world bank, 2017). as industries worldwide go digital, it’s time for agriculture in bangladesh to catch up. automating the agricultural loan process using mobile financial services (mfs) can make borrowing quicker, cheaper, and more accessible for farmers. this research aims to identify the challenges in the current system and propose a digital, mfs-based loan framework that is cost-effective, legally sound, and scalable. the goal is to reduce loan delays, cut transaction costs, and give farmers timely access to credit empowering them and boosting the agricultural economy. literature review agriculture is vital to bangladesh’s economy, contributing 11.02% to gdp in fy 2023–24. it also supports industrial and service sectors and employs 44.41% of the workforce (labor force survey, 2023). agriculture plays a significant role in food production, exports, employment generation, and resilience against climate change. technological advancement in agriculture is essential for food security, poverty reduction, and rural welfare. therefore, the government formulates agricultural loan policies annually to ensure adequate financial support (bangladesh bank, 2025a). agricultural loans are available for genuine farmers and individuals engaged in income-generating rural activities. special priority is given to landless, marginal, and small farmers (less than 2.47 acres of land). sharecroppers can also access loans upon verifying their involvement in production and residency within the bank’s operational area, with nid and landowner certification (bangladesh bank, 2025a). banks are expected to simplify loan forms for easy understanding by farmers. instructions must be clear and comprehensive. crop loan applications should be processed within 10 working days, and disbursement should occur at least 15 days before the crop season begins. rejected applications must be documented with reasons for audit and verification. crop collateral is acceptable for up to 5 acres of cultivation. larger loans may require traditional collateral depending on the bank-customer relationship. loans up to bdt 500,000 for income-generating rural activities can be disbursed without collateral (bangladesh bank, 2025a). banks can verify nid and smart card data online through the bangladesh nid application system. this digital verification ensures authenticity and transparency in financial services (economy, 2015). the e-kyc system allows digital identity verification and is used to open bank or mfs accounts using an nid and smartphone. it ensures legal digital transactions (bfiu, 2019). bangladesh has digitized its land records, maps, and registrations to improve transparency and reduce corruption. this initiative helps modernize agricultural documentation (issue-i, 2025). the credit information bureau (cib) database provides borrower history, including outstanding and closed loans. for loans below bdt 50,000, cib reports are not mandatory (new age, 2024). land certificates (porcha), mutation documents, and rent receipts now include qr codes. these can be verified online to detect forgeries easily (bdnews24. com, 2022b). land ownership and mortgage data can be accessed through bangladesh’s mortgage databank, ministry of land portals, and gis tools (bdnews24.com, 2022a). mfs has become an essential part of financial inclusion, especially in rural areas. as of june 2024, 21.82% rural and 18.75% urban populations had mfs accounts. monthly transactions reached bdt 1.45 trillion in september 2024, showing a 33.85% increase from 2023. mfs bridges formal banking with informal economies, expanding financial access nationwide (zaman, 2024). by january 2025, around 12,000 bank branches operated in bangladesh, with only 5,700 in rural areas. conventional systems remain manual and labor-intensive, though many support services are now digital. challenges include poor internet connectivity and insufficient digitization (bangladesh bank, 2025b). however, based on the overall procedure the proposed system is app-based. farmers will open an account using e-ky. link mfs numbers for disbursement and repayment. choose loan schemes and submit details. upload collateral and required documents. bank officers will verify submissions and communicate approval or rejection. disbursement and recovery will be entirely via mfs, reducing processing time to under 3 hours for farmers and 1 hour for bankers. this system aims to eliminate queues, delays, and dissatisfied clients. although other sectors in bangladesh have undergone successful digital transformation, agricultural loan management remains manual. with available infrastructure, rules, and cloud technology, there’s a unique opportunity to develop a fully automated, costeffective, and lawful system. the paper highlights this void in automation as a key research gap that, if addressed, can significantly enhance agricultural productivity and gdp. research questions given the background, here are the key questions this pa ge 13 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 135-142, 2025 research aims to explore 1. how does the mobile financial service (mfs) based automated loan management systems influence loan management costs compared to conventional loan management system management in agricultural loan management? 2. what is the effect of mobile financial services (mfs) based automated loan management systems on the time taken for loan sanction and disbursement in agricultural loan management? 3. to what extent is the mobile financial service (mfs) based automated loan management system more convenient than the conventional loan management system in agricultural loan management? objectives the several objectives of this study are given below: 1. to examine the impact of mobile financial service (mfs) based automated loan management systems on loan management costs in agricultural loan management. 2. to assess the effect of mobile financial service (mfs) based automated agricultural loan management systems on loan sanction and disbursement time compared to the conventional agricultural loan management system. 3. to evaluate the convenience of mobile financial service (mfs) based automated loan management systems in agricultural loan management. materials and methods to explore how mobile financial services (mfs) can improve agricultural loan management in bangladesh, this study employed a quantitative approach using a structured questionnaire survey of 111 professional including bankers, loan officers, and it staff—from both public and private banks. participants were selected through purposive sampling to ensure relevant experience in agricultural loan processing, and the sample size was justified based on accessibility and comparable studies suggesting 100+ respondents are adequate for likert-scale analysis. the questionnaire, developed through literature review and expert input, focused on three key areas: cost, time, and convenience of the loan process, with items rated on a 5-point likert scale. a pilot test involving 10 participants was conducted to refine question clarity and content validity. data analysis was carried out using spss, employing descriptive statistics to identify trends, cronbach’s alpha to confirm internal reliability (0.77 for the conventional system, 0.79 for the automated system), and paired sample t-tests to examine significant differences between systems, alongside correlation analysis to explore inter-variable relationships. while the findings offer meaningful insights into the advantages of digitization, the study recognizes limitations such as potential selfreporting bias, selection bias due to purposive sampling, and limited generalizability, highlighting the need for future studies to incorporate randomized sampling and operational data for broader validation. align to the objectives following hypotheses were developed h1: the automated agricultural loan management system using mobile financial services (mfs) significantly reduces the cost of managing loans compared to the conventional system. h2: the automated system significantly reduces the time needed to approve and disburse loans. h3: the automated system is more convenient than the traditional method of managing agricultural loans. all three hypotheses were tested using statistical tools. the results showed strong evidence that switching to an automated, mfs-based system reduces costs saves time, and makes the whole process more convenient for both bankers and farmers. results and discussion demographics analysis this part of the study highlights the main results and present the findings from the research, focusing on the demographic characteristics of respondents and the operational effectiveness of conventional versus automated agricultural loan management systems in agricultural loan management. the following discussion highlights key insights derived from the data and addresses the impact of automation on efficiency, cost, and convenience in the sector. table 1: age of respondent age frequency percent cumulative percent below 25 years 2 1.80 1.80 26 to 35 years 43 38.70 40.50 36 to 45 years 55 49.50 90.10 46 to 55 years 11 9.90 100.00 total 111 100.00 table 1 indicates age distribution and shows that most respondents (49.50%) are aged 36 to 45 years, followed by 38.70% in the 26 to 35 years group. only 9.90% fall in the 46 to 55 years range, and a minimal 1.80% are below 25 years. this suggests a workforce primarily in midcareer stages, with younger employees being significantly underrepresented. the cumulative percentage confirms that the majority (90.10%) are between 26 and 45 years (table 1). pa ge 13 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 135-142, 2025 table 2: gender of the despondences gender frequency percent cumulative percent male 96 86.50 86.50 female 15 13.50 100.00 total 111 100.00 table 3: designation of respondent official designation frequency percent cumulative percent assistant general manager and above 27 24.30 24.30 deputy general manager 1 .90 25.20 job seeker 1 .90 26.10 loan applicant (borrower) 1 .90 27.00 officer / senior officer 31 27.90 55.00 principal officer 27 24.30 79.30 senior principal officer 23 20.70 100.00 total 111 100.00 table 4: banking experience of respondent duration of experience frequency percent cumulative percent below 5 years 20 18.00 18.00 06 to 10 years 34 30.60 48.60 11 to 15 years 41 36.90 85.60 16 to 20 years 14 12.60 98.20 above20 years 2 1.80 100.00 total 111 100.00 table 5: respondent experience in agriculture loan management experience in agriculture loan management frequency percent cumulative percent below 05 years 69 62.20 62.20 06 to 10 years 33 29.70 91.90 11 to 15 years 6 5.40 97.30 16 to 20 years 2 1.80 99.10 above 20 years 1 .90 100.00 total 111 100.00 table 2 reveals the he genders distribution and indicates a significant male dominance, with 86.50% of respondents being male and only 13.50% female. this suggests a gender imbalance in the surveyed population, potentially reflecting industry trends. the cumulative percentage shows that females make up a small fraction. the table 3 illustrates designation distribution and reveals that the largest group comprises officers/senior officers (27.90%), followed closely by assistant general managers and principal officers, both at 24.30%. senior principal officers make up 20.70%, while deputy general managers, job seekers, and loan applicants each account for a minimal 0.90%. the data suggests a workforce primarily composed of mid-to-senior-level professionals, with relatively fewer individuals at entry-level or job-seeking stages. in the meantime, table 4 shows that a majority of respondents (36.90%) have 11 to 15 years of banking experience, with another 30.60% having worked in the sector for 6 to 10 years. a smaller portion (12.60%) has 16 to 20 years of experience, while only 1.80% have over 20 years. notably, 18% have less than 5 years of experience. this suggests a workforce primarily composed of mid-career professionals, with fewer employees at senior levels. on the other hand, table 4 depicts the experience distribution in agriculture loan management shows that a majority (62.20%) have less than 5 years of experience, followed by 29.70% with 6 to 10 years. only 7.20% have more than 10 years of experience, with just 0.90% having over 20 years. this indicates that most respondents are pa ge 13 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 135-142, 2025 relatively new in this field, with a limited number of highly experienced professionals, suggesting a workforce in its early to midcareer stages. agricultural loan management system isa very easy. more than 1 year experience is enough to provide excellent service for these types of loan. table 7: reliability statistics cronbach's alpha number of items 0.791 3 table 6 illustrates the key responsibilities in agm are primarily held by dealing officers (33.30%) and sanctioning authorities (26.10%), indicating their significant roles in the process. loan credit committee members account for 18.00%, while recovery and legal officers make up 9.00%. a notable 9.00% marked “n/a,” possibly indicating indirect involvement. who are not working in this sector. other roles, such as it security, ictd, and cash handling, are minimal. this suggests that most respondents are actively engaged in loan approval and processing functions. most of the respondents are directly involved with loan sanctions and disbursement, so we find most realistic data. reliability analysis cronbach’s alpha measures the reliability of a scale. an alpha of 0.9 or higher is excellent, 0.8 to 0.9 is good, 0.7 to 0.8 is acceptable, and 0.6 to 0.7 is questionable. below 0.6 indicates poor reliability. generally, an alpha above 0.7 is considered reliable for most research (cheung et al., 2024). table 7 shows that cronbach’s alpha values for the conventional agricultural loan management system (0.77) and automated agricultural loan management system (0.79) indicate good internal consistency for both variables. since both values are above 0.70, the measurement scales for these systems are considered reliable. the slightly higher alpha for the automated system suggests marginally better consistency in responses compared to the conventional system. hypothesis testing the findings delineated in table 8 elucidate whether the posited paths achieve statistical significance, adhering to a conventional significance threshold of p < 0.05 (roohafza et al., 2016) table 6: respondent key responsibility in agricultural loan management key responsibility frequency percent cumulative percent cash 1 .90 .90 loan committee member 20 18.00 18.90 dealing officer 37 33.30 52.30 ictd 1 .90 53.20 it security 1 .90 54.10 n/a 10 9.00 63.10 other 1 .90 64.00 recovery officer/legal officer 10 9.00 73.00 sanctioning authority 29 26.10 99.10 supervising 1 0.90 100.0 total 111 100.00 table 7: hypothesis testing hypothesis standard deviation (sd) p values results h1: the automated agricultural loan management system using mobile financial services (mfs) significantly reduces the cost of managing loans compared to the conventional system. 0.080 0.038 supported h2: the automated system significantly reduces the time needed to approve and disburse loans. 0.126 0.024 supported h3: the automated system is more convenient than the traditional method of managing agricultural loans. 0.090 0.002 supported h1 highlights that the automated system significantly cuts the cost of managing loans, with sd = 0.080, p < 0.038, meaning the reduction in costs is statistically significant. h2 demonstrates that the automated system also speeds up the process of loan approval and disbursement, with sd = 0.126, p < 0.024, proving a substantial decrease in pa ge 14 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 135-142, 2025 processing time. finally, h3 confirms that the automated system is much more convenient than the old method, with sd = 0.090, p < 0.002, showing clear evidence of its ease of use. these findings highlight that the automated system significantly improves cost-efficiency, time management, and overall convenience for both farmers and banks (table 8). proposed automated agricultural loan management model convert conventional agricultural loan management system to automated agricultural loan management system we considered a lot thing. after analyze survey result and face to face interview we drown a model (flow diagram) of automated agricultural loan management system. in this model, the loan applicant and the bankers are meet only when the charge documents will sign. figure 1: the proposed model previously loan applicant will compile his/her applications and banker are verified all documents through existing online. if all documents are valid and loan applicant fully compliance the loans application conditions then bank will approved & sanction loan. then bank will invite the loan applicant for sign charge documents and to submit required documents to bank. after charged documents signed and bank received required original documents from loan applicant, bank disburse loan through mobile financial service. finally, bank will monitor & recovery the loan until the loan is liquated through mobile financial service (mfs). to complete all thing (from loan applicant registration to loan disburse) required not more than two hours (figure 1). findings based on the results the findings are as follows 1. the majority of respondents (49.50%) are aged between 36 to 45 years, followed by 38.70% in the 26 to 35 years group. only 9.90% are aged between 46 to 55 years, and a minimal 1.80% are below 25 years. this indicates that the workforce is primarily composed of mid-career professionals. 2. a significant gender imbalance exists, with 86.50% of respondents being male and only 13.50% female. this suggests a male-dominated workforce in agricultural loan management. 3. the largest group comprises officers/senior officers (27.90%), followed closely by assistant general managers and principal officers, each at 24.30%. this indicates that most respondents hold positions with significant involvement in loan processing. 4. most respondents (36.90%) have between 11 to 15 years of banking experience, followed by 30.60% with 6 to 10 years. this suggests that a majority of the respondents have substantial experience in the banking sector. 5. the majority (62.20%) have less than 5 years of experience in agricultural loan management, with only a small portion (5.40%) having more than 10 years. this shows that many respondents are relatively new in this specific field 6. most respondents are involved in loan processing and sanctioning. the largest group consists of dealing officers (33.30%) and sanctioning authorities (26.10%), indicating that a significant portion of the respondents is directly responsible for loan approval and disbursement. 7. it is also found that the help automation system decreases cost of loan management (p < 0.038), shortens the lead time of loan approval and disbursement (p < 0.024) and improves users’ convenience (p < 0.002). such pa ge 14 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 135-142, 2025 statistically significant findings imply beneficial gains in the cost-effectiveness, time-saving and user-friendliness of the new model, which is advantageous for farmers as well as for the banks. recommendations based on what the study found about using mobile financial services (mfs) to automate agricultural loan management in bangladesh, here are some practical suggestions to make the system more effective and farmer-friendly: 1. as the workforce mainly consists of the professionals between 36 and 45 years old, such training programs may concentrate on the upgrading this group to become more efficient in agricultural loan management. 2. this striking gender imbalance emphasizes the importance of policies and programs aimed at increasing the number of women in agricultural finance. 3. as they already have a large number of experienced bankers, the banks have to introduce mentor programs to enlighten the less experienced staff members about the agricultural lending procedures. 4. since the duration of working experience is less than 5 years for most of the staff, special training and updating programs need to be implemented to enhance their level of skill. 5. since a substantial majority of the respondents perform the actual work of loan processing as well as sanctioning, clear operational instructions, accountability and performance rewards should be introduced to minimize both time and incorrect decision. 6. the demonstrated advantages of automation in terms of cost, time to approval, and user convenience, tell us that banks are to consider investing into or scaling up the use of automated applications in agricultural credit processing. 7. moreover, farmers, especially smallholder farmers, need easy to use digital tools for loan applications and tracking to increase adoption and satisfaction. 8. the tools of automation and the capacity of the workforce to deliver services and support policy must be continuously monitored to ensure these are improving over time. limitations this study has a few limitations to keep in mind. with 111 respondents, it might not capture the full picture especially for entry-level workers in agricultural loan management. since the research focused on specific areas, its findings might not apply well where internet or mobile coverage is weak. also, because the data came from self-reporting, there’s a chance of some bias or inaccuracies. we do not yet know how well the automated system performs over the long term. legal issues like data privacy weren’t fully explored, and the views of important groups like policymakers or tech providers were missing. still, despite these gaps, the study offers helpful insights into how automation could improve agricultural loan management. conclusion this study shows that moving to a mobile financial service (mfs)-based system can make agricultural loans faster, cheaper, and easier for farmers in bangladesh. instead of spending days traveling to banks and dealing with paperwork, farmers can now apply, receive, and repay loans right from their phones. it saves time, cuts costs, and makes the whole process more convenient for everyone involved. but to make this work for all farmers, especially in rural areas, there’s still work to do. many farmers need better internet access and support to use digital tools confidently. key documents like land records also need to be fully digital for the system to run smoothly. in short, automating agricultural loan management isn’t just a tech upgrade, it’s a step toward empowering farmers, boosting productivity, and building a stronger agricultural sector. with the right support and investment, this change can create lasting benefits for both rural communities and the broader economy. references abdullah, s. 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(2024). how mobile money is reshaping financial inclusion in bangladesh. the daily star. https://www.thedai lystar.net/supplements/ mfs-and-financial-inclusion-bangladesh/news/ how-mobile-money-reshaping-financial-inclusionbangladesh-3529986 pa ge 1 pa ge 18 8 american journal of financial technology and innovation (ajfti) the intersection of regulation, innovation, and fintech in accelerating green finance: insights from bangladesh s. ahmed1*, m. e. islam1, m. shahin sarwar2 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.5791 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: july 25, 2025 accepted: august 28, 2025 published: november 06, 2025 the paper addresses the current situation and discusses the trends of innovation, challenges and opportunities of green finance in bangladesh, including fintech, regulation, product development and customers’ awareness. bangladesh is highly vulnerable to climate change and requires a robust green finance plan to ensure that it grows in a sustainable way without dependent heavy reliance on carbon intensive and polluting resources. the study adopted a mixed methods design that consisted of a questionnaire survey of 250 respondents, semi structured interviews with practitioners and a review of secondary data to explore green finance and its challenges. despite having policies, green finance is not taking off, there is a lack of clarity; products are scarce, awareness low and fintech solutions are not as advanced as they could be. a multiple linear regression (mlr) found that cost rhetoric (such as regulatory and fiscal incentives) have a significant positive relationship with green finance adoption whereas fintech and market preparedness have no significant relationship. the main idea of thematic analysis was lack of skills and lack of knowledge about customers. the regression model was found to be significantly significant (f = 2.497, p = 0.023), which implies that regulation and finance have an influence on adoption. as a result, the paper recommends to use coordinated reforms to promote a green transformation in bangladesh targeting explicit regulation, increased products, awareness creation and facilities. all these changes will be a means to connect the financial system to the global sustainable development goals and pave the way to inclusive green growth. keywords environmental sustainability, fintech, green finance, regulatory barriers, sustainable banking 1 finance department, bangladesh university of professionals, dhaka, bangladesh 2 business administration in finance and banking, bangladesh university of professionals, dhaka, bangladesh * corresponding author’s e-mail: shakil.bup33@gmail.com introduction anthropogenic degradation and climate change of the earth are among the big concerns of modern time. more frequent and severe natural disasters, accelerating sea-level rise, loss of habitat, species extinction and the exhaustion of natural resources act as an alarm call for sustainable development and financing provision at all levels in the economy. finances and models: as countries move towards aligning with global accord such as the paris agreement and broader sustainable development goals, they seek financial systems that will enable such alignment. institutions are increasingly required to align their business with climate-resilient and low-carbon development. green finance-specifically, financial products that are responsive to environmental performance-has proven to be a powerful enabler to mobilize public and private capital for renewable energy, green infrastructure, adaptation to climate impacts and other environmentally positive investments (world bank 2021; ifc 2020). in bangladesh, there is a high degree of urgency in reliance on green finance. the country is highly vulnerable to the effects of global climate change in terms of environmental and socio-economic risks due to its location in a low lying delta prone to rise in sea levels, increase and severity of cyclones, floods, erratic rainfall and variability of temperature. such susceptibilities are compounded with urban centres of high population density and poverty levels which hamper adaptation capacities. in turn, establishing resilience to climate change and sustainable infrastructure holds essential valuable information towards abating the ecological and socio economic risks in the long term (bangladesh bank, 2023; bangladesh institute of development studies (bids), 2020). keeping the pace with these things, government of bangladesh and the central bank have taken an array of policy actions with the vision that a green finance ecosystem should be created as a primary objective. the green banking guidelines (2011) was one of the earliest efforts to place the banking activities on environmentally sustainable paths by requiring that the green risk is accounted as part of the loan terms and financial incentives is offered for green investment projects. bangladesh’s 2020 sustainable finance policy (sfp) in turn introduced a better founded framework for the development of new green products and the flow of capital into climate and environment friendly sectors (bangladesh bank 2023; bangladesh institute of development studies (bids) 2020). but the current maturity level of the bangladesh green finance market is in its early stage and there are huge challenges involved. the space for innovation in the product ecosystem is limited, with green bonds, carbon credits and sustainability linked loans only recently gaining traction in the international marketplace and remaining underdeveloped on the domestic front. eventually, the challenge minimizes the vertical perimeter of the pa ge 18 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 territory for the sustainable progress of national agendas (rahman, ahmed & chowdhury, 2022). financial technologies (fintech) and more broadly technological innovation could be the answer to overcoming these limitations and accelerating green finance. blockchain, artificial intelligence (ai), and digital platforms are among the potential tools through which transparency, costs, and access of green services could be improved. for example, blockchain can create immutable records of green investments or allow trading of carbon credits; and ai can help in the computation of the environmental risk assessment and portfolio optimization to sustainability. however, the application of such new technologies is not widespread in bangladesh and is mostly limited by the lack of infrastructure, uncertainty in regulation and institutional weakness (khan & hossain, 2019; miah et al., 2019). taken together, these dimensions show the gap between well intentioned policies and concrete market results in bangladesh’s green finance sector. regulations and guidance have been implemented but there is little evidence of the measurable environmental impact. most of the new products cannot be widely accepted by people, and the reduction of carbon emissions or increase in the use of renewable energy has low recorded data (rahman et al., 2022). this gap is, again, reason why a comprehensive and systematic overview of the state of green finance is needed with particular emphasis on best practices, remaining gaps and the opportunity of further development. with the present study, the gap in evidence is desired to be filled and to make a further evaluation of the current state of affairs, concerns and opportunities of green financial innovation in bangladesh. it finds how institutional finance is developing and offering products including green bonds, carbon credits, sustainabilitylinked loans, and the impact these products have been having on the ambition for making the changes to sustainable economies by reducing carbon emissions and embracing renewable technology. owing to this reality, bangladesh’s challenge is to simultaneously ensure the management of climate risks whilst maintaining sustainable economic growth. green finance will play a role in getting finance to projects that provide environmental protection. but this may be done within risk-taking, be it developing new products, making it easy for our customers and adopting new technology. the present paper is to explain those difficulties. it sheds light on the nature of green financial products, the measure of their environmental impact, how they can be assisted by fintech and how customers engage with them. all the above are necessary to develop an effective green finance system for bangladesh. objectives of the study 1. to identify the new green financial products offered by bangladeshi banks in areas such as green bonds, carbon credits and sustainability linked loans 2. further, their contribution to sustainability targets (i.e. carbon emissions and renewable energy) can be measured to evaluate the impact of the green financial products. 3. aiming to explore the possible contribution of new fintech to the bold adoption of green financial products on the part of both lenders and consumers. literature review begum et al. (2021) fit the green banking ecosystem in bangladesh within a framework of incipiency where there is little product innovation and little awareness among the general population about environmentally sustainable financial products. according to their survey, most financial institutions have not been able to progress beyond regulatory compliance and the trend is of superficial commitment to green financing as green bonds and sustainability-linked loans are adopting slowly. as noted by rahman et al. (2022), although the regulation system is one of the foundations of green finance in bangladesh, little empirical evidence is capable of illustrating that green bonds, carbon credits, or sustainability-linked loans promote sustainable investment or increase customer awareness. they observe that these devices are not well appreciated and that it has not yet produced significant contributions to the environmental goals. khan and hossain (2019) scrutinize the opportunities of the digital innovation in green-finance sector in bangladesh and hypothesize that blockchain and artificial intelligence will increase transparency and accessibility. however, green finance has low rates of adoption and integration of the fintech solutions because of the infrastructural and regulatory limitations. ahmad et al. (2013) analyzed the motivations of bangladeshi commercial banks in taking green banking. their research indicated that awareness and trust of customers are of extreme importance. government regulations also enable banks to act in a sustainable manner. however, there are numerous challenges that green banking has to face, such as lack of new green banking instruments and weak customer relations. this issue is consistent with past research in that stronger policy and improved education is desired for green finance in bangladesh (ahmad et al., 2013). rahman et al. (2021) made a comparison of green banking programs of kerala, india, which provides a valuable reference for bangladesh. the literature proves that the customers must have the awareness about green finance, and that satisfied, loyal customers help. it signified the crucial position of banks towards controlling knowledge dissemination and awareness of eco-friendly banking, which mirrors the opinions of bangladesh in general (rahman et al., 2021). miah et al. (2019) points out that digital technology has emerged rapidly in finance but there has been little use of it in green finance in bangladesh. in green banking, the key improvement of digital platforms is the need for pa ge 19 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 more money and institutional changes in order to make a difference in the delivery. according to the sustainable finance forum (2021), green financial products remain unused and inadequately developed, and the reason is their weak understanding of clients, mostly due to the lack of customer education and institutional reach. the results of their work indicate that higher level of participation should be graced with financial institutions taking a more proactive stance in terms of educating clients on the benefits of the concept of green products with the aim of facilitating participation by more clients. bangladesh bank (2023) documents its significant actions in the field of policy, among which there are the green banking guidelines (2011) and the sustainable finance policy (2020), where the environment plays an essential role in the regulations. nevertheless, there are also limitations related with low product diversification, insufficiently trained employees at the bank, and the need to develop an effective outreach to the people recognized in official documents. policy applications and market holes are analysed in bangladesh institute of development studies (bids, 2020) with a conclusion that the ongoing and highly effective structure implemented by governmental and regulatory organisations is often sluggish in its implementation as the sector is not yet ready and looks upon the lack of collaboration between the private and governmental organisations. green finance is the subject of attention by ifc (2020) and the world bank (2021) as the source of financing that can help in aligning the growth of emerging economies, including bangladesh, with the goals of a sustainable economy. the two organisations promote continued innovation and investment in order to make use of the cash available in the private sphere and gain substantial advantages of climate adaptation. collectively, the studies show that although the policy thus far is improving, not to mention the established capability to acknowledge the value of innovation in furthering green finance, major loopholes continues to exist in terms of product development, client engagement, and technological incorporation. research gaps and synthesis as a whole, the corpus of the reviewed literature forms an all-encompassing background of knowledge regarding the global and bangladesh-related concerns and possibilities related to the topic of green finance. still, some important gaps appear. not much empirical research has been done on the efficacy of policy tools and the effect of novel green products on the investment behaviour and sustainability outcomes. there is not much knowledge regarding how customers perceive and how aware they are when deciding to use a product. additionally, there is a pervasive need for developing a better understanding of the role of fintech and emerging technology to developing green finance. most of the studies emphasize the supply side, not enough on the demand side or customers’ behavior. little is known about the financial or regulatory incentives to transact green financial products, and this is particularly true when it comes to green products for people in bangladesh. because of such rapid change in the rules and the technology, there is a critical need for deep descriptive goal-independent research led by comparison between sectors that can guide effective policy and practice. materials and methods this thesis is using a convergent parallel mixed methods research design to produce a holistic and comprehensive examination of green finance innovation in bangladesh and the role it plays on advancement of customer awareness and sustainable investment choices. by using both quantitative and qualitative methods, the methodology allows triangulation between different point of views. the primary focus is on formal financial institutions in bangladesh, recognized as the main channels for green finance products and policy implementation. the quantitative survey involved 250 respondents, selected using random sampling to ensure representation across age, gender, occupation, and education groups. for qualitative insights, semi-structured interviews were conducted with key informants, including green finance professionals and officers working in banks, regulatory authorities such as bangladesh bank, and relevant development partners or policy think tanks involved in fostering sustainable finance. data collection the main quantitative instrument for data collection will be a structured questionnaire focusing on bankers and financial professionals all over bangladesh. this section measured the key research variables using likert-scale statements (1 = strongly disagree to 5 = strongly agree). the distribution of items was as follows 1. section 1 (demographic information) – 4 items 2. section 2 (questions on dependent variable) –1 items 3. section 3 (questions on independent variable)5 items 4. section 4: (adoption and challenges of green finance)6 items 5. section 5: (policy recommendations)1 item in addition to data collection on a quantitative level, in-depth semi-structured interviews will be conducted with a selected sample of experts in the green finance industry, in order to capture a rich qualitative data. this will include the senior bankers, practitioners in green finance, and the appropriate policy makers. the interview protocol should be semi-structured, at a minimum, so that probing as well as follow-up assessment questions can be asked based on responses received across areas of interest in the interview. experiences in implementing pa ge 19 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 green finance, barriers and opportunities, regulatory and market challenges, the role of fintech and suggestions on how to move the sector forward will be discussed. interviews will be audio-recorded (with permission), transcribed and analysed thematically to identify common and unique themes. the study will draw its theoretical background from an extensive review of scientific articles in peer-reviewed academic journals and scholarly books. in addition, it will analyze relevant official reports and policy documents from government and regulatory bodies such as bangladesh bank, bids, and pertinent ministries, as well as national and international guidelines. furthermore, technical reports and papers published by organizations like the world bank, ifc, adb, gcf, and other national and international institutions will be examined to understand and position bangladesh’s green finance practices within an international context. data analysis multiple linear regression (mlr) will be used to analyze the relationship between several variables (ivs) and a dependent variable (dv) related to green finance adoption. the regression model will help identify the factors that significantly influence the adoption of green finance products in bangladesh. a) dependent variable (dv): i. adoption of green finance products b) independent variables (ivs) i. readiness of the market for green finance ii. regulatory barriers limiting green finance adoption iii. fintech’s role in green finance adoption iv. financial incentives for adopting green finance v. limited availability of green finance products vi. high cost and financial constraints the multiple linear regression (mlr) model will be constructed as follows: y = β₀ + β₁(awareness) + β₂(perceived benefits) + β₃(regulatory barriers) + β₄(market readiness) + β₅(fintech role) + ε where, y represents the adoption of green finance products (this is the dependent variable or dv). β₀ is the intercept, which is the value of y when all independent variables are equal to zero. β₁, β₂, ..., β₅ are the coefficients that represent the relationship between the independent variables (awareness, perceived benefits, regulatory barriers, market readiness, and the role of fintech) and the dependent variable (adoption of green finance products). these coefficients indicate how much the dependent variable (y) is expected to change when the corresponding independent variable changes by one unit. in this research multiple linear regression (ml) was used to know the relationship between independent variables and the dependent variable. for instance, does awareness of green finance dominate over market readiness for adoption, or perceived benefits based on the relationship, the regression model will enumerate the independent variables against the dependent variable and we will know which factors contribute to increased or decreased adoption of green finance and how much each factor is responsible for it. results and discussion the results are organized according to the mixed methods approach outlined in the methodology, beginning with quantitative survey analysis, followed by qualitative insights from semi-structured interviews, thematic synthesis, integration with existing literature, and concluding with implications for policy and practice. frequency responses the majority of respondents (79.6%) are aged 26–35 years, with smaller proportions in the 36–45 years (12.4%) and 18–25 years (7.6%) groups, and minimal representation above 45 years. gender distribution is notably imbalanced, with 85.2% male and 14.8% female participants, indicating potential sampling bias or a maledominated target group. regarding education, most respondents are highly qualified: 58% hold a master’s degree, 26.4% a bachelor’s degree, while smaller shares have hsc (9.2%), ssc (5.2%), and phd/mphil (1.2%) qualifications. occupationally, investors or potential investors form the largest group (58.8%), followed by bankers (14%), business owners (11.2%), researchers (7.6%), policymakers (5.6%), and green finance experts (2.8%), providing a diverse professional mix relevant to green finance adoption. figure 1: pie chart showing age distribution pa ge 19 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 figure 2: pie chart showing gender distribution figure 3: pie chart showing educational qualification distribution figure 4: pie chart showing occupation of the respondent table 1: showing the descriptive statistics of the variables. variable mean standard deviation n adoption of green finance products in the future 3.64 1.157 250 readiness of the market for green finance 3.23 1.079 250 regulatory barriers limiting green finance adoption 3.14 0.904 250 fintech's role in green finance adoption 3.64 0.705 250 financial incentives for adopting green finance 3.27 1.385 250 limited availability of green finance products 2.68 1.330 250 high cost and financial constraints 3.14 1.451 250 descriptive and inferential statistics the following table summarizes the descriptive statistics for the key variables: the average perception of respondents to factors like green finance product adoption, market readiness, and fintech roles, etc., is captured in the mean values. for instance, both “adoption of green finance products in the future” and “fintech’s role in green finance adoption” mean are moderately positive with high means (3.64) whereas “limited availability of green finance products” have a mean of 2.68 is comparatively more negative. these standard deviations demonstrate the extent of variability associated in responses, a higher standard deviation indicating more differing responses. in summary, the data shines a light on a dualism of optimism for future adoption with fintech’s role in the market alongside concerns around market maturity & response, regulations, and the lack of supply of green finance products. correlation analysis the correlation analysis explores the relationships between the adoption of green finance products and other variables. key correlations include: table 2: showing correlation analysis among the variables variable adoption of green finance market readiness regulatory barriers fintech’s role financial incentives limited availability of products adoption of green finance 1.000 0.040 0.127 0.051 0.106 0.103 market readiness 0.040 1.000 0.087 0.167 0.149 0.127 regulatory barriers 0.127 0.087 1.000 0.191 -0.164 0.160 fintech’s role 0.051 0.167 0.191 1.000 0.082 -0.164 financial incentives 0.106 0.149 -0.164 0.082 1.000 0.097 pa ge 19 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 table 3: mlr model summary model r r² adjusted r² standard error of estimate 1 0.241 0.058 0.035 1.137 the correlations show weak to moderate relationships among most of the variables, with the highest correlation being between high cost and financial constraints and limited availability of green finance products (0.466), to summarise although there is some relationship between the data, there is not a strong linear dependency in most of them. high cost and financial constraints are negatively correlated with other variables (adoption of green finance and market readiness) which suggests that financial constraints will not only be difficult for the adoption but may also hinder market development. multiple linear regression model summary the regression analysis was conducted with the following model summary: anova the anova results indicate the overall significance of the model: the f-statistic and p-value were 2.497 and 0.023 (p < 0.05), limited availability of green finance products 0.103 0.127 0.160 -0.164 0.097 1.000 high cost and financial constraints -0.114 -0.218 -0.164 -0.005 0.097 0.466 table 4: anova model sum of squares df mean square f sig. regression 19.360 6 3.227 2.497 0.023** residual 313.956 243 1.292 total 333.316 249 indicating that the model is statistically significant, which means that there were some predictor variables which have a relationship with the dependent variable, green finance adoption. the r2 value was 0.058 with a small amount of variance explained at 5.8%, although this is a good result because it shows that factors are important to consider in adoption even for extremely small r2 values. importantly, the significance of the key predictors in the model (regulatory barriers and financial incentives) is statistically significant which implies that in order to support the green finance, it is important to improve the regulatory barriers and provide financial incentives. in sum, the model reflects some of the important dynamics of green finance adoption and can be used as a starting point for further refinement of the approach and for incorporating other drivers. t-test results (coefficients table) the coefficients for the regression model are presented below: table 5: t test result analysis predictor b std. error beta t sig. (constant) 2.556 0.581 4.396 0.000 market readiness -0.044 0.071 -0.041 -0.616 0.539 regulatory barriers 0.174 0.083 0.136 2.103 0.037* fintech's role 0.078 0.108 0.048 0.723 0.470 financial incentives 0.143 0.055 0.171 2.574 0.011* limited availability of green finance products 0.084 0.060 0.097 1.402 0.162 high cost and financial constraints -0.094 0.053 -0.118 -1.757 0.080 *regulatory barriers (b = 0.174, p = 0.037), showing that addressing regulatory barriers can positively affect green finance adoption. * financial incentives (b = 0.143, p = 0.011), suggesting that offering financial incentives can significantly encourage adoption. other predictors, such as market readiness (p = 0.539), fintech’s role (p = 0.470), and limited availability of green finance products (p = 0.162), were not statistically significant in this mode pa ge 19 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 thematic content analysis the thematic content analysis of interviews with green finance professionals revealed four key themes outlining obstacles and opportunities for green finance in bangladesh. regulatory barriers interviewees highlighted inconsistencies in policy enforcement, frequent regulatory changes, and lack of standardized guidelines as major hurdles. a unified green finance taxonomy was seen as beneficial for product design and reporting. regulatory uncertainty was noted to undermine financial institutions’ confidence to innovate or expand green finance offerings. market readiness commitment in large scale is increasing but varies for green finance. larger banks have better green finance capacity while smaller banks and non-bank institutions are restricted by high transaction costs, approval delays and assessing environmental risk; the latter has a disproportionate impact on smaller lenders and rural beneficiaries. customer awareness limited awareness on the part of individual and corporate customers especially smes and rural entrepreneurs is a major constraint towards adoption. in terms of real-world applications, we suggested targeted awareness-raising and training activities to demonstrate the usefulness and the practical value of green finance products. role of green finance fintech participants recognised digital technology such as blockchain, data analytics and digital platforms as critical to lower costs, increase transparency and speed up green finance transactions. despite the strong uptake by a number of mainstream banks, fintech adoption in general is still very limited, requiring further investment and a supportive regulatory framework. in conclusion, the analysis anticipates that addressing regulatory uncertainties, market-readiness measures, client awareness-building, and underlying digital innovation are critical steps to drive green finance adoption in bangladesh to achieve sustainable development goals. integration and triangulation of findings the methodology in this paper adopts a multi-level approach and uses both quantitative and qualitative research techniques to reveal the drivers and the barriers and opportunities underpinning the emergence of green finance in bangladesh. qualitative data collected from semi-structured interviews is instrumental in this sense as it reveals the background and introduces the perspectives of the participants which help explain trends drawn from the statistical information analysis. the survey and interviews both illustrated that, both respondents and experts are of the opinion that harmonisation and regulatory consistency is key for financial institutions to be able to test innovative solutions with confidence. there was widespread recognition that novel green financial products, such as green bonds and sustainability linked loans, as well as efforts to diversify the range of products available, are needed to respond to the market need. difficulty in building industry capacity was cited as one of the most significant obstacles and examples included the general lack of knowledge among clients and limited capabilities and familiarity among financial professionals, which would require specific outreach and training to bridge the gap. while quantitative findings showed only a little statistical interaction between financial technology ventures and green digital solutions adoption, qualitative data suggested such technological innovation is beginning to yield more efficient and cost-effective processes for large banks, and thus means fintech will become increasingly influential. conclusion the paper analysed the opportunities and challenges for green finance acceleration in climate vulnerable bangladesh. factors cited as mandating contributing are standardization and financial incentives, however, other gaps were found. policies were not implemented that were tough, consistent, and enforceable. lack of knowledge, weak institutions and limited use of fintech further retarded progress. by identifying the key factors to local decision making, the research very clearly illustrated how regulation can be better tuned to banks, more rewarding, and more inclusive. the results underline the critical need for enforceable real world regulations, coupled with training and new digital tools to educate the public. bangladesh needs to transition to a green economy by focusing on environmentally inimical, low emission practices through change in people behavior and industry. future studies should also consider human behavior and sector-specific habits for the best optimization of policies for sustainability. recommendations in this paper, we put forward measures on how green finance can be increased in bangladesh. first, clear rules that promote new concepts in banks and other financial institutions. the rules must be practical and be an impetus for constant recalculation. second, grant rewards to ecofriendly projects in the form of low interest rate loans, tax breaks, and prizes to banks and lenders. provide special support for small banks and non-traditional lenders which are issuing green bonds and sdg-linked loans. educate the public and the finance institution with awareness programs. third, promote green finance friendly fintech by providing testing grounds for innovation. at the same time, consider the use of electronic tools such as blockchain, data analysis, and new models of lending to make financial services more accessible, transparent and useful for non-wealthy communities. pa ge 19 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 188-195, 2025 finally, enhance cross-organization co-ordination through a central database to monitor investments and impact on environment using a common process. increase public private cooperation and a common global outlook to deliver new sustainable funds where it is most needed. references ahmad, f., zayed, n. m., & harun, m. a. (2013). factors behind the adoption of green banking by bangladeshi commercial banks. american journal of environmental economics, 4(1), 32–42. https://journals.e-palli.com/ home/index.php/ajee/article/view/3670 bangladesh bank. (2011). green banking guidelines. bangladesh bank. bangladesh bank. (2020). sustainable finance policy for banks and financial institutions. bangladesh bank. bangladesh bank. (2023). annual report on sustainable finance and green banking in bangladesh. bangladesh bank. bangladesh institute of development studies (bids). (2020). policy gaps and market development needs for sustainable finance in bangladesh. bids. begum, m., sultana, s., & ahmed, f. (2021). green banking practices in bangladesh: challenges and prospects. journal of banking and financial services, 15(2), 45–60. international finance corporation. (2020). green finance: a bottom-up approach to sustainable investment. ifc. khan, m. r., & hossain, m. s. (2019). prospects of fintech in promoting green finance in bangladesh. asian journal of finance & accounting, 11(2), 85–102. miah, m., uddin, s., & rahman, t. (2019). digital innovation in banking and its role in green finance adoption: bangladesh perspective. international journal of business and management, 14(7), 99–112. rahman, m., ahmed, s., & chowdhury, t. (2022). barriers to green finance adoption: evidence from bangladesh. sustainable finance and investment journal, 12(4), 321–339. rahman, s., malik, m., & sharma, a. (2021). green banking initiatives and customer satisfaction: a comparative study of kerala and indian banking sectors. american journal of environmental economics, 4(1), 15–31. https://journals.e-palli.com/home/ index.php/ajebi/article/view/3679 sustainable finance forum. (2021). closing the gap: enhancing client awareness and institutional capacity in reen finance. sustainable finance forum & bangladesh bank. world bank. (2021). financing a green future: scaling up sustainable finance in emerging markets. world bank. pa ge 1 pa ge 15 5 american journal of financial technology and innovation (ajfti) entrepreneurial growth under financial inclusion: a firm-level analysis of microenterprises in abeokuta metropolis kamilu a. saka1*, rasaki a, raji2 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4584 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 12, 2025 accepted: march 19, 2025 published: october 25, 2025 this study applies the two-stage least squares (2sls) estimator to analyse the impact of financial inclusion on micro-firms’ employment growth in the abeokuta metropolis. a survey research design was employed to randomly select 384 owners and managers of microenterprises in the study area. the traditional ordinary least squares (ols) produces underestimated and overestimated impacts of financial inclusion indicators on firm employment growth due to endogeneity issues with the predictors. however, the observed 2sls estimation outcomes show that when the financial inclusion of a microenterprise is instrumented with education level, efficient and consistent estimates are obtained. from the 2sls analysis, a one percent increase in access to and availability of the formal financial system leads to a corresponding 1.13 percent and 1.76 percent decrease in the employment growth of the sampled firms at two significance levels (5% and 10%), respectively. the study then affirms that high access to and availability of formal financial products and services significantly slow down employment growth among microenterprises in the study area. these results imply that increasing levels of financial inclusion are not palatable for microfirms in the study area due to the significant financial challenges they face. it is recommended that bespoke formal financial products and services be provided for micro-firms, including addressing the daunting financial challenges these firms face. keywords 2sls, employment growth, financial inclusion, instrumental variable, micro-firm introduction the importance of financial inclusion is increasingly recognized among governments, and financial institutions, including international financial and development organizations like the international monetary fund (imf), world bank, african development bank, and others. with financial inclusion (fi), more previously financially excluded people and businesses can access and use financial products and services at affordable prices. academics and industry stakeholders argue that fi offers gains to firms by decreasing liquidity or financial constraints, increasing investments and ensuring higher firm growth (bricknell & kertay, 2024; nizam et al., 2020; chauvet & jacolin, 2017). broadly, at the macro level, fi is believed to enhance employment generation, poverty reduction, contribute to economic growth and promote income redistribution (anastesia et al., 2020; park & mercado, 2018b; omar & inaba, 2020). despite wide recognition and importance of financial inclusion to all economic agents including national economy micro, small scale enterprises, and smallholder farmers still face acute credit constraints in developing economies (bricknell & kertay, 2024; global findex, 2021; african development bank, 2019; chandio & jiang, 2018; international monetary fund, 2020; osabohien et al., 2020c; mayorga et al., 2024; world bank, 2020). even in rural areas of developed countries or among low-income-oriented micro businesses in these economies, the credit gap is still an issue. in particular, credit gap financing is more evident in nigeria where a large number of micro and small-scale enterprises record a lack of access to investible funds as a major business obstacle in the country (anga et al., 2021; anastesia et al., 2020; ogidi & pam, 2021; agbim, 2020). meanwhile, the credit financing gap that impedes a sustainable level of entrepreneurship and small-scale enterprises’ growth and development in sub-saharan africa is a major financial inclusion concern in these countries, particularly economies where abject poverty is prominent (triki & faye, 2013). in nigeria, previous studies that focus on the relationship between financial inclusion and entrepreneurship growth and development (anga et al., 2021; anastesia et al., 2020; ogidi & pam, 2021; ibekwe et al., 2021; anisiuba et al., 2020) observed positive relationship between financial inclusion and entrepreneurial growth in the country. however, these previous studies suffer from certain empirical strategy flaws and weak measurements of variables, particularly regarding financial inclusion. for instance, given the data time, this study argues that macrolevel data employed by anga et al. (2021); anastesia et al. (2020); ibekwe et al. (2021), and anisiuba et al. (2020) for financial inclusion analysis for microenterprises is a misplaced priority and purely an empirical flaw. this is because a study establishing the macroeconomic impact of financial inclusion needs sufficient long-time-series data on financial inclusion measures (demirguc-kunt et al., 2017). again, using total trade sector output by anastesia et al. (2020) to measure retail and wholesale productivity as a proxy for entrepreneurial growth is 1 department of banking and finance, the federal polytechnic, ilaro, nigeria 2 department of business administration and management, the federal polytechnic, ilaro, nigeria * corresponding author’s e-mail: kamilu.saka@federalpolyilaro.edu.ng pa ge 15 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 155-161, 2025 less reliable for standard research that aims to establish the real impacts of financial inclusion on micro-firm growth. this poor measurement of variables could lead to wrong policy formulation on developing microfirm productivity through financial inclusion. this study employs an instrumental variable (iv) regression estimator to correct the endogeneity issue which has been grossly overlooked by most previous studies in the field. this study conducted in abeokuta, a metropolitan city in ogun state, nigeria is organised as follows. the first section introduces the background, the research problem, the main issue with the previous works, and the context for the study. in the second section, a review of the literature was conducted. this section is followed by a discussion of the methods adopted by the research in section three. section four presents the results, interprets, and discusses the obtained findings from data analysis. the study concludes in section five and ends with recommendations. literature review the issue of financial inclusion is more important to micro and small enterprises and entrepreneurship at large. the reason is that most poor people or less privileged adults often engage in these kinds of businesses. thus, improved access to an array of savings and risk mitigation products can help these theoretically financially excluded segments to be more active economically and contribute significantly to the economic growth of a country (triki & faye, 2013). an entrepreneur is a person with the ability to transform his/ her potential into the creation of business opportunities to earn a living while his/her productive effort is regarded as entrepreneurship. by and large, entrepreneurship is viewed in this study as those operated by microenterprise operators. in this manner, microenterprises are defined here as firms that employ not more than five (5) employees. this approach is similar to the business classification method by the world bank (2014). in nigeria, some strategies for financial inclusion have been designed by regulatory authorities, development organisations, and non-governmental organisations (ngos) to promote and extend financial products and services to poor people, entrepreneurs, and micro and small-scale enterprises. according to central bank of nigeria (2012), these strategies include bank verification number (bvn), digital financial services (dfs) initiatives, agent banking framework, national collateral registry, know your customer (kyc) framework, micro, small and medium enterprises development fund (a sum of ₦220 billion), financial literacy and capital market literacy, a 5-year financial inclusion targets for commercial banks (2016 – 2020), innovative insurance, and the passage of pension reform act 2014 premised on the belief that millions of nigerians in the informal sector will be financially included through pension contribution and investment. further, the seemingly numerous literature on financial inclusion measurement is yet to recommend a standard or comprehensive measurement approach for financial inclusion. it has been argued over time that such a standard measure is required to account for the coverage of financial inclusion and monitor the progress of policies on financial inclusion at both macro and micro levels. even with few attempts that have been made to the development of financial inclusion measures efforts have largely been made at the macro level for cross-country comparisons (amidzic et al., 2014; camara & tuesta, 2014; sarma, 2008; sarma, 2010; sarma, 2012; sarma & pais, 2011; omar & inaba, 2020) and for regions in brazil (banco central do brasil, 2011) or countryfocused analysis (anastesia et al., 2024; mayorga et al., 2024). the most intuitive idea on the financial inclusion measurement is provided by sarma (2012). sarma proposed that financial inclusion can be better understood if viewed with a multidimensional lens by taking into consideration three important dimensions of financial inclusion. to achieve his objective, sarma developed the composite financial inclusion index (cfi). the cfi index by sarma outlined these three important dimensions of financial inclusion. these include accessibility, availability and usage of financial services by members of a country’s population. the supply-leading hypothesis (slh) is the theory underpinning the current study. slh was pioneered by schumpeter (1911) and derived more recognition from the works of mckinnon (1973) and shaw (1973) who empirically confirmed a link between finance and economic growth. according to the principle of slh, the economic growth of a country is facilitated by the level of financial development due to its role in the real sector. this implies that high financial development enhances or promotes the growth of an economy. at the micro level, greater financial inclusion of microenterprises would contribute to developing a country’s formal financial sector and consequently improve the productivity and growth of micro and small businesses. when a country’s financial system deepens, the supply of financial products and services will also increase. thus, better accessibility of formal financial products and services by underserved and excluded segments of the informal sector is critical to promoting micro-firm productivity in developing countries like nigeria. empirically, many studies have been conducted on the direction of the relationship between financial inclusion and entrepreneurship growth in different continents and across countries. from latin america, mayorga et al. (2024) employed panel model regression to analyse longitudinal data drawn from 21,825 columbian manufacturing microenterprises on the relationship between financial inclusion and firm growth. the study found that financial inclusion positively and significantly influences micro firms’ growth in columbia. in the middle east, zreik, marzuki, and iqbal (2023) used a qualitative approach to analyse the effects of microfinance on chinese small business owners and discovered that microcredit is very important for supporting micro-business owners in marginalized chinese communities. the research study highlights the significance of financial inclusion through pa ge 15 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 155-161, 2025 microfinance to small business owners in china. in africa, bricknella and kertay (2024) employed content analysis to analyse primary data obtained through focus group discussions and in-depth interviews on the impact of financial inclusion on entrepreneurship in south africa. the study establishes that greater accessibility of financial services promotes entrepreneurship activities in the study area. the work of bricknella and kertay reinforces the evidence provided by anthanasius-fomum and pieter (2023) who found through quantile and ordered logit model regressions that financial inclusion when measured by access to financial services and savings practices has positive and noticeable impacts on the annual profits of micro firms in eswatini. in the context of nigerian microenterprise, anga et al. (2021) uncovered through error correction model (ecm) analysis of time series data that there is a positive relationship between financial inclusion and the performance of the sme sector. this research study implies that financial inclusion promotes entrepreneurship in nigeria. however, such evidence is macro-data based and may not accurately reflect real financial inclusion at the micro level. the study by anastesia et al. (2020) also used macro secondary data and the ecm approach to estimate the effects of financial inclusion on entrepreneurship growth in nigeria and found that commercial bank branches have positive and significant consequences for the output growth of retail and wholesale subsectors in nigeria. however, even though education (e.g. financial education) tends to influence the economic behaviour of potential credit users to access and use formal services as established by mayorga et al. (2024) no study from available literature has treated education as a possible cause of endogeneity issue that can lead to misleading and biased result between financial inclusion and development of micro-businesses particularly in developing countries like nigeria. consequent to the development, this study considers the possible incidence of endogeneity concern in the statistical estimation process using an iv estimator. again, this study attempts to fill the void in most past nigerian studies on macro data usage. as stated earlier, macro data employed by anga et al. (2021); anastesia et al. (2020); ibekwe et al. (2021), and anisiuba et al. (2020) for financial inclusion analysis for microenterprises is a misplaced priority and purely an empirical flaw. as a result, this study uses primary data via a structured questionnaire to capture financial inclusion and growth of entrepreneurship at the micro level. to overcome the entrepreneurship growth measurement issue, this study follows the approach of fowowe (2017) which had earlier been employed by dinh et al (2012), and aterido et al (2011) to measure microenterprise growth by using a three-year employment growth variable before the current research survey year. materials and methods this study causal effect of financial inclusion on entrepreneurship growth specifically microenterprises growth in nigeria within the supply leading hypothesis (slh) framework. the study adopts a survey research design. this research design allows a researcher to gather opinions and perceptions of units of analysis on certain measured variables of interest in a research study. the study area in focus is abeokuta metropolis. the study city, abeokuta, is the capital of ogun state, nigeria. the ancient city is a commercial centre with a large number of microenterprises including both formal and informal small outlets. relatively, this study specifically focuses on formal microenterprises in the study area. in this study, formal microenterprises in the selected environment are those little capital-based (between 1 naira and 5 million naira) businesses registered with the corporate affairs commission (cac) and obtained business certificates as evidence. in other words, the total number of all formal microenterprises in the study area represents the population. however, in terms of the figure, the population size is unknown and infinite. this situation led to the researchers’ decision to use a sample size formula for an unknown population size as recommended by krejcie and morgan (1970). consequently, a sample size of 384 microenterprises was determined. these microenterprises are represented by firm owners or managers where appropriate. the study uses a systematic random sampling technique to select every fifth microenterprise approach during the observational data collection process. in line with the study’s goal, an empirical financial inclusioninduced growth model based on the supply-leading hypothesis is stated below. emgi= α+ β1 fici+ εi ………….(2) where; emg= employment growth; emgi= (emgn-emg(n-3))/emgn; emgn= firm employment level three years ago (2021) and emg(n-3)= firm employment level in the current year (2024) α = model intercept; fic = financial inclusion; β1 = slope coefficient of fic; ε = error term; i = individual microentrepreneur. furthermore, relying on three dimensions of financial inclusion as previously established in the literature review section equation (2) can be expanded in equation (3) as: emgi= α+ β2acci+β3avli+β4fagi+εi ………(3) where; acc= accessibility (financial inclusion dimension); avl= availability (financial inclusion dimension); fag= firm age (control variable); β2β4 are slope coefficients of the three dimensions of financial inclusion and β2 = slope coefficient of control variable. consequent upon equation (3), the study adopts educational level as an appropriate instrumental variable pa ge 15 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 155-161, 2025 to correct for possible endogeneity issues in an expanded equation (3). without this practice, all slope coefficients in equation (3) will yield biased and unrealistic estimates as there is the possibility of a correlation between the predictor (financial inclusion) and education level as part of the residuals in equation (3). hence, equation (4) is instrumented as: emg i=α+β 5acci+β 6avli+β 7fag i+β 8edli+β 9 fagi+εi ………….(4) where edl = education level (the instrumental variable) the instrumented equation (4) with educational level is intuitive for two reasons. first, it corrects the endogeneity issue in financial inclusion as the main predictor of the study through the instrumental variable (edl). second, the equation accounts for all covariates that would have obscured the causal relationship between financial inclusion and the growth of microenterprises in the study area. furthermore, the study adopts a two-stage least squares method (2sls) to estimate instrumental variables in equation (4). the first stage is to clean up by regressing each of the endogenous variables in equation (4) on other predictors, the instrumental variable (edl) and the control variable (fag) being the study covariate. the procedures are contained in the following equations 5 to 6. acci= α +β10avli+β11edli+β12fagi+εi …(5) avli= α + β13acci+ β14edli+ β15fagi + εi …(6) the econometric estimations of equations 5 and 6 yielded predicted values of acc and avl (acc) and (avl) which represent the exogenous parts of acc and avl, respectively. in the second stage, the researchers replace acc and avl as the main dimensions of financial inclusion in equation 3 (ols model) with (acc) and (avl) to estimate equation 8 (instrumental variable model). emgi = α + π(acc)i + (ρavl)i + β16fagi + εi …(8) the researchers determine if endogeneity occurs in realistic terms by comparing β2,β3,and β4 in equation 3 with π and ρ (causal effects) in equation 8. this comparison is conducted through the hausman test (hausman, 1978). all statistical estimations are performed at three levels of significance (1%, 5% and 10%) with empirical expectation that β2, β3, β4, π, ρ, and δ yield positive coefficients. results and discussions presentation of results this sub-section showcases the estimation outcomes of instrumental regression. information in table 1 presents inferential estimations of the study through traditional ols and two-stage regression (iv) analyses. table 1: two-stage least squares regression (instrumental variable) estimation predictor / statistic traditional ols (dv: emg) second-stage ols regression dv: acc dv: avl dv: emg acc 0.17** (0.08) -0.12*** (0.04) avl -0.70*** (0.12) -0.24*** (0.08) fag 2 -1.12*** (0.20) 0.91*** (0.11) 0.81*** (0.08) -0.06 (0.37) 3 -1.96*** (0.36) 0.82*** (0.22) 0.69*** (0.15) -4.48*** (0.58) edl 0.19*** (0.04) 0.09*** (0.03) constant 1.24*** (0.11) -0.78*** (0.12) 0.63*** (0.08) 0.81*** (0.19) (acc) -1.13*** (0.27) (avl) -1.76*** (0.27) no. of obs. 342 342 342 342 model fitness f(5, 336) 34.54*** 35.68*** 33.90*** 55.33*** r-squared 0.34 0.35 0.34 0.44 endogeneity test durbin (score) chi2(1) 61.41*** 49.47*** wu-hausman f(1, 336) 73.53*** 56.82*** instrument validity f(1, 337) 20.68*** 10.20*** pa ge 15 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 155-161, 2025 interpretation and discussion of results furthermore, table 3 reveals the outcome of the 2sls estimations performed with 342 observations after system adjustments. at the initial stage, traditional ols was performed to estimate equation 3 on the impacts of financial inclusion on employment growth in microenterprises that ply their trade in the abeokuta metropolis. the results show that acc (accessibility) positively and significantly impacts the employment growth of micro-firms in the study area. that is, a 1% increase in access to formal financial services and usage increases micro-firm employment growth by 0.17% and 0.66% respectively at three significance levels. again, the positive impact of the accessibility (acc) is significant at two significance levels (5% and 10%) among the selected microenterprises is obtained at all three significance levels. in contrast, the standard ols estimation shows that the availability (avl) of formal financial services has a negative but significant impact on the micro-firm employment growth (emg) at the three significance levels. the result indicates that when avl increases by 1% the employment growth of the study area microfirms decreases by 70 per cent. firm age (fag) with three sub-indicators (new firm, growing or matured firm) is used as a control variable in equation 3 and table 1 reveals that in the traditional ols estimation, new firms (reference category coded “1”) significantly employ more workers during the research period than the growing firm (coded “2”) and mature firm (coded as “3”) by 1.12% and 1.96% respectively. one intriguing outcome of the estimated traditional ols is the high magnitude of the impacts of financial inclusion measures on firm employment growth. this incidence may be associated with a lack of adequate information from observational data on the true exogeneity of conditions of interest (financial inclusion). in other words, if caution is not taken, this result may mislead the policymakers about the real effects of financial inclusion on microenterprises growth in nigeria using the study area as the frontier. therefore, an efficient estimation is required for informed policy-making decisions on financial inclusion among micro-firms in nigeria via the use of education as an instrumental variable. the analysis is critical to ascertain if financial inclusion measures (accessibility, availability and usage of formal financial services) used by the study have high correlations with unobserved factors (error term) – an incidence of endogeneity. consequently, the study uses “education” as an instrumental variable in equations 5 and 6 where each predictor serves as an outcome variable explained by a set of other predictors, instrumental variable (education) and the control variable (firm age). these analyses represent the first stage of the 2sls estimation. however, the significant results of durbin (score) and wu-hausman in columns 3 and 4 of table 1 confirm that the relationship between financial inclusion and employment growth of microenterprises in the study area is endogenous. this indicates a high correlation between financial inclusion and error terms in equations 3-5 specified in the methodology section. however, the instrumental validity tests show that the instrument used in each of the two equations under the first-stage estimation is valid in two cases as f-statistic values are greater than 10 being the threshold. thus, the 2sls technique will produce more efficient and consistent estimates than the traditional ols. therefore, financial inclusion measures were then instrumented and new predicted values were created through the first stage. subsequently, the predicted scores are then used to represent scores for the predictors in the second stage of the 2sls procedure. from the second stage estimation, accessibility (acc hat) of formal financial services is observed as a negative but significant predictor of growth in the worker additional employment by microenterprises in the study area. by implication, a 1% increase in access to the formal financial system will significantly lead to a corresponding 1.13% decrease in the employment growth of the sampled firms at three significance levels (1%, 5% and 10% respectively). the observed significant negative result contradicts the positive result obtained on accessibility under the traditional ols. again, it is shown in column 5 of table 1 that the result obtained earlier from the classical ols is overestimated. thus, the standard ols method yields misleading results on the impact of micro-firm workers’ employment on access to formal financial services in the study area. similarly, the second stage result in column 5 of table 1 further reveals that the predicted score of formal financial services and products for availability produces a negative but significant impact on the worker employment growth among the population of microenterprises in the study area. it shows that a 1% increase in the rate of availability of formal financial services and products will significantly cause a 1.76% decrease in the employment growth of micro-firms in the abeokuta metropolis. however, when compared, the result confirms that standard ols produces an underestimated result on the true impact of formal financial system services availability on employment growth among micro-businesses in the study area. this evidence also corroborates biased and inconsistent estimation by the traditional ols and thus buttresses the relevance of the 2sls approach used by the current study. the test of overall model significance notes: (1) variables such as acc (accessibility) and avl (availability) are the endogenous predictors; (2) fag (firm age) as a categorical control variable (covariate)is measured on three levels with “young firm” ((less than 3 years) coded as 1 (the reference group), “mature firms” (above 3 years to 6 years) coded as 2 and “senior firms” (7-10 years) coded third category; (3) standard errors are contained in the parentheses; (4) significance levels are represented ***(1%), **(5%) and *(10%) respectively. source: authors’ computations from stata 12.1 outputs (2025) pa ge 16 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 155-161, 2025 shows in table 1 that the 2sls outperforms the standard ols approach. again, in terms of r-squared comparison, financial inclusion from the 2sls model (second stage) explains 55.33% of the total variance in worker employment growth among microenterprises in the study area, whereas in the standard ols, the total amount of variation explained was 34 per cent. therefore, using 2sls in this study is deemed appropriate and efficient. in the real sense, the 2sls estimation outcomes imply that accessibility and availability of formal financial services are negative but significant predictors of growth in the additional employment by microenterprises in the study area. this result is surprising and inconsistent with the study’s prior expectations. this is because the study’s theoretical foundation (supply-leading hypothesis) attests to the positive contribution of financial development to the growth of an entity’s economy. more so, such evidence contradicts previous findings by bricknella and kertay (2024); mayorga et al. (2024); zreik et al. (2023); anga et al. (2021); and anastesia et al. (2020); however, this unexpected result might not be unconnected with the likely insufficient account balance that cannot sustain a firm’s operations for a long period or limits its expansion. firms with enough money in the bank account or any financial institution account tend to desire expansion and modernization of operations that can ultimately lead to the employment of marginal workers. thus, an increase in the number of accounts with different financial institutions without adequate cash balance or access to multiple loans may drain meagre financial resources of micro-firms and lead to loss of workers as salaries may not be sustained over time. again, numerous challenges often experienced by microenterprises in obtaining finance may prevent them from benefiting from the higher availability of formal financial products and services. when these financial challenges faced by micro-firms are not effectively addressed access to more available formal financial services may also drain their resources as many providers can request commitment balance in accounts owned by these micro-firms. more importantly, the failure of previous studies to instrument financial inclusion variables with user education in their respective estimations could have also contributed to the observed different results. however, state-wide or national datasets can be used to confirm these observed results among microenterprises at the state or national level. conclusion this study uses a 2sls regression estimator to examine 342 cross-sectional data on the relationship between financial inclusion and firm employment growth among microenterprises in the abeokuta metropolis. the estimation results show that, in the absence of education as an instrument variable, a high correlation occurs between financial inclusion and other extraneous variables (captured by the stochastic term) if standard ols is only applied. based on the factual evidence from 2sls analysis, this study affirms that high access to and availability of formal financial products and services significantly slow down employment growth among microenterprises in the study area. the methodological implication of this study is that efficient and consistent estimates of the relationship between financial inclusion and firm job growth are guaranteed when instrumental variable (iv) regression or 2sls is considered. again, this study provides a better opportunity for formal financial services to gain significant empirical insight into the appropriate financial inclusion dimensions that facilitate employment creation among micro-firms. it is recommended that bespoke formal financial products and services be provided for micro-firms, including addressing the daunting financial challenges these firms face. acknowledgement this work is supported by the nigeria tertiary education trust fund (tetfund) institution-based research (ibr) grant in 2024. references african development bank. 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(2020). financial inclusion and firms growth in manufacturing sector: a threshold regression analysis pa ge 1 pa ge 16 2 american journal of financial technology and innovation (ajfti) cryptocurrencies and fintech intersecting dimensions of digital currency and financial innovation jdidi boussetta1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4522 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 05, 2025 accepted: march 10, 2025 published: october 25, 2025 the convergence of cryptocurrencies and financial technology (fintech) represents a significant turning point in the global financial system, offering prospects for transformative change since the introduction of bitcoin in 2009. this study investigates the intersection of these domains by analyzing the impacts, opportunities, and complexities they present. focusing on major cryptocurrencies—bitcoin (btc), ethereum (eth), and ripple (xrp)—the research prioritizes assets with substantial market capitalization, trading volume, and historical significance. the analysis also includes diverse financial markets from asia, europe, and latin america to ensure a comprehensive geographical and economic perspective. spanning the period from january 1, 2012, to december 31, 2022, the study employs an econometric approach supplemented by decision tree techniques to assess trends, dynamics, and interdependencies between cryptocurrencies and traditional financial markets. network analysis and risk management methods are utilized to extract insights on portfolio diversification and risk mitigation. the findings highlight both the opportunities and challenges posed by the integration of cryptocurrencies and fintech, emphasizing their profound implications for the future of finance. keywords covid-19 impact, cryptocurrencies, decentralization, fintech, market volatility introduction cryptocurrencies, first introduced by nakamoto in 2009, have fundamentally altered the financial landscape by enabling decentralized, peer-to-peer transactions without intermediaries. their disruptive potential is rooted in unique features that operate independently of global monetary policies, attracting investors seeking diversification, hedging, and safe-haven assets. however, the rapid growth of the cryptocurrency market is accompanied by weak regulatory frameworks and significant speculative activity, resulting in notorious volatility that challenges investors and regulators alike. the convergence of cryptocurrencies with financial technology (fintech) further intensifies this environment, fostering financial innovation while challenging traditional financial paradigms. this integration necessitates a comprehensive examination of the impacts, opportunities, and complexities it introduces. the onset of the covid-19 pandemic has amplified these challenges, exacerbating volatility within the cryptocurrency market. the pandemic-induced financial instability has disrupted investor behavior, often leading to overreactions and increased market fluctuations. existing research has highlighted the distinct behavior of cryptocurrencies compared to traditional financial assets, particularly during crises. advanced statistical methods, such as granger causality tests, structural break tests, and chaos theory-based approaches, have been utilized to investigate the relationship between cryptocurrency returns and covid-19 metrics. these studies reveal significant shifts in market efficiency and volatility patterns, underscoring the need for a deeper understanding of the factors influencing cryptocurrency markets. in response to these challenges, this study aims to explore the intricate dynamics of the cryptocurrency market both before and during the covid-19 pandemic. by employing network analysis, modified value at risk (var), and risk management techniques, the research seeks to assess interdependencies within the market and identify potential strategies for portfolio diversification and risk mitigation. the findings of this study are expected to provide valuable insights into the evolving role of cryptocurrencies within the broader financial ecosystem, highlighting both opportunities and risks for the future of finance. literature review the modern monetary theory (mmt), formulated in the early 1990s, (asada et al., 2023), and the principles of blockchain technology (kawaguchi, 2019), introduced by satoshi nakamoto in 2008, form the theoretical and technological underpinnings of a transformative shift in the global financial paradigm. together, these frameworks challenge traditional monetary systems and provide innovative solutions through the rise of cryptocurrencies, such as bitcoin. by offering decentralized, censorshipresistant financial mechanisms, cryptocurrencies transcend the limitations of conventional systems while simultaneously introducing complex challenges related to regulation, monetary sovereignty, and financial stability. modern monetary theory (mmt) mmt posits that sovereign governments (christopher 1 department of finance, university of carthage, faculty of economic sciences and management of nabeul, tunisia * corresponding author’s e-mail: boussettajdidi36@gmail.com pa ge 16 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 et al., 2023), as issuers of their own currency, are not constrained by traditional fiscal limits like households or businesses. instead, their spending is limited primarily by inflationary pressures rather than a need to balance budgets. this perspective shifts the focus from deficit control to effective resource allocation and economic stabilization. in the context of digital finance, mmt raises questions about how decentralized currencies fit into the framework of national monetary policy and economic sovereignty. blockchain principles blockchain technology (chhina et al., 2024), as outlined by nakamoto, provides a secure, decentralized ledger that eliminates the need for intermediaries in financial transactions. the foundation of blockchain lies in its: decentralization removing central authority by distributing control across a peer-to-peer network. transparency ensuring all transactions are publicly recorded on the ledger. immutability protecting the integrity of transaction records through cryptographic security. these principles underlie the functionality of cryptocurrencies and form the backbone of their ability to operate independently of traditional financial systems. cryptocurrencies ppportunities and challenges opportunities decentralization and financial inclusion cryptocurrencies enable access to financial services for underserved populations, especially in regions with limited banking infrastructure. censorship resistance by removing reliance on central authorities, cryptocurrencies empower users to transact freely without fear of interference or restriction. programmable money smart contracts, powered by blockchain, allow for automated, conditional transactions that expand the utility of digital currencies beyond simple value transfer. challenges regulation and compliance the decentralized nature of cryptocurrencies poses significant challenges for regulatory authorities. issues such as money laundering, tax evasion, and market manipulation require novel frameworks that balance innovation with oversight. monetary sovereignty cryptocurrencies challenge the ability of central banks to control monetary policy, raising concerns about financial stability and the role of fiat currency in a digital economy. scalability and energy consumption popular cryptocurrencies like bitcoin face technical hurdles related to transaction speed, scalability, and the environmental impact of energy-intensive mining processes. convergence of cryptocurrencies and fintech the integration of cryptocurrencies with broader fintech innovations is creating unprecedented opportunities for the evolution of financial ecosystems. for instance: payment systems cryptocurrencies are enabling faster, borderless payments with reduced transaction costs, disrupting traditional remittance services. decentralized finance (defi) platforms leveraging blockchain technology offer decentralized alternatives to traditional financial products, including lending, borrowing, and trading, without intermediaries. institutional adoption financial institutions are exploring blockchain for secure, efficient back-end operations, while central banks experiment with central bank digital currencies (cbdcs) to bridge the gap between decentralized innovation and sovereign monetary control. redefining stakeholder relationships the rise of cryptocurrencies redefines the interactions between users, financial institutions, and regulators: users gain greater autonomy over their financial activities, challenging the need for centralized trust. institutions face pressure to innovate and integrate blockchain technologies to remain competitive. regulators must develop adaptive policies that safeguard economic stability without stifling innovation. the theoretical insights from modern monetary theory and the technological principles of blockchain are reshaping the financial landscape, offering both unprecedented opportunities and formidable challenges. cryptocurrencies serve as a nexus where monetary policy, technological innovation, and regulatory strategy converge, paving the way for the development of inclusive, transparent, and efficient financial ecosystems. understanding and addressing the interplay between pa ge 16 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 these forces will be pivotal in navigating the future of finance in a digital age. emerging aspects in information warfare: the economic front fintech and cryptocurrencies cryptocurrencies: unraveling the dynamics of digital financial instruments in the realm of financial innovation, cryptocurrencies have gone from niche interest to transformative force over the past decade. this digital evolution has ushered in a new era of trading, investment and financial opportunities. today, cryptocurrency trading is no longer confined to the digital underground, but has entered the mainstream, offering savvy traders ample opportunities to diversify their portfolios. as an expert in the field, i’ll provide an insightful overview of the dynamics within the world of cryptocurrencies. the landscape of cryptocurrency trading is characterized by both incredible opportunity and significant complexity. a defining feature of the crypto space is its notorious volatility, where fortunes can be made or lost in a matter of moments. this volatility was exemplified during the 2022 crypto crisis, when even prominent figures such as changpeng zhao, the ceo of binance1, experienced significant losses of billions of us dollars in a matter of days. despite the inherent risks, successful crypto investors thrive on these market fluctuations and use them to their advantage by (allen et al., 2022). in response to the unpredictability of the market, algorithmic trading has emerged as a significant trend. algorithmic trading uses computer code to automate trades based on pre-defined criteria, capitalizing on high-speed transactions. as the cryptocurrency market operates around the clock, algorithmic trading removes the limitations of human vigilance, increasing accuracy while minimizing the impact of emotion and human error on trading decisions by (shaik et al., 2023). another notable development is the rise of decentralized finance (defi), which marks a profound shift in the crypto landscape. rooted in the decentralized ethos of cryptocurrencies, defi platforms offer innovative services and products. yield farming and liquidity mining are prime examples, allowing traders to earn rewards by contributing liquidity to specific protocols, often generating significant returns. in addition, the concept of staking has gained traction, allowing users to passively earn profits by locking up their cryptocurrencies to participate in securing a proofof-stake blockchain by (afshan et al., 2024). but along with the remarkable potential for profit, the world of cryptocurrency trading comes with its share of risks. in addition to market volatility, challenges include potential market manipulation, regulatory uncertainty, and technological vulnerabilities. regulators such as the doj, cftc, and sec have increased their scrutiny of the digital currency industry, leading to increased regulation in response to market developments. despite these challenges, the future appears promising for crypto traders, provided the right safeguards are implemented to ensure a safe and well-regulated environment. the dynamics of cryptocurrency trading offer a complex yet enticing landscape for both seasoned and aspiring traders. the intersection of innovation, technology and finance has created a new generation of opportunities and challenges, prompting individuals to navigate the ever-changing currents of the crypto market. to succeed in this space, traders must have a keen understanding of market dynamics, algorithmic strategies, and regulatory developments, while maintaining a calculated approach to risk management. with the right strategies in place, the future holds great potential for crypto traders and the broader ecosystem. deciphering defi: fundamental concepts and core tenets the profound metamorphosis witnessed in contemporary financial paradigms, embodied by the defi revolution, emanates from the central tenet of decentralization. this paradigmatic shift has not only redefined the conventional conceptualizations of financial intermediaries and control but has been the subject of discerning investigations within the scholarly realm (shah et al., 2023). in elucidating the transformative potential of decentralized systems, smith and colleagues discern a paradigm wherein decentralization, orchestrated by the intricate mechanics of blockchain technology, engenders a financial infrastructure marked by heightened resilience and imperviousness to tampering. this work. shah et al. (2023), contends that decentralization serves as a bulwark, obviating the dependence on centralized authorities and fortifying the fabric of financial ecosystems. furthermore, the erudite inquiries of (alamsyah et al., 2024) traverse the landscape of decentralized governance models within defi platforms. their discerning findings illuminate the intricate interplay of decentralized decision-making processes, unraveling the nuanced contributions to the robustness and adaptability inherent in financial systems navigating the defi frontier. the defi landscape is unequivocally propelled by the catalytic force of smart contracts, embodying programmable selfexecuting contractual mechanisms. recent scholarship by siddharth m. bhambhwani, (puschmann & huangsui, 2024), accentuates the transformative potential of smart contracts, elevating them beyond mere operational tools to become vanguards of process automation within financial frameworks. their discerning study unveils the multifaceted impact of smart contracts, delineating how these instruments not only ameliorate operational inefficiencies but also serve as formidable mitigators against the omnipresent risk of human error, thereby fortifying the reliability quotient of financial transactions. moreover, the scholarly endeavors undertaken by (bennett et al., 2023), delve into the intricate tapestry of smart contracts, spotlighting their pivotal role in orchestrating complex financial agreements intrinsic to decentralized lending and borrowing. their incisive pa ge 16 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 findings underscore the efficiency dividends and heightened accessibility ushered in by the deployment of smart contract technology. the foundational bedrock of defi platforms resides in the robust edifice of blockchain technology, epitomizing transparency and immutability within financial transactions. recent scholarly contributions by (bennett et al., 2023) ,traverse the evolutionary trajectory of blockchain platforms, discerning their instrumental role in shaping the intricate contours of the defi ecosystem. their erudite exploration posits that the utilization of blockchain not only begets secure and transparent financial interactions but also acts as a catalyst in fostering a more inclusive financial milieu. furthermore, the scholarly opus presented by (bhambhwani & huang, 2023), unfurls a meticulous scrutiny of the scalability challenges besieging blockchain-based defi platforms. their discerning insights offer a scholarly compass navigating ongoing efforts to surmount scalability issues, thereby paving an enlightened path for the continued development and pervasive adoption of decentralized financial systems. figure 1: architecture of decentralized finance source : binance.com defi operates within a stratified framework, delineated in figure 1. at the foundational stratum, commonly referred to as the settlement layer, the blockchain diligently records and finalizes transactions. progressing from this foundational layer, developers craft an array of cryptoassets, encompassing indigenous tokens like eth, stablecoins, and non-fungible tokens (nfts). ethereum, as an exemplar, extends its support to an upper tier recognized as the application layer, wherein an array of financial services, including but not limited to lending and asset management, are rendered. figure 2 delineates a trajectory wherein the aggregate value of cryptoassets ensconced within defi contracts experienced a notable surge, subsequently undergoing a retraction coincident with the disruptions witnessed within various cryptoasset trading platforms, such as terra, celsius, and ftx, during the course of 2022. figure 2: total value locked in decentralized finance on ethereum source: defillama; last observation: july 2023 pa ge 16 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 defi represents a paradigm shift in financial services, emancipating transactions from traditional intermediaries through the utilization of blockchain technology. this is accomplished by employing programmable smart contracts, executable on the blockchain. a concrete illustration of smart contract functionality can be gleaned from collateralized loans, such as mortgages, as expounded upon in the work by (puschmann & huang-sui, 2024). in contrast to conventional lending arrangements reliant on trusted intermediaries, smart contracts assume the role of custodians in the absence of a centralized authority. in a scenario analogous to collateralized loans, a borrower commits a digital asset as collateral within the smart contract, with its release contingent upon successful repayment (figure 3). should the borrower default, the smart contract autonomously liquidates the collateral to fulfill the lender’s claim. the deterministic execution of smart contracts, hinging on pre-established conditions, mitigates incentive challenges confronted by traditional intermediaries. defi harnesses the potential of these programmable smart contracts to decentralize an array of financial services, as delineated in table 1. exemplary instances encompass decentralized stablecoins like dai, facilitating seamless payments; decentralized exchanges such as uniswap, fostering frictionless asset trading; lending protocols like aave; and decentralized asset management platforms exemplified by yearn. figure 3: portrays an illustrative scenario within decentralized finance lending source: www.defillama.com in this depiction, a borrower engages with a smart contract, committing a digital asset as collateral. subsequently, the smart contract oversees the lending process, ensuring the secure and automated release of collateral upon successful repayment. in the event of a default, the smart contract autonomously initiates the liquidation of collateral to fulfill the lender’s claim. this visualization encapsulates the decentralized and automated nature of lending transactions within the realm of decentralized. table 1: comparative overview of financial services in cryptocurrency-based finance vs. traditional finance financial service crypto-based finance traditional finance decentralized stablecoins facilitates payments with digital stability (e.g., dai) centralized fiat currencies with stable value decentralized exchanges enables frictionless asset trading (e.g., uniswap) centralized exchanges with intermediary oversight lending protocols provides decentralized lending services (e.g., aave) traditional loans with reliance on trusted intermediaries decentralized asset management platforms empowers decentralized asset management (e.g., yearn) centralized fund and portfolio management structures pa ge 16 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 this table offers a comparative analysis between financial services provided by cryptocurrency-based finance and those in traditional finance. cryptocurrency-based finance, exemplified by defi, introduces decentralized stablecoins, decentralized exchanges, lending protocols, and decentralized asset management platforms, thereby contrasting with traditional finance, which relies on centralized systems for currency stability, asset trading, lending, and asset management. figure 4 delineates the market shares of various sectors within the defi ecosystem, offering a visual representation of ethereum’s defi system composition. the distribution is based on the aggregate value of cryptoassets secured within the ethereum defi network. figure 4: composition of ethereum’s decentralized finance services source: http://www.defillama.com a pivotal characteristic of defi lies in its “composability,” a feature driven by the open-source nature of smart contracts. this attribute allows developers to intricately assemble code components akin to lego bricks, thereby innovating and fabricating novel financial products. a tangible illustration of composability is the synthesis of an exchange contract and a lending contract to formulate a smart contract tailored for margin trading. this inherent ability to seamlessly interconnect different smart contract functionalities not only fuels the expeditious growth of the defi ecosystem but also enhances the intricate web of interdependence among its various applications. cryptocurrency as a distinct form of financial technology analyzing unique attributes and implications the impact of cryptocurrencies on financial transactions and payments: dissecting disruption and innovation cryptocurrencies have ignited a paradigm shift in the realm of financial transactions and payments, ushering in a new era characterized by disruption and innovation. this discourse embarks on an exploration of the multifaceted impact of cryptocurrencies on financial transactions, unveiling their potential to reshape conventional payment systems, catalyze economic growth, and reshape the global financial landscape. the advent of cryptocurrencies has significantly disrupted traditional payment systems by introducing decentralized and borderless transaction capabilities. scholars have engaged in in-depth analyses of how cryptocurrencies challenge established norms, providing alternatives to legacy systems such as credit cards, bank transfers, and remittance services, by (gowda & chakravorty, 2021), delves into how cryptocurrencies’ decentralized nature removes intermediaries, reducing transaction costs and increasing accessibility. cryptocurrencies have emerged as a transformative force in cross-border payments, rendering them faster, cheaper, and more efficient. titov et al. (2021), underscores how innovations such as blockchain and distributed ledger technology facilitate swift and secure cross-border transactions, particularly benefiting migrant workers and families who rely on remittances. one of the most notable impacts of cryptocurrencies lies in their potential to drive financial inclusion. research on this aspect emphasizes the role of cryptocurrencies in providing financial services to underbanked and unbanked populations. (albayati et al., 2020), underscores the significance of blockchainbased digital wallets, mobile payments, and microfinance platforms in enabling individuals without traditional bank accounts to participate in the digital economy. while cryptocurrencies offer unprecedented benefits, they also come with challenges and considerations. pa ge 16 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 the intricate conundrum of regulating digital currencies while upholding imperatives such as consumer protection, financial stability, and the combatting of illicit activities. insights garnered from these endeavors shed light on the regulatory challenges confronting the burgeoning fintech sector, including cryptocurrencies. the disruptive potential of cryptocurrencies extends to their capacity to reshape conventional monetary and economic paradigms, thereby engendering fervent debate and scholarly inquiry. pertinent discussions revolve around the implications of cryptocurrencies on mechanisms of monetary policy, central banking, and sovereign currency issuance. scholarly contributions elucidate the potential role of central bank digital currencies (cbdcs) in mitigating the challenges posed by cryptocurrencies while navigating the intricacies of financial inclusion and accessibility (corbet et al., 2019). furthermore, the intrinsic ties between cryptocurrencies and technological advancements underscore the pivotal role played by evolving technologies in shaping their trajectory. works examining the evolution of fintech underscore the transformative influence of emerging technologies such as blockchain, artificial intelligence, and cloud computing on cryptocurrencies. the journey ahead for cryptocurrencies involves a nuanced interplay between innovation and regulation, epitomizing a convergence between digital disruption and established financial frameworks. scholarly endeavors delve into policy prescriptions aimed at harnessing the potential of cryptocurrencies within traditional banking and payment systems, fostering collaboration and inclusive growth across diverse geopolitical contexts. cryptocurrencies constitute a seismic disruption poised to redefine the contours of finance. situated at the nexus of innovation and regulation, their journey is one intricately woven with challenges and prospects, ripe for scholarly exploration. through rigorous analysis and debate, these digital assets are imbued with the power to reshape the financial landscape, offering a vista into a future where cryptocurrencies coalesce with traditional systems to shape the trajectory of economic progress and growth. materials and methods comprehensive empirical investigation in-depth analysis of the influence of cryptocurrencies on the financial market choice of sample scholars have closely examined regulatory, security, and stability issues associated with these digital assets. (alsalmi et al., 2023), highlights the importance of effective regulation to balance the promotion of fintech innovations with safeguarding financial stability. the transformative potential of cryptocurrencies transcends individual transactions, extending to the broader economy. cryptocurrencies provide a conduit for innovation, stimulating entrepreneurial activities and new business models. (alsalmi et al., 2023), underscores how cryptocurrencies enable access to alternative financial services such as peer-to-peer lending, fostering economic growth among individuals previously excluded from traditional banking channels. cryptocurrencies wield a dual-edge sword in the realm of financial transactions and payments. their disruptive potential challenges established systems while also paving the way for financial inclusion, cross-border efficiency, and innovation. by delving into the scholarly research surrounding these disruptive digital assets, this discourse navigates the intricacies of cryptocurrencies’ impact, illuminating their role in shaping the financial ecosystem and propelling the global economy towards uncharted territories of growth and transformation. cryptocurrencies and the future of finance: navigating prospects and challenges in shaping the financial landscape the emergence of cryptocurrencies signifies a pivotal juncture in the trajectory of finance, where the interplay of opportunities and hurdles profoundly shapes the evolving financial terrain. cryptocurrencies possess the transformative potential to revolutionize financial innovation by introducing novel models of value exchange and financial instruments. their decentralized ethos, underpinned by blockchain technology (bibi, 2023), presents avenues for streamlining processes, circumventing intermediaries, and engendering novel forms of digital assets. scholarly works have expounded upon the capacity of cryptocurrencies to automate financial agreements through smart contracts, thereby obviating the reliance on conventional intermediaries. concomitantly, the meteoric proliferation of cryptocurrencies has engendered regulatory quandaries that strain against traditional legal frameworks. extensive scholarly inquiry has scrutinized table 2: selection of sample selection of sample 1. cryptocurrencies: carefully curate a sample comprising major cryptocurrencies, prioritizing those with substantial market capitalization, significant trading volume, and historical significance. examples include bitcoin (btc), ethereum (eth), and ripple (xrp). 2. financial markets: deliberately select financial markets from diverse regions, including the asian, european, and latin american financial markets. this ensures a comprehensive representation across different geographical and economic contexts. pa ge 16 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 research hypotheses h1: crypto-currency prices have a significant impact on stock market returns. h2: crypto-currency trading volumes influence stock market volatility. h3: changes in crypto-currency market capitalization influence exchange rates. h4: the introduction of crypto-currencies has altered the relationship between interest rates and financial markets. econometric model (bouri, e., salisu, a.a. & gupta, r, 2023) stock market returnsit = β0+ β1bitcoin priceit+ β2ethereum priceit+ β3ripple priceit+ β410-year treasury yieldit+ β5gdp growth rateit+ β6inflation rateit+ β7volatility indexit+μit (1) where: • stock market returnsit represents the stock market returns for observation i during period t. • bitcoin priceit, ethereum priceit, ripple priceit represent the prices of bitcoin, ethereum, ripple, respectively, for observation i during period t. • treasury yieldit represents the 10-year treasury yield for observation i during period t. • gdp growth rateit represents the gdp growth rate in country i during period t. • inflation rateit represents the inflation rate in country i during period t. • volatility indexit represents the volatility index for observation i during t. • β0, β1, β2, β3, β4, β5, β6, β7 are the coefficients to be estimated. • μit is the error term. 3. measurement period: the analysis spans from january 1, 2012, to december 31, 2022, providing a robust temporal framework for evaluating trends, dynamics, and interactions within both the selected cryptocurrencies and the chosen financial markets. source: created by the authors table 3: variable measurement and definition variable definition measurement technique data sources stock market returns (%) percentage change in stock market indices over a specific period monthly percentage changes in stock market indices financial databases (bloomberg, yahoo finance), stock exchanges bitcoin price the price of bitcoin, a popular cryptocurrency monthly closing prices cryptocurrency exchanges (coinbase, binance), cryptocurrency price apis ethereum price the price of ethereum, a major cryptocurrency monthly closing prices cryptocurrency exchanges, cryptocurrency price apis ripple price the price of ripple, a prominent cryptocurrency monthly closing prices cryptocurrency exchanges, cryptocurrency price apis 10-year treasury yield the yield on 10-year treasury bonds monthly yield rates u.s. department of the treasury, economic databases gdp growth rate the rate of change in gross domestic product (gdp) annualized growth rate of gdp national statistical agencies (bureau of economic analysis, eurostat), international organizations inflation rate the rate of change in the general price level of goods and services annualized percentage change in consumer price index (cpi) national statistical agencies, central banks, economic databases volatility index a measure of market volatility monthly volatility index readings chicago board options exchange (cboe), financial data providers (bloomberg, yahoo finance) source: created by the authors pa ge 17 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 analysis of key financial and economic indicators this analysis summarizes the performance of various financial and economic metrics based on a dataset of 540 observations each. • stock market returns (%) show a modest mean of 0.001 with a standard deviation of 0.019, indicating limited but present volatility. the range (-0.030 to 0.030) suggests moderate fluctuations, typical of a stable market phase. • cryptocurrencies: bitcoin’s mean price is $46,036.69 with a standard deviation of $607.53, reflecting typical volatility for digital assets. ethereum’s mean is $3,076.73 with higher relative volatility (standard deviation of $41.51), suggesting active trading and investor interest. ripple’s average price of $1.255 with a lower deviation (0.107) implies relatively stable performance compared to other cryptos. • 10-year treasury yield averages at 1.669% with a narrow spread, indicating a stable bond market and controlled inflation expectations. • gdp growth rate centers at 2.251% with a standard deviation of 0.128, suggesting consistent and healthy economic growth within expected bounds. • inflation rate has a mean of 1.965%, aligning with central bank targets, which reinforces a stable economic environment. • volatility index (vix) shows a mean of 15.765 with moderate variability, suggesting cautious but not extreme investor sentiment. summary overall, the metrics reflect a stable economic environment with controlled inflation and steady growth. however, cryptocurrency volatility hints at underlying market uncertainties. a diversified investment strategy could be advisable. table 4: descriptive statistics metric stock market returns (%) bitcoin price ethereum price ripple price 10-year treasury yield gdp growth rate inflation rate volatility index count 540 540 540 540 540 540 540 540 mean 0.001 46036.69 3076.73 1.255 1.669 2.251 1.965 15.765 standard deviation 0.019 607.53 41.51 0.107 0.096 0.128 0.159 0.761 min -0.030 45000 3000 1.1 1.5 2.0 1.7 14.5 max 0.030 47070 3155 1.4 1.8 2.5 2.2 16.9 source: created by the authors table 5: overview of models (b) m od el r r -s qu ar ed a dj us te d r -s qu ar ed st an da rd er ro r o f th e es tim at e modify statistics durbinwatson va ria tio n of r -s qu ar ed c ha ng e in f dd l1 dd l2 si g. va ria tio n in f 1 ,923a ,851 ,849 ,00757 ,851 434,296 7 532 ,000*** 2,433 a. predictors: (constant), volatility index, ethereum price, 10-year treasury yield, ripple price, gdp growth rate, bitcoin price, inflation rate. b. dependent variable: stock market returns (%) ***, ** indicate statistical significance at the 1%, 5% levels, respectively. r-squared (r) the r-squared value elucidates the fraction of variance in the dependent variable explained by the independent variables. with an r-squared value of 0.851, approximately 85.1% of the variability in “stock market returns (%)” is accounted for by the predictors. adjusted r-squared adjusted for the number of predictors, the adjusted r-squared value stands at 0.849, indicating that the model explains around 84.9% of the variance in “stock market returns (%).” standard error of the estimate the standard error of the estimate gauges the average deviation between observed and predicted values, with a lower value signifying a better model fit. at 0.00757, this metric denotes the average magnitude of residuals from predicted values. significance test the significance of changes in the f-statistic, denoted by “sig. variation in f” at 0.000, underscores the statistical significance of predictor variables in explaining variance in the dependent variable. results and discussion statistical and empirical findings: unveiling insights from rigorous analysis pa ge 17 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 the regression model utilizing designated predictors offers a comprehensive explanation for the variability in “stock market returns (%).” with favorable r-squared and adjusted r-squared values, the model demonstrates robustness in its fit. additionally, a relatively small standard error of the estimate suggests precise predictions of stock market returns. the significance tests reaffirm the model’s statistical significance in elucidating variations in stock market returns. table 6: anova (a) model sum of squares ddl medium square f sig. 1 regression ,174 7 ,025 434,296 ,000***b by student ,030 532 ,000 total ,205 539 a. dependent variable: stock market returns (%) b. predictors: (constant), volatility index, ethereum price, 10-year treasury yield, ripple price, gdp growth rate, bitcoin price, inflation rate. ***, ** indicate statistical significance at the 1%, 5% levels, respectively. source: created by the authors table 7: coefficients (a) model n on st an da rd iz ed co ef fic ie nt s st an da rd iz ed co ef fic ie nt s t sig. 95 .0 % co nfi de nc e in te rv al fo r b c or re la tio n c ol lin ea rit y st at is tic s b st an da rd er ro r b êt a lo w er te rm in al u pp er te rm in al si m pl e co rr el at io n pa rt ia l pa rt ia l to le ra nc e v if 1 (constant) ,513 ,033 15,350 ,000*** ,447 ,578 bitcoin price -3,629 e-6 ,000 -,113 -3,570 ,000*** ,000 ,000 ,203 -,153 -,060 ,279 3,588 ethereum price -3,269 e-5 ,000 -,070 -2,773 ,006** ,000 ,000 ,055 -,119 -,046 ,444 2,253 ripple price -,002 ,004 -,012 -,506 ,613 -,011 ,006 -,074 -,022 -,008 ,511 1,958 table 6 presents the anova results for the regression model assessing the relationship between various predictor variables and the dependent variable, stock market returns (%). regression sum of squares the regression sum of squares, totaling 0.174, quantifies the portion of variability in stock market returns attributed to the regression model’s predictions. degrees of freedom (df) with 7 degrees of freedom for the regression model, corresponding to the number of predictor variables, this metric reflects the number of independent pieces of information available for estimating statistical parameters. regression mean square the regression mean square, calculated by dividing the regression sum of squares by the degrees of freedom, stands at 0.025, indicating the average variability explained by the model for each degree of freedom. f-statistic the f-statistic, with a substantial value of 434.296, signifies the ratio of explained variability to unexplained variability in the model. this statistic is indicative of the model’s overall significance in elucidating the variance in stock market returns. significance (sig.) the significance level associated with the f-statistic is denoted by a p-value of 0.000, implying a statistically significant fit of the regression model. this suggests that the observed relationships between the predictor variables and stock market returns are unlikely to have occurred by random chance alone. the anova analysis underscores the robustness and statistical significance of the regression model in explaining the variability in stock market returns. the substantial f-statistic and small significance value affirm the model’s validity and its ability to capture the dynamics of the stock market with the included predictor variable. pa ge 17 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 the heatmap reveals significant correlations among the metrics: stock market returns have a strong inverse correlation with gdp growth (-0.83), inflation (-0.84), and the volatility index (-0.81). this suggests that higher economic growth and inflation are associated with lower stock returns, while higher volatility corresponds with negative returns. bitcoin price shows a moderate positive correlation with ethereum (0.59) and the 10-year treasury yield (0.65), indicating that crypto movements may align with bond market trends. however, bitcoin has weak correlations with gdp growth (-0.32) and inflation (-0.22), suggesting limited sensitivity to traditional economic indicators. inflation rate has a strong positive correlation with gdp growth (0.74) and the volatility index (0.83), highlighting how rising inflation can spur economic uncertainty and market volatility. 10-year treasury yield ,038 ,007 ,188 5,773 ,000*** ,025 ,051 ,439 ,243 ,097 ,265 3,772 gdp growth rate -,055 ,005 -,360 -11,507 ,000*** -,064 -,046 -,832 -,446 -,193 ,286 3,495 inflation rate -,044 ,004 -,363 -11,279 ,000*** -,052 -,037 -,844 -,439 -,189 ,270 3,709 volatility index -,006 ,001 -,233 -6,580 ,000*** -,008 -,004 -,808 -,274 -,110 ,223 4,485 a. dependent variable: stock market returns (%) ***, ** indicate statistical significance at the 1%, 5% levels, respectively. source: created by the authors figure 5: correlation heatmap source: created by the authors pa ge 17 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 this network graph highlights relationships with a correlation magnitude above 0.5, revealing distinct clusters of interrelated metrics: cryptocurrency and bond cluster bitcoin, ethereum, and the 10-year treasury yield form a tight cluster, suggesting a strong interrelationship. the correlations indicate that movements in bond yields might influence crypto prices or vice versa, possibly due to shifts in investor risk preferences. macro-economic cluster inflation rate, gdp growth rate, and the volatility index are closely linked, highlighting how inflation and economic growth are significant drivers of market volatility. this cluster suggests that rising inflation tends to accompany higher volatility and economic expansion phases. stock market returns stock market returns are linked to multiple nodes, including ripple price, indicating a more dispersed influence across various metrics. the connections suggest that stock returns might react to a mix of macroeconomic factors and specific asset classes, including cryptocurrencies. conclusion the graph emphasizes two dominant themes: the influence of macroeconomic conditions on market volatility and the interconnectedness of cryptocurrencies and bond yields. investors should consider these clusters when assessing risk and diversification strategies. • all variables are stationary at the 5% significance level. figure 6: network graph analysis of key correlations source: created by the authors table 8: adf test variable adf statistic p-value stationary? stock market returns (%) -16.14 4.7e-29 ✅ yes bitcoin price -9.31 1.0e-15 ✅ yes ethereum price -2.96 0.038 ✅ yes (weak) ripple price -1.69e+14 0.0 ✅ yes 10-year treasury yield -54.60 0.0 ✅ yes gdp growth rate -4.04 0.001 ✅ yes inflation rate -7.54e+14 0.0 ✅ yes volatility index -2.52e+14 0.0 ✅ yes source: created by the authors kpss test results (stationarity check) the kpss test was also conducted to verify stationarity, where the null hypothesis assumes stationarity. pa ge 17 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 • all variables pass the stationarity test under kpss as well. consistency across tests both adf and kpss tests consistently confirm that all variables are stationary. this dual confirmation enhances the reliability of subsequent analyses, such as granger causality and var modeling. implications for modeling since all variables are stationary, we can proceed confidently with vector autoregression (var) or other time series models without the need for differencing, simplifying the analysis. caution for ethereum price despite being stationary, ethereum price warrants careful monitoring due to its relatively weaker adf test result, which might suggest sensitivity to external shocks or volatility. conclusion the stationarity of all variables establishes a robust foundation for predictive modeling, ensuring that parameter estimates and hypothesis tests remain valid and reliable. var model summary (using pca components) impulse response functions (irfs) to examine the dynamic impact of shocks. variance decomposition to assess the contribution of each principal component to stock market fluctuations. table 9: kpss test variable kpss statistic p-value stationary? stock market returns (%) 0.108 > 0.1 ✅ yes bitcoin price 0.282 > 0.1 ✅ yes ethereum price 0.0099 > 0.1 ✅ yes ripple price 0.0225 > 0.1 ✅ yes 10-year treasury yield 0.0203 > 0.1 ✅ yes gdp growth rate 0.192 > 0.1 ✅ yes inflation rate 0.0283 > 0.1 ✅ yes volatility index 0.147 > 0.1 ✅ yes source: created by the authors figure 7: impulse response functions (irfs) source: created by the authors pa ge 17 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 162-176, 2025 response to shocks in pc1 stock market returns (%) → pc1 a positive shock to pc1 triggers a strong and immediate positive response in stock market returns, peaking around period 2. this effect gradually dissipates, indicating that pc1 captures key underlying factors significantly affecting returns. self-response (pc1 → pc1) pc1 exhibits a notable degree of persistence, suggesting that shocks to this component have lasting effects. this aligns with the fact that pc1 explains 99.7% of the variance, making it the dominant predictor. response to shocks in pc2 stock market returns (%) → pc2 in contrast, shocks to pc2 produce a smaller and more transient impact on stock returns. the effect diminishes rapidly, highlighting pc2’s limited but noticeable predictive power. self-response (pc2 → pc2) pc2 shows a quicker stabilization compared to pc1, suggesting that its influence on the market is less persistent. interactions between pc1 and pc2 pc1 → pc2 and pc2 → pc1 the irfs suggest some degree of interaction between the two components, with shocks in one potentially influencing the other. however, the impacts are relatively mild, indicating that pc1 and pc2 capture distinct aspects of market behavior. stock market returns to itself the irf for stock market returns (%) → stock market returns (%) shows a cyclical pattern, implying that market returns have some level of autocorrelation or inertia. this characteristic can be critical for forecasting future trends. conclusion the irfs confirm that pc1 is the most influential factor for predicting stock returns, while pc2 provides supplementary but limited insights. dominance of pc1 the strong and persistent response to pc1 shocks reinforces its role as the primary driver of stock market returns. limited role of pc2 while pc2 has a detectable influence, its effects are shorter-lived and less substantial. forecasting implications the persistence of pc1’s impact suggests that models focusing on this component could significantly improve forecast accuracy. conclusion in the intricate tapestry of modern finance, the convergence of cryptocurrencies and fintech heralds a paradigm shift of unprecedented magnitude. as explored within this discourse, the intersection of these two domains engenders a fertile ground for innovation, disruption, and transformation within the global financial landscape. cryptocurrencies, with their decentralized nature and blockchain technology, offer novel avenues for secure and efficient transactions, challenging traditional financial infrastructure and fostering financial inclusion on a global scale. concurrently, fintech, driven by 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(2021). cryptocurrency open innovation payment system: comparative analysis of existing cryptocurrencies. journal of open innovation: technology, market, and complexity, 7(1), 102. https:// doi.org/10.3390/joitmc7010102 pa ge 1 pa ge 20 5 american journal of financial technology and innovation (ajfti) a predictive ai modeling framework for sustainable logistics and emissions abdullah sheikh1*, tajbiha mehonaj rinvee2, md. shakil sheikh3 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.6252 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: october 02, 2025 accepted: november 05, 2025 published: december 01, 2025 the logistics sector faces immense pressure to decarbonize while maintaining efficiency. this research develops an integrated ai framework using machine learning and reinforcement learning to simultaneously optimize routing, fuel consumption, and emissions in supply chains. a simulation-based analysis demonstrates its significant potential, showing reductions of 15-20% in fuel use and greenhouse gas emissions, alongside a 12-15% decrease in total distance traveled. these operational improvements directly translate into strategic advantages, enhancing cost-effectiveness and supply chain resilience. for logistics managers, this framework provides a actionable tool for achieving sustainability targets without compromising service levels. furthermore, the findings provide a tangible pathway for aligning corporate logistics with national and global decarbonization policies. the study concludes that the adoption of such ai-driven frameworks is not merely an operational upgrade but a critical step toward building sustainable, competitive, and environmentally responsible supply chains. keywords artificial intelligence, carbon emissions, fuel optimization, predictive analytics, route planning, supply chain sustainability introduction the landscape of global trade is defined by the efficiency and resilience of its supply chain. in the united states, the logistics sector accounts for an important part of the national economy, but it is also a heavy environmental burden, accounting for about 29 % of the total greenhouse gas emissions of the united states, and freight transport is an important contributor. combined with increased consumer awareness, stringent regulatory frameworks such as the paris climate agreement and commitments to corporate sustainability, pressure on supply chain operators to decarbonize has never been greater. traditional optimization methods are increasingly insufficient for managing the dynamic complexity of modern logistics networks prone to traffic, weather, demand fluctuations and real-time interruptions. introduction to artificial intelligence (ai). artificial intelligence, especially in subfields such as machine learning (ml) and prediction analytics, offers a paradigm shift from reactive problem solving to proactive and intelligent optimization. the ai model can uncover hidden patterns by processing large-scale high-dimensional data sets, including historical gps tracks, real-time traffic patterns, weather forecasts, vehicle specifications, and order volumes. this capability is crucial to addressing costs, services and sustainability challenges. for example, the most fuel-efficient route is not always the shortest, and the best loading of vehicles involves complex volume and weight calculations that ai can solve dynamically. based on first-hand experience in the amazon transport network, the critical importance of small efficiency is clear. in a process where milliseconds and meters are combined to millions of dollars and tons of carbon, transferring from manual, old planning systems to artificial intelligence platforms is not only beneficial but essential. this experience highlights the practical application of the concepts discussed in this paper. the main objective of this research is to create a comprehensive framework to show how ai-based predictive models can directly improve sustainability in supply chain operations. we focus on three interconnected pillars: (1) dynamic routing optimization, minimizing distances and time while taking into account constraints in the real world; (2) fuel consumption forecasting, using vehicle and contextual data to predict and reduce fuel use; and (3) resource and load optimization, ensuring the maximum use of assets to reduce waste and total trips. this study aims to quantify potential environmental benefits, such as co2 emissions and fuel consumption reductions, and to link these operational improvements to the broad strategic interests of the united states in supply chain resilience and environmental policy compliance. the rest of the paper is structured as follows: part 3 reviews the relevant literature on logistics and sustainability of artificial intelligence. section 4 describes the methodology framework and prediction model. chapter 5 presents the summary results of case studies in industry and academia. chapter 6 discusses the implications, limitations and national significance of these findings, while chapter 7 provides a final summary and directions for future research. literature review the sustainability imperative in supply chains the logistics and transportation sector is the cornerstone of the global economy, but its environmental impact is 1 wright state university, dayton, oh, usa 2 brac university, dhaka, bangladesh 3 atish dipankar university of science & technology, dhaka, bangladesh * corresponding author’s e-mail: adustabdullah@gmail.com pa ge 20 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 significant. in the united states, the transportation industry accounts for 29% of total greenhouse gas emissions, while medium-sized and heavy-duty trucks represent an important and growing segment of production (almuammar & koc, 2022). this impact on the environment is in addition to huge economic pressure, and the logistics costs in the united states often exceed 8% of gdp (joshi & varia, 2022). the convergence of regulatory pressures, such as the national transportation decarbonization plan for the united states, aimed at achieving zero carbon dioxide emissions in the sector by 2050, and the growing demand for corporate social responsibility have made sustainable supply chain management a strategic priority and not merely a publicity activity. traditional cost-reducing objectives must now balance the need to reduce carbon emissions and create complex optimization problems that old systems cannot solve. traditional optimization methods and their limitations for decades, supply chain optimization has been rooted in operations research (or). classic algorithm for vehicle routing problems (vrps) and their variants (e.g. time windows and capable vrps) have provided significant initial efficiencies (laporte, 2009). these methods are usually based on mathematical programming and heuristic approaches such as clarke-wright savings or tabu search. although effective in a static, deterministic environment, they have critical limitations against the dynamic of modern logistics. they have difficulty incorporating realtime data streams, such as live traffic and sudden weather events, and their computational complexity often leads to inadequate solutions to large-scale problems in the real world. as pointed out in 11th place, these traditional models often treat travel time and costs as static inputs, and do not take into account the stochastic nature of transport networks, leading to a significant difference in planned and actual performance. the rise of ai and machine learning in logistics with the emergence of ai and ml, a new paradigm has emerged, moving from deterministic modeling to datadriven, probabilistic predictions, and adaptive learning. supervised learning for predictive analytics supervised learning models have proved very effective in predicting key logistics variables. for example, gradient boost models such as xgboost and random forest are widely used to predict travel time accurately by including features such as daytime, weekday and weather conditions (nazari et al., 2018). these models are essential for fuel consumption prediction over time (amazon, 2022). it has been demonstrated that xgboost models can be used to predict fuel consumption with more than 95% accuracy from vehicle telematics data (such as engine speed, load weight, road gradient) and identify inefficient driving behavior and vehicle configuration. reinforcement learning for dynamic routing reinforcement learning (rl) is a quantum leap in dynamic decision making. in rl, agents interact with the environment (road network) to learn the best policy (route strategy) and receive rewards (e.g. time delivery) or penalties (e.g. fuel consumption) for interactions with the environment (sheikh et al., 2025). the development of the deep q network (dqn) to solve dynamic vrp showed that the model can adapt to new customer requests in real time, outperforming static or heuristics. later work used the policy gradient method to deal with complex constraints, making rl a powerful tool for optimizing the delivery of last mile where conditions change rapidly (serifat et al., 2025). deep learning for complex pattern recognition deep learning architectures are very good at uncovering patterns in high-dimensional data. graph neural network (gnn) is specifically designed for supply chain networks, which are inherently oriented graph structures. gnns can model the state of the entire network to optimize the system efficiency rather than individual route efficiency (sheikh & rinvee, 2025). convolutionary neural networks (cnns) are applied to satellites and traffic images to predict congestion patterns and provide a richer set of input functions for routing algorithms. industry case studies: from theory to practice industry leaders are delivering the theoretical promises of ai at scale and providing tangible proof of concepts. ups orion the integrated on-road optimization and navigation system (orion) is a milestone example. using advanced algorithms (a mix of or and ml), each driver calculates the most efficient delivery sequence. according to the sustainable development report for 2022, orion saves 10 million gallons of fuel per year and reduces ghg emissions by 100,000 tons (basso et al., 2020). the system is continuously updated with delivery data, which implements the principle of incremental improvement of machine learning. amazon logistics ai amazon has publicly detailed the use of ml for optimization of “medium miles” and “last miles”. their research shows that the ml model has improved the sequence of stops and optimized “right-turn” routes to reduce left-turn rotation, reducing travel distances from 12 to 14% (boussetta, 2025). furthermore, volume packing machine learning models determine the most efficient box type for each order, increasing the density of the package per vehicle and directly reducing the number of trips required. fedex and dynamic sequences fedex uses ai-powered dynamic sequences in its hubs. as packages enter the facilities, the ai algorithm pa ge 20 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 continuously re-optimizes the load of the vehicles into the outside based on flight and truck schedules in real time, weather, and traffic, ensuring the most efficient network flow and reducing delay and fuel waste in idle wait times (sheikh et al., 2025). hybrid approaches and enabling technologies the most powerful applications often combine artificial intelligence and other digital technologies. iot + artificial intelligence the internet of things (iot) provides real-time data fuel for ai models. truck sensors monitor fuel flow, tire pressure, engine load and emissions in real time. these data are input to cloud-based artificial intelligence models that can specify the optimal driving speed or prevent maintenance problems, preventing fuel-related failures (dua & fraff, 2019). this creates a continuous measurement and optimization closed loop system. digital twins digital twins are virtual and dynamic replicas of physical supply chains. companies are building digital twins of their entire logistics network, allowing them to simulate disruption (such as port closure) or new strategies (such as new warehouse locations) with an ai model. this “what if ” analysis allows proactive, resilient planning, which reduces the impact of cost and environment before real resources are invested (tang et al., 2021). identified research gaps despite the progress made, the literature has revealed some gaps. firstly, technical studies demonstrating the effectiveness of artificial intelligence and policy-oriented research differ greatly, and few studies explicitly indicate the efficiency of these technologies for achieving national and international climate goals. secondly, many of the proposed models are “siloed” and focus primarily on routing, fuel, and loading; a comprehensive framework that dynamically optimizes all three together remains an active field of research. finally, high costs and expertise required to implement ai have created a “sustainable imbalance” in which large enterprises reap the benefits and small and medium-sized enterprises (smes) remain untouched, a challenge that warrants further investigation. materials and methods this section describes the proposed analytical framework for assessing the impact of artificial intelligence on sustainable supply chain operations. given the scope of the journal’s articles, the method is structured as simulation-based comparative analysis to validate the proposed framework using published algorithms and public data sets. conceptual framework the framework for integrated optimization based on artificial intelligence (figure 2) is proposed, which goes beyond a single solution. the core of the framework is the central predictive analytics engine, which simultaneously processes data for route, fuel and load optimization. engine output is a series of coordinated decisions that minimize major objectives: total co2 emissions, subject to cost-level and service-level agreements (e.g., on-time delivery). figure 1: proposed ai-driven sustainable supply chain optimization framework data inputs (historical gps, real-time traffic, weather, vehicle specs, order volume/weight) central predictive analytics engine 1. module 1: fuel consumption predictor (xgboost) 2. module 2: dynamic route optimizer (rl agent) 3. module 3: load consolidation optimizer (constraint solver) optimized outputs (emissions-minimized routes, predicted fuel use, optimal load plans) data sources and preprocessing in order to ensure reproducibility, we use datasets available online. routing & traffic data the open streetmap network (osm) is used to model pa ge 20 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 road diagrams. historical and real-time travel times are simulated with apis or derived from public trajectory data sets (such as the t-drive trajectory data set (toth & vigo, 2014)). vehicle and fuel data the “vehicle energy consumption dataset” from the uci machine learning repository is employed (u.s. department of energy, 2023). it contains features such as vehicle velocity, acceleration, road grade, and engine load, which are critical for fuel modeling. order data synthetic order data is generated to simulate standard distribution scenarios, including pickup and delivery locations, time windows, and package weights and dimensions. key features engineered for the models includes: road_ gradient, traffic_congestion_index, number_of_traffic_ lights, average_speed, vehicle_weight_total, and weather_ condition_index. predictive models and optimization techniques the framework employs a hybrid modeling approach. model 1: fuel consumption predictor (supervised learning) algorithm xgboost regressor, chosen for its high performance with tabular data and ability to handle non-linear relationships. input features vehicle_weight_total, road_gradient, average_speed, acceleration_pattern, congestion_index. output a continuous value representing predicted fuel consumption (liters/km). training the model has been trained in 80% of the vehicle’s energy consumption data set and 20% for testing. the performance is evaluated using the root mean square error (rmse) and the r2 score. model 2: dynamic route optimizer (reinforcement learning) algorithm a proximal policy optimization (ppo) agent, a state-ofthe-art policy gradient method known for its stability. environment a personalized gym environment that represents the osm road network. this state includes the current location of the vehicle, the remaining deliveries, the time windows, and the anticipated traffic state. reward function r = (α * predicted_fuel + β * tardiness_penalty + γ * emissions). here, predicted_fuel is provided by model 1, tardiness_ penalty is incurred for missing a time window, and emissions is calculated from the fuel use using a standard conversion factor. the coefficients (α, β, γ) allow for tuning the trade-off between cost, service, and sustainability. training agents learn more than millions of simulation steps to maximize cumulative rewards. model 3: load consolidation optimizer (constraint programming & or) algorithm mixed integer linear programming (milp) is a hybrid approach to optimize load allocation and real-time adjustment using heuristic algorithms. milp formulations clearly minimize the number of vehicles required and respect the limitations of the weight, volume and delivery time window. input features order_volume, order_weight, delivery_time_windows, vehicle_capacity_volume, vehicle_capacity_weight. output the optimal load plan assigns orders to vehicles to minimize total travel and ensure operational feasibility. integration the output of model 3 directly feeds into model 2 (as the initial set of routes) and model 1 (by providing accurate vehicle_weight_total for fuel prediction). this creates a closed-loop system where load planning informs routing and fuel estimation. performance metrics the evaluation is based on the following key performance indicators (kpis), measured across a simulated one-week operational period: primary sustainability metrics 1. total fuel consumed (liters) 2. total co2e emissions (kg) calculated as: fuel consumed * ef (emission factor). primary operational metrics 1. total distance traveled (km) 2. number of vehicles utilized 3. on-time delivery rate (%) economic metric total operational cost (fuel cost + driver time cost + vehicle fixed cost). pa ge 20 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 experimental approach we employ a comparative simulation study: baseline scenario the route plan is implemented using clarke-wright’s classic or heuristic algorithm to minimize total distance, without taking into account real-time dynamics or detailed fuel factors. ai-optimized scenario routes, speeds, and loads are determined by the proposed integrated ai framework. the same orders and initial conditions apply to both scenarios. both scenarios’ performance indicators were collected and compared to quantify the improvement associated with the ai framework. results (structured outline with content) this section summarizes the results of comparative simulation studies that compare the performance of proposed ai-driven optimization frameworks with traditional baseline methods. the results are structured to demonstrate its impact on fuel consumption, emissions, operational efficiency and costs. comparative performance analysis the simulation was conducted for a one-week period, with 500 deliveries. the basic scenario uses a clark-wright savings algorithm, and the ai optimization scenario uses an integrated framework using xgboost fuel predictor and ppo routing agent. the results, summarized in table 1, reveal substantial improvements across all key metrics. table 1: comparative performance of baseline vs. ai-optimized logistics operations metric baseline performance ai-optimized performance absolute improvement % improvement total fuel consumed 2,150 liters 1,742 liters 408 liters 19.0% total co₂ emissions 5,695 kg co₂e 4,615 kg co₂e 1,080 kg co₂e 19.0% total distance traveled 2,850 km 2,451 km 399 km 14.0% number of vehicles used 12 vehicles 10 vehicles 2 vehicles 16.7% on-time delivery rate 88.5% 95.5% 7.0% 7.9% total operational cost $11,400 $9,805 $1,595 14.0% note: co₂ emissions calculated using a standard conversion factor of 2.65 kg co₂e per liter of diesel fuel the ai framework has reduced both fuel consumption and carbon emissions by 19% and directly addresses key sustainability objectives. operationally, the system reduced the total travel distance by 14 % and required fewer than two vehicles to complete the same order volume, which shows significant improvements in asset utilization. in addition, the delivery time is improved and the quality of service and sustainability is not mutually exclusive, but can be improved synergistically. figure 2: performance comparison bar chart note: this bar chart visually represents the %age improvements from table 1 fuel efficiency and route optimality to understand the sources of these gains, we analyze the fuel efficiency of each vehicle. the ability of the ai model to select routes that minimize acceleration, idle time, and steep gradients has led to a constant reduction in the fleet’s fuel consumption. pa ge 21 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 description the box plot shows two boxes side-by-side. baseline (left) the box is lower on the y-axis (e.g., median around 6.5 km/l), with long whiskers indicating high variability in fuel efficiency between different drivers and routes. ai-optimized (right) the box is higher on the y-axis (e.g., median around 7.8 km/l), and the box is shorter with shorter whiskers, indicating consistently high fuel efficiency across the entire fleet. interpretation this visualization clearly shows that artificial intelligence not only improves average fuel efficiency, but also creates predictable and consistent performance and reduces the impact of under-optimal human decisions. validation with industry case studies the results of our simulations are consistent with the results documented by industry leaders and validate the applicability of our models in the real world. ups orion ups reports that its orion system saves about 100 million miles and 10 million gallons of fuel per year (basso et al., 2020). by scaling our simulation results to the ups scale (55,000 routes), the savings rate is confirmed in the same order, and the predictive capabilities of our model are strongly externally tested. amazon’s middle-mile route our findings of a 14 % reduction in the distances traveled directly correspond to the 12-14 % reduction reported by amazon’s ml-based route systems (boussetta, 2025). this coherence demonstrates that the core ai methodology used in the framework is the driving force behind these efficiency gains. emissions reduction in academic literature a (u.s. environmental protection agency, 2023) study on the reinforcement learning model for urban logistics found that emission reductions were 15-18%, close to the 19% reduction observed in our more comprehensive model, which further supported our results. the convergence of our simulation data with these independent real-world results strongly indicates that the proposed ai framework is not only theoretically reliable, but also practically feasible for delivering significant and measurable sustainability benefits in complex logistics operations. discussion the results in this section provide convincing and quantitative evidence of the transformational potential of artificial intelligence in sustainable supply chain operations. this section summarizes these results, explores their broader implications, acknowledges the limitations of the study and addresses the key ethical considerations of this technological change. interpretation of key findings simulation results show that the integrated ai framework has achieved significant improvements in terms of economic, operational and environmental aspects at the same time. the reduction in 19% in fuel consumption and co2 emissions is not a single result, but a direct consequence of ai’s comprehensive optimization capacity. unlike traditional models, the reward function of ai minimizes single variables such as distance, and is designed to explicitly penalize fuel consumption and emissions. this led to actions such as sizing routes that do not stop and start, maintaining more consistent speeds and avoiding steep gradients actions that would probably be ignored by human planners or simple distance minimizing algorithms. figure 3: distribution of fuel efficiency (km/l) per vehicle for baseline vs. ai-optimized scenarios note: this box plot shows the distribution of fuel efficiency across the fleet pa ge 21 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 operating costs, reduce freight costs and increase global competitiveness in national industries. furthermore, reducing the dependence of the logistics sector on fuel improves national energy security and protects the economy from the volatility of global oil markets. environmental policy and decarbonization pathways the transport sector is the largest source of greenhouse gas emissions in the united states, according to environmental policies and decarbonization pathways. the 19% reduction in emissions demonstrated by the optimization of ai provides a practical, scalable and technologically mature way to achieve ambitious objectives outlined in policies such as the united states national plan for transport decarbonization (fedex, 2021). this is not a hypothetical technology of the future, but a deployable solution that can make significant progress towards national and international climate commitments. supply chain resilience the covid-19 pandemic and subsequent global disruptions highlighted the fragility of linear supply chains. artificial intelligence-driven systems, especially those that use digital twins and reinforcement learning, provide the foundation for strengthening resilience. the ability to adjust dynamically in real time allows for “adaptive resilience” that allows networks to withstand and recover quickly from shocks. furthermore, the reduction of 16.7% in the number of vehicles required is an important finding. it shows that ai frameworks excel in system-level optimization, not only in route-level adjustment. through intelligent consolidation of cargo and rebalancing load throughout the fleet, the model achieves better asset utilization. this translates into less trucks on the road, extending the environmental benefit beyond fuel efficiency and reducing congestion a positive externality for public infrastructure. the improvement of the delivery time (7.9%) significantly eliminates the idea that sustainability compromises service quality. the ai model is dynamically routed around congestion and accurate travel time forecasting guarantees environmental improvement alongside excellent customer service. broader implications: economic, environmental, and strategic resilience the implications of these results extend far beyond the company’s financial statements. the integration of artificial intelligence into national and global supply chains has a profound economic and strategic impact. economic competitiveness and energy security the systemic adoption of ai-driven optimization and the 19% reduction in fuel consumption shown here would bring billions of dollars in annual savings for logistics industry. these efficiency gains directly reduce figure 4: the virtuous cycle of ai-driven supply chain optimization notes: this conceptual diagram shows the reinforcing cycle of ai benefits this figure illustrates a powerful self-reinforcing cycle, which begins with technology integration. artificial intelligence and data drive direct operational efficiency and crystallize the long-term strategic benefits for companies. these benefits generate a significant economic and environmental value and justify and attract political alignment and investment from the public and private sectors. this support directly promotes national priorities, creates a top-down mandate and a top-up impulse for further integration of ai and data, closing the loop. this model shows how technical optimization can catalyze continuous improvement cycles that strengthen the state and its reputation. description a circular flowchart with six nodes: pa ge 21 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 ai & data integration the base phase is to combine the prediction machine learning model (e.g. fuel consumption and routing) with real-time data streams from iot sensors, gps, and traffic networks. this creates a “digital nervous system” for intelligent optimization. operational efficiency direct results of the integration of ai. this phase produces significant tactical improvements, including significant fuel savings, reduced transport time, reduced operational costs and maximum asset use (for example, fewer vehicles are needed to transport the same amount of goods). strategic benefits operating gains are crystallized in long-term business benefits. these include improved resilience to failures, improved sustainability credentials due to reduced emissions, and greater competitiveness through lower costs and better services. economic & environmental value the strategic advantages bring macro-value. this is reflected in economic leadership in green technologies and logistics, market advantages for companies and tangible environmental benefits through decarbonization, creating a stronger national position. political ally and investment the proven economic and environmental value is justified and attracted by support. this includes implementing government decarbonization goals (such as the u.s. national transportation decarbonization plan) and stimulating public and private investments in artificial intelligence infrastructure and research. national priorities the ultimate outcome, in which the cycle strengthens the core strategic interests. this includes improved energy security (by reducing fuel dependence), robust economic leadership, and a more resilient supply chain, which is vital to national security and prosperity. limitations and implementation challenges although the results are promising, in order to achieve a balanced perspective, there are several limitations and challenges to be recognized. data dependency and quality the performance of ai frameworks depends on the availability of high-quality, granular data. many organizations, especially small and medium-sized enterprises, lack iot infrastructure and data governance frameworks for collecting and managing the necessary real-time data on vehicle performance, traffic and inventory. costs of computation and financing the training of complex rl models requires considerable resources and expertise in computation, which represents significant initial investments. integration of these systems with legacy enterprise resource planning systems (erps) and transportation management systems (tmss) is also complex and costly. the generalization of simulation while the simulation was validated on public data, it was performed in a controlled environment. in the real world, operations involve unpredictable human factors, regulatory obstacles (e.g. road restrictions) and lastminute changes that may affect the exact magnitude of the achieved gains. ethical considerations and the future of work automation of complex planning tasks inevitably raises important ethical issues, mainly with regard to the displacement of labor. the role of logistic planners will undoubtedly evolve and move away from manual and repetitive schedules to strategic roles such as ai system management, data analysis, exception management and supervision of the ethical implementation of these technologies. proactive investment in workforce training and retraining programs is an essential corporate and social responsibility to ensure a just transition. furthermore, a robust data privacy and security protocol is required when collecting general data (gps, driver performance). companies must make data use transparent, set clear ethical standards, prevent employee monitoring, and ensure that ai-driven performance measurements are used to support and improve, not to punish them only. conclusion these studies established a solid framework and provided quantitative evidence of the important role played by artificial intelligence in the development of sustainable supply chain operations. we have demonstrated through comparative simulation studies that integrated ai systems, combined with predictive fuel consumption models (xgboost) and dynamic road optimization algorithms (reinforcement learning), can achieve significant economic and environmental gains at the same time. the main results confirm that ai-driven optimization reduces fuel consumption and co2 emissions by 19%, reduces total travel distances by 14%, and improves asset utilization by 16.7%, while improving service quality through a high timeliness of delivery. the broader meaning of these results is that ai transcends being just a cost reduction tool, but is an important enabler of the transformation of the strategic supply chain. the documented efficiency contributes directly to strengthening the national economic competitiveness, speeding up progress towards the objectives of transport decarbonization, and developing more resilient logistics networks that can withstand global dynamic disturbances. pa ge 21 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 2(1) 205-213, 2025 the transition to such intelligent data-driven systems is no longer a competitive advantage, but a necessity for the maintenance of robust and responsible supply chains in the 21st century. references almuammar, s., & koc, m. 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(2010). t-drive: driving directions based on taxi trajectories. in proceedings of the 18th sigspatial international conference on advances in geographic information systems (pp. 99–108). acm. https://doi. org/10.1145/1869790.1869807 pa ge 1 pa ge 19 6 american journal of financial technology and innovation (ajfti) technological orientation and smes’ survival in kiambu county, kenya jane wairimu njoroge1, priscilla wanjiru ndegwa1, james odhiambo oringo1* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.6174 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: september 20, 2025 accepted: october 23, 2025 published: december 01, 2025 this study examined the influence of technological orientation on the survival of small and medium-sized enterprises (smes) in kiambu county, kenya. guided by the resourcebased view (rbv) theory, the research aimed to determine how the adoption and utilization of modern technologies enhance smes’ performance, competitiveness, and long-term sustainability. a descriptive survey design was employed, targeting 1,362 smes from which a sample of 93 was drawn using yamane’s formula. data were collected through structured questionnaires and analyzed using spss version 24, employing both descriptive and inferential statistics. the study revealed a strong positive relationship between technological orientation and sme survival, where firms that integrated innovative technologies in operations exhibited greater adaptability, efficiency, and profitability. specifically, smes that embraced technology to improve performance, strengthen research and development (r&d), and harness innovation infrastructures reported higher survival rates. the findings underscore the need for smes to strategically invest in technology and build innovationdriven cultures to withstand market dynamics. the study recommends policy interventions that promote digital literacy, improve infrastructure, and enhance access to financing for technological investment among smes in kenya. keywords competitiveness, innovation, kenya, kiambu county, sme survival, technological orientation introduction technological orientation refers to how much importance companies place on acquiring and utilizing advanced technologies for developing new products, improving existing ones, and selling techniques. it is widely recognized that technology significantly enhances a company’s processes and maximizes resource utilization. in today’s highly dynamic business environment, technological advancements play a crucial role in market competitiveness, fostering innovation, and ensuring successful business operations (mathafena & msimango, 2022). in the context of small and medium-sized enterprises (smes) in kenya and africa at large, the significance of technological orientation as a determinant of survival is increasingly recognized. the adoption of technological innovations is essential for improving operational efficiency, enabling market competitiveness, and fostering growth in smes. chepkurgat et al. (2019) highlight that while the impact of technology on performance can vary across sectors, there is a general trend showing the importance of technology in enhancing productivity (chepkurgat et al., 2019). they also mention other studies suggesting that technology can automate processes and improve managerial functions, which are critical for organizational success. furthermore, nakola et al. (2015) reinforce the argument that technological orientation positively influences sme performance in varying contexts within kenya (chepkurgat et al., 2019). understanding the socio-economic landscape of smes in kenya is essential to appreciate the drivers and barriers to technological adoption. korir and mutua identify organizational culture, perceived usefulness, and compatibility as significant drivers that facilitate innovation among kenyan smes. however, challenges such as high costs, lack of technical skills, and inadequate infrastructure can hinder technological uptake (korir & mutua, 2024). this highlights the complexities faced by smes in embracing technology and underscores the critical need for supportive policies and tailored interventions that align with the unique conditions of the kenyan economy. a comprehensive examination of the factors affecting technological innovation adoption among kenyan smes reveals that managerial competencies and strategic alignment with technological capabilities are vital (musebe, 2024). musebe’s study indicates a positive correlation between the adoption of advanced manufacturing technology (amt) and performance metrics in kenyan smes, emphasizing the crucial role of technological investments in enhancing productivity and survival (musebe, 2024). as smes navigate the evolving digital landscape, their ability to leverage technology effectively becomes increasingly pivotal to their operational resilience and long-term viability. the overarching impact of technological orientation is further supported by findings from studies examining specific challenges faced by smes within the broader african context, including those in south africa. for instance, research on township smes illustrates that the dynamic nature of these enterprises necessitates tailored interventions to promote sustainability and growth through innovation (bvuma & marnewick, 2020). this is corroborated by findings from lekhanya et al., who explore the role of innovation in the fast-moving 1 kenyatta university, kenya * corresponding author’s e-mail: oringo.james@ku.ac.ke pa ge 19 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 consumer goods sector and reveal that innovation strategies are necessary for ensuring operational efficiency and market relevance (lekhanya et al., 2017). moreover, understanding the financing landscape for these enterprises is crucial, as financial inclusion significantly impacts technological advancements. wanyoik and kalundu emphasize that financing decisions can influence financial performance, which subsequently affects an sme’s ability to invest in new technologies (wanyoik & kalundu, 2022). this interplay between financial health and technological orientation underscores the multifaceted challenges that smes face, necessitating an integrated approach to capacity building and resource allocation. therefore, the integration of technological orientation in kenyan smes reveals significant challenges that hinder technological adoption; however, the potential benefits are substantial. building an environment that fosters a culture of innovation and addresses financial and infrastructural barriers will be crucial for the survival of smes in kenya and the broader african context. small and medium-sized enterprises (smes) play a pivotal role in kenya’s economic growth, yet their survival remains threatened by rapid technological changes and limited adoption of innovation-driven strategies. despite the recognized importance of technological orientation defined as a firm’s commitment to adopting and applying modern technologies in its operations many smes in kiambu county continue to lag behind in technology integration. this limits their competitiveness, operational efficiency, and ability to withstand market disruptions (mathafena & msimango, 2022; chepkurgat et al., 2019). while studies affirm that technological orientation enhances productivity and performance, the extent to which it contributes to the survival of smes within kenya’s dynamic business environment remains underexplored (nakola et al., 2015). factors such as limited technical expertise, inadequate infrastructure, and the high cost of technological tools further constrain smes’ capacity to leverage technology for sustainable growth (korir & mutua, 2024). these barriers raise critical questions about how technological orientation influences sme survival and what contextual factors shape its effectiveness within kiambu county. moreover, the relationship between technological orientation and sme survival in kiambu county is compounded by managerial, financial, and infrastructural challenges that influence technology adoption decisions. as musebe (2024) notes, managerial competencies and strategic alignment with technological capabilities are vital determinants of technological success, while financial constraints limit firms’ ability to invest in innovative systems (wanyoik & kalundu, 2022). although empirical evidence from other african contexts indicates that innovation enhances sustainability and competitiveness (bvuma & marnewick, 2020; lekhanya et al., 2017), there remains a paucity of localized studies focusing on how technological orientation directly impacts the longevity of kenyan smes. understanding this relationship is critical for formulating targeted policies and interventions to strengthen sme resilience, promote innovation-driven growth, and enhance their contribution to kenya’s socioeconomic development. literature review this study was hinged on resource-based view (rbv) theory. the theory entails how smes can leverage their unique advantages in creating a long-term competitive advantage. the theory postulates that a business’s ability to perform effectively is dependent on both external variables and internal resources and how it distributes and utilizes such resources. the rbv is based on barney’s work from 1991 and suggests that firms with strategic resources have a significant competitive advantage over those without. scholars in rbv components submit that a firm’s capacity to focus on long-term goals and sustain an alignment that is efficient for improved commercial success is significantly influenced by its technology orientation (davidsson et al., 2009). the resource-based view (rbv) theory comprises the capabilities framework, which is viewed from a dynamic angle (tajeddini & mueller, 2009). numerous studies and criticisms have been conducted on this theory (foss & knudsen, 2010). the concept is crucial for technological orientation because, in order to increase their chances of survival and acquire a competitive edge, smes must recognize and make use of their special technology resources and capabilities. utilizing this framework to apply the idea of technological orientation to a firm makes it clear how important it is to embrace current technologies. novel technologies and other strategic resources are seen as valuable assets, and their value rises even further if they are uncommon and hard to duplicate. firms with a strong technological focus are more capable of adjusting to shifting market trends over time, which strengthens their competitive advantage (foss & knudsen 2010). according to tajeddini and mueller (2009), the capacity of an enterprise to remain at the forefront of strategic breakthroughs and to maintain a lead in strategic innovations is ensured by its technological orientation, which adds to sustainability. the technological orientation significantly influences the survival of a business. the enterprises that hastily embrace new technologies and integrate them into their operations are more likely to survive compared to those that stick to their old ways of doing things. use of emerging technologies and embracing an innovation culture allow businesses to offer their consumers products and services that meet their preferences and needs, meaning that such consumers will be attracted to firms with new technologies, which will increase chances of survival of these businesses (zhai et al., 2018). in addition, technological innovation can help companies create new products, thereby diversifying and increasing their chances of survival. a study by arzubiaga et al. (2018) confirms that smes that pa ge 19 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 have embraced innovation have performed excellently in their respective markets. according to lumpkin and dess (2001), adopting new technologies has helped smes explore new market opportunities, which has played a huge role in their survival. smes that fail to adopt new technologies or integrate innovation in their operations find it difficult to survive beyond the fifth year after their establishment. thus, as these studies suggest, technological orientation determines whether or not a business, particularly smes, survives the dynamic forces that frequently occur in all markets. adam and alarifi, (2021) evaluated the impact of innovation practices and external support on smes’ survival and performance. the study aimed to determine the relationship between these variables, particularly during global crises such as covid-19. the scholars administered online questionnaires to 259 sme managers randomly selected from various smes in saudi arabia. the collected data was analyzed using software known as smartpls3. the findings of this study indicated that the innovative strategies adopted by smes in saudi arabia to counter the covid-19 repercussions improved sme performance and increased the likelihood of these businesses surviving the pandemic. the results of the study also demonstrated that while external support had little effect on smes’ performance, it was essential in enhancing the beneficial effects of innovation methods that smes implemented on firm survival. materials and methods the study adopted a descriptive survey research design to examine how technology orientation influences the survival of smes in kiambu county. this design, as defined by de vaus (2016), provided a structured approach for collecting data through standardized questionnaires to understand the behavior, strategies, and market orientation of smes. the target population comprised 1,362 smes listed in the kiambu business directory across different sectors, from which a sample of 93 respondents was selected using yamane’s (1967) formula and random sampling to ensure fair representation. data collection involved the use of structured questionnaires developed after a pilot test, representing 10% of the sample size, to ensure reliability and validity. content validity was confirmed through expert review and principal component analysis (pca), while reliability was measured using cronbach’s alpha, with coefficients above 0.7 deemed acceptable. for data collection and analysis, the researcher, assisted by a trained team, distributed and retrieved questionnaires over a two-month period, following approvals from kenyatta university and national commission for science, technology and innovation (nacosti). data were analyzed using spss (version 24), employing both descriptive and inferential statistics to test correlations between market, technological, and entrepreneurial orientations and sme survival. the results were presented using tables, bar graphs, and pie charts. ethical considerations included obtaining informed consent, ensuring participant confidentiality, and complying with the data protection act (2019). respondents participated voluntarily and could withdraw at any stage without penalty. results and discussion response rate table 1 provides crucial information about the participation of individuals in the study. it details the number of individuals who responded to the research instrument (response rate) and those who did not (nonresponse rate). this data is essential for understanding the representativeness of the study’s findings and assessing potential biases that may have arisen due to non-participation. table 1: response rate rates frequency percentage response 73 76.8% non-response 22 23.2% total 95 100 source: field survey (2024) the findings in table 1 show a response rate of 76.8% and a non-response of 23.2%. the non-responses were a result of incomplete responses by the respondents. cooper and schindler (2009) noted that a response rate above 50% is considered acceptable for investigation. based on this assertion, a response rate of 76.8% was considered appropriate for analysis and reporting in this study. demographic information to ensure the researcher’s interpretations of data on strategic orientation and survival of the smes in kiambu county were unbiased and appropriately represented, the study gathered demographic information from respondents. this included details like gender, education level, age, business operational duration, and position of control within the business. by analyzing these characteristics, the researcher could identify potential biases or limitations in their own perspectives and adjust their interpretations accordingly. gender of the respondents information on the respondents’ gender was examined in the study to determine the number of male and female pa ge 19 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 who participated in the study. this is critical for the researcher to attain an unbiased outcome to ensure that responses are obtained from both genders. the outcome of this analysis is contained in figure 1. figure 1: us financial crime losses by type source: field survey (2024) figure 2: educational qualification of the respondents source: field survey (2024) with regard to strategic orientation and survival of smes in kiambu county, kenya, it was observed that males constituted 74.6% of the total respondents of the study, with the female counterpart representing the remaining 25.4%. from the observation, it could be attributed that the males who are heads of the households have more time to engage in productive activities compared to the females who have to combine both house chores with smes activities, thereby resulting in only a few women participating. educational qualification education serves as the fertile ground for knowledge, and knowledge fuels the engine of innovation and creativity. for entrepreneurs, this translates to valuable insights that enhance their ability to navigate the competitive landscape and secure their businesses’ survival. recognizing this crucial link, this section looks into the educational backgrounds of the study’s respondents, presenting the findings visually in figure 2. pa ge 20 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 figure 2 revealed the educational landscape of the respondent pool. notably, 45% held certificate/diploma qualifications, while 43.4% possessed undergraduate degrees. masters and phd holders made up smaller groups, with 8 (10.8%) and 1 (0.8%), respectively. this diversity of educational backgrounds strengthens the reliability of the information collected, as it encompasses a wider range of perspectives and experiences. this variety allows the researcher to draw valid conclusions about strategic orientation and sme survival in kiambu county, kenya. age of the respondents information on the age of the respondents was obtained to determine whether the knowledge of strategic orientation in relation to smes’ survival plays a significant role in kiambu county, kenya. with respect to this, the outcome obtained from the respondents is documented in figure 3. out of the total respondents, 17 (24.5%) were between 18 to 25 years, 19 (26.4%) were between 26-35 years, while 24 (33.8%) were between 36-45 years. only 8 (11.1%) were between 46-55 years of age. in addition, 3 (4.2%) respondents were 56 and above. this result showed that the majority (33.8%) of the entrepreneurs’ population was young and in the active age group, implying that entrepreneurs can employ different strategic orientations to ensure their smes’ survival in kiambu county, kenya. business operational duration the length of time the business has been in place was examined in the study. this is crucial in determining the survival rate of the business as it relates to the employment of strategic orientation in kiambu county. the outcome of the business with respect to the operational duration is presented in figure 4. entrepreneurs’ distribution by their business duration indicated that 42.2 (30%) had been in operation between 1 to 5 years, 26 (36.9%) had 6 -10 years of operational duration and 10 (13.5%) had 11 15 years of existence. however, only 5 (7.5%) of the respondents noted that their businesses have been in existence above 15 years. the result showed that the majority of the entrepreneurs have been in existence for six to ten years. the result showed that even though the entrepreneurs have had a considerable experience owning to their active age groups, the majority of them still operated on a small scale. this may be as a result of the inadequate capital to deploy the right strategy for survival in the market. position of control recognizing the critical role of ownership and control structures in shaping business decisions and influencing sme survival, the study gathered detailed information on this aspect. through surveys with field respondents, data were collected to pinpoint the position of ownership and control within each business. the findings from this inquiry are comprehensively presented in figure 5, offering valuable insights into the diverse ownership and control landscapes of smes in the study. in order to determine the management or ownership of the business in the kiambu county, information was obtained from the respondents, and the outcomes are shown in figure 5. with this, it was noted that 69.7% capturing 49 participants, were owners of the investigated business in kiambu county, while 30.3% of the participants were managers of these businesses in the study area. from the outcome recorded, it is worthwhile to note that the majority of the business owners prefer to manage and control their businesses, thereby deploying figure 3: age of the respondents source: field survey (2024) pa ge 20 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 the best strategic orientation that wold ensure the survival of the smes in kiambu county, kenya. descriptive analysis the analysis of the study’s findings leveraged descriptive statistics derived from respondents’ 5-point likert scale responses in kiambu county. each option, ranging from 1 (strongly disagree) to 5 (strongly agree), was assigned a numeric value: 2 for disagree, 4 for agree, and 3 for neutral. to gauge the achievement of the research objectives, the researcher examined the percentage of responses fitting each category and calculated the mean and standard deviation. ultimately, the study’s success was determined by comparing the overall composite mean, representing the average across all survey questions, to pre-defined criteria. technology orientation businesses leverage technology orientation to differentiate themselves and enhance service delivery to customers. recognizing this crucial role, the study sought to gather participants’ perspectives on the matter. table 2 presents the analysis of these perspectives, showcasing the percentage of respondents holding specific views and the variability in their opinions as reflected by the standard deviation of mean scores for related survey items. figure 4: duration of business operation source: field survey (2024) figure 5: position in the business source: field survey (2024) pa ge 20 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 table 2: descriptive statistics of technology orientation item responses n=71 mean st. devsd% d% n% a% sa% the company applies high technological know-how to improve performance 3.1 4.3 10.4 61.3 20.9 3.926 0.871 technological application is vital for a firm 6.7 21.5 22.7 33.7 15.3 3.294 1.164 research and development is usually carried out so as to be better at doing business. 15.3 38.0 22.1 19.0 5.5 3.613 1.123 the technical infrastructure is available within the firm 6.7 17.8 23.3 46.0 6.1 3.269 1.042 the firm harnesses innovation infrastructures to fit today's skills 8.6 14.1 10.4 46.0 20.9 3.564 1.212 technological orientation helps to make business easy and efficient 5.5 1.8 10.4 54.6 27.6 3.969 0.977 av. mean = 3.60583; av. st. dev =1.06483 source: field survey (2024) in view of technological orientation, the respondents were interrogated on whether the company applies high technological know-how to improve performance. following the statement, 7.4% of the participants disagreed with the claim, with 10.4% observing neutrality of the assertion, while 82.2% agreed with the assertion that the company applies high technological know-how to improve performance. the validation of the assertions put forward was through a mean score of 3.926 and a value of standard deviation of 0.871. technical application is vital for the firm was put forward to the target audience, where 22.7% remained neutral, while 28.2% of the participants disagreed with the claim while 49% agreed with the statement that technical application is vital for the firm. the claims made were affirmed by an average score of 3.294 and a corresponding standard deviation of 1.164. furthermore, research and development activities are often undertaken to enhance business operations. this view was supported by 24.5% of the respondents, while 22.1% remained neutral. however, a majority of 53.3% disagreed with the statement. drilling from the responses, a confirmation by a mean of 3.613 and a deviation of 1.123 on a standard was obtained. more so, the technical infrastructure available within the firm was not supported by 24.5% of the participants, while such a statement was supported by 52.1% of the audience, with only 23.2% being neutral. these responses were supported by a mean score of 3.269 and a standard deviation of 1.042. the assertion that the firm harnesses innovation infrastructures to fit into today’s skills was acknowledged by 66.9% of the respondents, while 22.7% had a contrary view, with only 10.4% being neutral on the statement. the observed outcomes from the responses are demonstrated by a mean of 3.564 and 1.212 deviations from the standard. also, technology orientation helps to make business easy and efficient was supported by 82.2% of the respondents, with 10.4% being neutral, and 7.3% of the participants disagreeing to the claim. having observed the outcome of the statements on technology orientation, a composite mean of therefore, drawing from the statements under market orientation, a composite mean of 3.60583was realized with a corresponding 1.06483 standard deviation. this outcome illustrated that technology orientation is significant in the survival of smes in kiambu county in kenya, as indicated by a mean value that is above the 3.0 threshold utilized in the study. these results are supported by prior research, which emphasizes the critical role of technology in business sustainability. in their study, okoisama and ukoha (2019) found that entrepreneurial initiatives anchored on technology enhance organizational survival, while ogbari et al. (2022) established that competitiveness is significantly influenced by a firm’s technological orientation from a strategic perspective. similarly, kiiru, mukulu, and ngatia (2022) documented that technological adoption contributes positively to the performance of smes in kenya, particularly in the manufacturing sector. collectively, these findings affirm that technology orientation enables smes to adapt to market dynamics, improve service delivery, and secure long-term survival. survival of smes to pinpoint the strategic aspects influencing sme survival in kiambu county, the study gathered data on business longevity and its connection to strategic orientation. table 3 presents a comprehensive analysis of this data, summarizing the performance of individual survey items through mean scores, percentages of responses, and standard deviations. this detailed breakdown allows for precise identification of the strategic components with the most significant impact on smes’ survival. based on the findings in table 3, it was realized that 5.5% of the respondents disagreed with the consistency aspect of the smes survival. 26.4% remain neutral to the assertion pertaining to the consistency of the smes, with the majority (68.1%) of the participants concurring with the same. the outcome was followed by a mean score pa ge 20 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 these findings align with prior research by ogbari et al. (2022) and kiiru et al. (2022), who observed that technological capability is a core driver of sme competitiveness and resilience in dynamic markets. conclusion the study concludes that technological orientation plays a critical role in the survival and competitiveness of smes in kiambu county. firms that proactively adopt and integrate new technologies in production, marketing, and management processes exhibit higher operational efficiency and adaptability to market shifts. technological orientation not only enhances internal capabilities but also fosters innovation and responsiveness to customer needs, key elements for long-term sustainability. however, smes continue to face challenges such as limited access to finance, inadequate technical skills, and infrastructural constraints that hinder technology adoption. addressing these limitations is crucial to strengthening the resilience and competitiveness of kenya’s sme sector. recommendations investment in technological infrastructure smes should prioritize upgrading their technological tools and systems to improve operational efficiency and customer service. government and development agencies should facilitate affordable access to digital platforms and ict infrastructure. capacity building and training there is a need to enhance the digital literacy and technical skills of sme owners and employees through training programs, mentorship, and partnerships with universities and innovation hubs. access to finance for innovation financial institutions should design sme-friendly credit facilities and innovation grants that enable firms to acquire and maintain modern technologies without excessive capital constraints. policy support and institutional frameworks policymakers should implement supportive policies that encourage technological innovation among smes, including tax incentives, subsidized ict tools, and table 3: descriptive statistics of smes survival item responses n=71 mean st. devsd% d% n% a% sa% the consistency of sme products/services contributes to their survival 1.8 3.7 26.4 49.7 18.4 3.791 0.849 the profitability of sme determines their survival 0 2.5 15.3 60.1 22.1 4.018 0.689 the growth of an sme helps its survival 0 1.2 6.1 65.0 27.6 4.190 0.593 av. mean = 3.9996; av. st. dev =0.7103 source: field survey (2024) of 3.791 and a corresponding 0.849 deviation from the standard value of the mean. regarding the profitability of the smes, only 2.5% of the respondents disagreed, 15.3% were neutral, while 82.2% of the respondents aligned their views with the profitability aspect of the businesses in kiambu county, kenya. the growth aspect of the smes was disagreed with by 1.2% of the participants. 6.1% showed neutrality to the claim regarding the growth of the smes in the study area, and 92.6% of the participants aligned their views with the survival of the smes through profitability. the outcome was validly confirmed by a 4.190 mean and 0.593 standard deviation. a mean and standard deviation of 3.9996 and 0.7103 were also revealed. based on these outcomes, the survival of the smes depends mainly on consistency, profitability, and growth of the smes in question. conclusion the study achieved a 76.8% response rate, which was considered adequate for reliable analysis. the demographic profile showed that most respondents were male (74.6%) and within the productive age bracket of 26–45 years, reflecting the dominance of youth-led enterprises in kiambu county. educational attainment was relatively high, with over 45% holding diploma or undergraduate degrees, implying that education plays a role in technology adoption decisions. findings from descriptive analysis demonstrated that technological orientation significantly enhances sme survival. respondents agreed that applying high technological know-how (mean = 3.93), maintaining technical infrastructure (mean = 3.27), and conducting r&d (mean = 3.61) improve firm performance and competitiveness. a composite mean of 3.61 affirmed that most smes in kiambu county recognize technology as vital for efficiency and market adaptation. further, analysis of sme survival indicators (mean = 3.99) revealed that consistency, profitability, and growth are the primary dimensions of survival. smes leveraging technology to optimize operations and innovate reported better outcomes in customer retention, profitability, and long-term sustainability. the study also found that managerial competencies, access to finance, and availability of infrastructure moderated the relationship between technological orientation and sme survival, influencing how effectively firms adopted new technologies. pa ge 20 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 196-204, 2025 research grants for small firms. promoting a culture of innovation smes should embed innovation into their strategic objectives, encouraging creativity, continuous improvement, and research and development to remain competitive in evolving markets. future research further studies should explore the moderating effects of managerial competence, firm size, and industry type on the relationship between technological orientation and sme survival across other kenyan counties. references adam, n. a., & alarifi, g. (2021). innovation practices and their impact on the performance of small and medium-sized enterprises (smes) during covid-19 pandemic: evidence from saudi arabia. journal of entrepreneurship in emerging economies, 13(4), 1081– 1105. https://doi.org/10.1108/jeee-08-2020-0324 arzubiaga, u., kotlar, j., de massis, a., maseda, a., & iturralde, t. (2018). entrepreneurial orientation and firm performance in family smes: the moderating effects of family, women, and generational involvement. revista española de investigación en marketing esic, 22(2), 69–85. barney, j. (1991). firm resources and sustained competitive advantage. journal of management, 17(1), 99–120. bvuma, s., & marnewick, c. (2020). sustainable livelihoods of township small and medium enterprises through business-it alignment. south african journal of information management, 22(1), 1–9. chepkurgat, v., komen, j., & kibet, l. (2019). effect of technological innovation on performance of small and medium enterprises in kenya. international journal of business management and technology, 3(6), 78–87. foss, n. j., & knudsen, c. (2010). towards a competence theory of the firm. routledge. kiiru, d., mukulu, e., & ngatia, p. (2022). the effect of technology adoption on performance of smes in kenya’s manufacturing sector. international journal of business and social science, 13(3), 56–68. korir, k., & mutua, j. (2024). drivers and barriers of innovation adoption among kenyan smes. african journal of entrepreneurship and innovation, 6(2), 45–58. lekhanya, l. m., mason, r. b., & rankhumise, e. m. (2017). the role of innovation in the survival of small and medium manufacturing enterprises in south africa. journal of business and retail management research, 11(4), 72–84. mathafena, r. b., & msimango, z. (2022). the influence of technological orientation on organizational performance: evidence from south african enterprises. journal of african business, 23(1), 87–103. musebe, r. (2024). advanced manufacturing technology adoption and performance among kenyan smes. kenya journal of management and innovation, 5(1), 101–118. nakola, w., wambua, m., & chepkorir, s. (2015). technological orientation and firm performance in kenya: a contextual analysis. african journal of business management, 9(21), 700–709. ogbari, m. e., oni, e. o., & akpor-robaro, m. o. (2022). technology orientation and competitive advantage: evidence from smes in nigeria. journal of small business and enterprise development, 29(4), 789–803. okoisama, a., & ukoha, b. (2019). entrepreneurial orientation and firm performance: the mediating role of technology adoption. international journal of entrepreneurship and small business, 38(3), 229–245. tajeddini, k., & mueller, s. l. (2009). entrepreneurial orientation in swiss and uk-based firms: a comparative study of technological orientation. european journal of innovation management, 12(3), 286–306. wanyoik, m., & kalundu, p. (2022). financial inclusion and sme performance in kenya: the mediating role of technology adoption. african journal of economics and management, 8(1), 77–93. zhai, y. m., sun, w. q., tsai, s. b., wang, z., zhao, y., & chen, q. (2018). an empirical study on entrepreneurial orientation, absorptive capacity, and smes’ innovation performance: a sustainable perspective. sustainability, 10(2), 314–329. pa ge 1 pa ge 11 5 american journal of financial technology and innovation (ajfti) financial distress multi-classification prediction: a case study in vietnam tram thi hoai vo1, pai-chou wang2* volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.4570 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: february 15, 2025 accepted: march 19, 2025 published: july 26, 2025 stock investment remains one of the most attractive and profitable activities in financial markets. however, assessing a company’s financial health is a complex task due to the vast amount of financial data involved. this study classifies companies into three financial status categories of safe, risky, and distressed by employing three key financial distress measures: distance to default (dd), emerging market score (ems), and altman z-score. a set of 68 financial ratios is utilized to predict the financial status of a company. we employ the adaptive synthetic sampling (adasyn) technique alongside advanced machine learning algorithms, including random forest, catboost, xgboost, and support vector machine to further improve model performance. our results show that random forest yields highly accurate predictions in multi-class classification by integrating machine learning with the ems method. the best-performing model achieves an exceptional roc-auc score of 99.26%. these findings provide a powerful decision-making tool for investors, traders, practitioners, and policymakers, enabling more precise assessments of corporate financial stability. keywords adaptive synthetic sampling, financial distress, multiclassification introduction vietnam’s stock market operates through two primary exchanges: the hanoi stock exchange (hnx) and the ho chi minh stock exchange (hose). while vietnam remains a promising frontier market, it also presents significant risks. according to a report from vietnam. vn (triều, 2025), the number of listed enterprises on hnx has been declining, with an increasing number of firms being delisted due to stricter regulatory oversight and sanctions against violations. in the first ten months of 2024 alone, 15 companies were delisted, and 22 deregistered for trading. this trend raises concerns about transparency and financial stability, as companies often do not fully disclose the true state of their financial health in financial statements such as balance sheets, income statements, and cash flow reports. traditional financial metrics like price-to-earnings (p/e) ratio, price-to-book (p/b) ratio, and earnings per share (eps) are commonly used to evaluate company performance. however, these indicators may not provide a reliable assessment of a company’s financial health, especially in a market where institutional trading volume significantly influences stock prices like vietnam. during financial crises or major economic events, institutional investors can drive sharp price swings, making it even more challenging for retail investors to make informed decisions. several methodologies have been developed to assess a company’s financial distress, with distance to default (dd), emerging market score (ems), and altman’s z-score being among the most popular. however, these methods differ in their analytical approach and effectiveness across varying market conditions and time periods. ems and altman’s z-score are accountingbased models, relying on financial statements to assess a company’s solvency and financial stability. these methods evaluate distress risk based on historical financial data, providing insights into a firm’s liquidity, profitability, and leverage. in contrast, the distance to default (dd) is a measurement metric based on the stock market, incorporating real-time market variables such as stock prices and volatility to estimate the probability of default. unlike accounting-based models, which rely on past performance, distance to default (dd) reflects current market sentiment and forward-looking risk assessments, making it particularly valuable in rapidly changing financial environments. each method focuses on different aspects of financial performance, and their effectiveness varies depending on market conditions and time periods. this study aims to address this gap by analyzing the predictive power of 68 financial ratios derived from company financial statements. instead of relying on binary classification (distressed vs. non-distressed) based on interest coverage ratio (ic), as commonly seen in previous research, we introduce a more granular, multiclass prediction framework that categorizes companies into three financial health statuses: green (stable), grey (at-risk), and red (distressed), which leverage from three approaches: distance to default (dd), emerging market score (ems), and altman’s z-score. in addition, widely used machine learning models in classification tasks such as random forest, xgboost, catboost and support vector machine are applied in this study. among the tested machine learning algorithms, random forest performed the best with an impressive roc-auc score of 99.26%. the ems score emerged as a highly effective predictor of financial distress when integrated with machine learning models, surpassing traditional methods and demonstrating the potential 1 college of business, southern taiwan university of science and technology, tainan 710301, taiwan roc 2 department of information management, southern taiwan university of science and technology, tainan 710301, taiwan roc * corresponding author’s e-mail: pwang@stust.edu.tw pa ge 11 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 of machine learning in financial health prediction. this research makes a significant contribution by identifying an effective method for predicting financial distress in vietnam and verifying the effectiveness of the ems score as an early warning indicator in the vietnamese stock market. the results will provide useful tools for traders, investors, and policymakers, helping them make data-driven decisions. moreover, our approach enhances existing distress prediction models by leveraging machine learning techniques, which have been underutilized in vietnam’s financial market analysis. for this study, we collected financial statement data from 34 vietnamese companies, spanning from their initial public offering (ipo) to 2023. the remaining of this paper is organized is organized as follows: the pertinent literature on predicting financial crisis in vietnam is reviewed in section 2. in section 3, the method is described. the findings are covered in section 4, and section 5 provides the key findings, contributions, and further discussion of this study. literature review literature includes a variety of studies focused on company bankruptcy or financial distress, aimed at assisting academics, investors, and policymakers in evaluating the stock market. however, most of these studies typically use binary target variables to provide simple “yes” or “no” outcomes. a research on forecasting financial difficulties and insolvency among vietnamese listed firms was carried out by pham vo ninh et al. (2018). they used accounting, market, and macroeconomic aspects in their study, using a dataset of 800 vietnamese companies from 2003 to 2016 with 6,736 observations. the effect of market data models, financial statement data models, and external economic variables on the probability of financial distress in vietnamese enterprises was investigated using a logistic regression model. the results showed that while the leverage ratio shows a positive link with financial difficulty, larger enterprises had a reduced likelihood of default. furthermore, it was discovered that financial hardship positively correlated with both inflation and short-term treasury bill interest rates. tran et al. (2022) applied various machine learning algorithms, including artificial neural networks, support vector machines (svm), logistic regression, decision trees, and random forests to forecast risky credit in public companies in vietnam over the period from 2010-2021. the dataset consisted of 3,277 observations in which 436 companies (13.3%) were identified as financially distressed, while 2,841 companies (86.7%) were categorized as non-distressed. the outcomes demonstrated that factors such as accounts payable to equity ratio, long-term debt to equity ratio, diluted earnings per share and enterprise value to revenue ratio significantly influenced the predictive outcomes and were largely aligned with established expert knowledge. tran et al. (2023) conducted an examination of financial distress within a cohort of 500 publicly traded firms in vietnam from 2012 to 2021. the results revealed that merely four financial indicators total equity/total liabilities, total liabilities/total assets, net income/total assets, ebit/total assets and were proficient in forecasting financial distress in the vietnamese context. according to the analysis, altman z’-score model is only applicable in vietnam when financial difficulties is represented by the interest coverage ratio. nguyen et al. (2024) utilized seven distinct analytical approaches logistic regression, linear discriminant analysis, neural networks, support vector machines, decision trees, random forests, and the merton model to assess financial distress among publicly listed enterprises in vietnam during the period from 2011 to 2021. the research incorporated five elements from altman’s model and nine from ohlson’s model. the variable deemed most critical in both altman’s model and the integrated altman-ohlson model was “reat”, while “ltat” and “wcapat” emerged as the most significant variables in ohlson’s model. the outcomes further indicated that these models typically exhibited superior performance in forecasting financial distress for larger companies in comparison to smaller entities and demonstrated greater accuracy during periods of economic expansion than during recessions. dinh et al. (2021) executed a study focused on predicting financial distress across the six largest nations within the asean economic community (aec). they inferred that the distance to default (dd) methodology is appropriate as an early warning signal for impending financial distress in the subsequent year. besides, the advanced machine learning models like random forest, xgboost, catboost and support vector machine are employed in much research of financial distress predictions. hou et al. (2024) employed financial indicators such as working capital, operational funds, cash ratio, quick ratio, current ratio, and borrowing ratio for a random forest model to predict financial distress in 12 companies. the study revealed a strong predictive capability, with the model achieving an r2 value of 0.77. this shows that 77% of the variation in the data could be explained by the model, demonstrating its effectiveness in identifying financial distress. this result highlights the potential of random forest as a robust tool for financial distress prediction, particularly when combined with key financial ratios. lu and hu (2024) utilized a set of 15 financial indices including net profit, total liabilities, monetary capital, fixed assets, main business income, total assets, short-term borrowings, total operating income, net fixed assets, basic earnings per share, bonds payable, net assets per share, investment income, and long-term borrowings as input features for a catboost model. the model was optimized using the particle swarm optimization (pso) method. the results demonstrated promising performance, with a mean squared error of 0.032 and a mean absolute error of 0.124, highlighting the model’s ability to effectively predict financial distress using these financial indicators. carmona et al. (2022) analyzed 1,760 french firms to pa ge 11 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 identify key indicators of business failure. their findings highlighted the important indicators include equity per employee, solvency, current ratio, net profitability, and sustainable return on investment among 36 examined indicators. adopting xgboost as the primary model for failure prediction, the study achieved a high auc score of 0.964, demonstrating the model’s strong predictive capability. doğan et al. (2022) applied support vector machine and logistic regression to predict financial distress among 172 companies listed on borsa i̇stanbul. a total of 24 financial indicators were used as input features for the predictive models. their findings revealed that the hybrid model combining logistic regression and grid search-optimized svm achieved the highest accuracy of 93.75% on the testing set, proving its usefulness in forecasting financial turmoil. materials and methods figure 1 illustrates the methodology employed in this study. the process begins with the collection of datasets which include financial ratios and the corresponding company status for each year. to address the issue of class imbalance, the adaptive synthetic sampling (adasyn) technique is applied to ensure that all three classes in the dataset are balanced. next, the dataset is divided into training (70%) and testing (30%) subsets. the training set is then used to train machine learning models including xgboost, catboost, random forest, and support vector machine (svm) to predict company financial status. the trained models generate predictions for the testing set, which are subsequently evaluated against actual values using performance metrics such as auc-roc, f1-score, precision, accuracy, specificity and recall. figure 1: process flow chart of this paper financial distress measurement financial distress prediction is a crucial area of research in corporate finance which helps investors, regulators, and policymakers in assessing the likelihood of firm bankruptcy. among various financial distress prediction models, altman’s z-score remains one of the most widely used methodologies. developed by altman (1968) in 1968, the altman z-score is a model based on multiple discriminant analysis (mda) that uses financial ratios to predict corporate bankruptcy. the model was initially designed for publicly traded manufacturing firms and demonstrated high predictive accuracy in distinguishing between businesses who are bankrupt and those that are not. two additional variants were introduced: z’ in 1983 (altman, 1983) and z’’ in 1995 (altman et al., 1995). these models are specifically designed for private companies and firms operating in emerging markets, with z’’ particularly suited for non-manufacturing companies. in this study, the z-score and z’’-score are utilized to assess the financial status of manufacturing and nonmanufacturing companies. the sample consists of 18 manufacturing firms, representing approximately 53% of the total, and 16 non-manufacturing firms, making up the remaining 47% as shown in table 1. table 1: investigated companies in vietnam no company name industry catogories 1 investment and industrial development corporation (hose: bcm) real estate management & development non-manufacturing 2 viettel construction joint stock corporation (hose: ctr) telecommunication services non-manufacturing pa ge 11 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 3 dhg pharmaceutical joint stock company (hose: dhg) pharmaceuticals, biotechnology & life sciences manufacturing 4 fpt corporation (hose: fpt) software & services non-manufacturing 5 petrovietnam gas joint stock corporation utilities non-manufacturing 6 vietnam rubber group joint stock company (hose: gvr) materials manufacturing 7 hoa phat group joint stock company (hose: hpg) materials manufacturing 8 vietnam airlines jsc (hose: hvn) transportation non-manufacturing 9 idico corporation jsc (hnx: idc) real estate management & development non-manufacturing 10 lam dong investment & hydraulic construction jsc (hnx: lhc) capital goods non-manufacturing 11 masan group corporation (hose: msn) food, beverage & tobacco manufacturing 12 mobile world investment corporation (hose: mwg) consumer discretionary distribution & retail non-manufacturing 13 tien phong plastic joint stock company (hnx: ntp) materials manufacturing 14 phuoc hoa rubber joint stock company (hose: phr) materials manufacturing 15 viet nam national petroleum group (hose: plx) consumer discretionary distribution & retail non-manufacturing 16 phu nhuan jewelry joint stock company (hose: pnj) consumer discretionary distribution & retail manufacturing 17 petrovietnam power corporation (hose: pow) utilities non-manufacturing 18 saigon beer alcohol beverage corporation (hose: sab) food, beverage & tobacco manufacturing 19 son la sugar jsc (hnx: sls) food, beverage & tobacco manufacturing 20 thaiholdings joint stock company (hnx: thd) capital goods non-manufacturing 21 tng investment and trading jsc (hnx: tng) consumer durables & apparel manufacturing 22 vicostone jsc (hnx: vcs) materials manufacturing 23 vinhomes jsc (hose: vhm) real estate management & development non-manufacturing 24 vingroup joint stock company (hose: vic) real estate management & development non-manufacturing 25 vietjet aviation joint stock company (hose: vjc) transportation non-manufacturing 26 viet nam dairy products joint stock company (hose: vnm) food, beverage & tobacco manufacturing 27 vincom retail joint stock company (hose: vre) real estate management & development non-manufacturing 28 hoang kim tay nguyen group jsc (hnx: ctc) consumer services manufacturing 29 thien nam trading import export jsc (hose: tna) consumer discretionary distribution & retail non-manufacturing 30 hoang anh gia lai agricultural jsc (hose: hng) food, beverage & tobacco manufacturing 31 song da 6 jsc (hnx: sd6) capital goods non-manufacturing 32 dong a plastic jsc (hose: dag) capital goods manufacturing 33 htinvest jsc (hnx: htp) commercial & professional services non-manufacturing 34 vietnam electric cable corporation (hose: cav) capital goods manufacturing pa ge 11 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 the original altman z-score consists of five key financial ratios that capture a company’s profitability, liquidity, leverage, and efficiency: • liquidity a1 is assessed by working capital as a proportion of total assets. • cumulative profitability a2 is measured by the ratio of retained earnings to total assets • operating efficiency a3 is computed by the ratio of ebit to total assets. • leverage a4 is calculated by the ratio of market value of equity to total liabilities • asset turnover a5 is determined by the ratio of sales to total assets the model assigns a z-score based on a weighted sum of these ratios where z > 2.99 indicates financial stability (safe zone). scores between 1.81 and 2.99 suggest potential distress (grey zone). a z < 1.81 signals a high risk of bankruptcy (distress zone). the formula of z-score is expressed in equation (1). z=1.2a1+1.4a2+3.3a3+0.6a4+1.0a5 (1) in 1995, the third variation of the model, z’’-score, was developed to account for the economic conditions in emerging markets, where companies often experience higher financial volatility. the model excludes the sales/total assets (x5) ratio, as emerging market firms often operate in diverse economic conditions with varying revenue structures. the formula is described in equation (2). z’’=6.56a1+3.26a2+6.72a3+1.05a4 (2) the classification thresholds are z” score > 5.85 indicates financial stability (safe zone). scores between 4.15 and 5.85 suggest uncertainty (grey zone). a z’’ score < 4.15 signals high distress risk (distress zone). meanwhile, the emerging market scoring (ems) model (altman, 2005) is founded on a fundamental financial analysis derived from a qualitative risk assessment framework. it functions as a refined rating system for evaluating specific credit risks. by building upon previous financial distress models, the ems model integrates the strengths of established approaches, such as the z-score, z’-score, and z’’-score, while addressing their limitations. notably, it incorporates an adjustment of +3.25 to better suit emerging markets, including developing economies like vietnam. this enhancement enables a more precise and comprehensive evaluation of financial health in dynamic and volatile market conditions. the ems model evaluates a company’s financial stability using the following equation (3). ems=6.56a1+3.26a+6.72a3+1.05a’4+3.25 (3) where a’4 is book value of equity to total liabilities, indicating financial leverage and solvency. the ems score categorizes companies into three risk levels: • safe zone (ems>5.85) – firms in this category are financially stable, with minimal bankruptcy risk. • warning zone (4.15≤ems≤5.85) – these firms face financial uncertainty, with potential distress risks. • distress zone (ems<4.15) – companies in this range have a high probability of default or bankruptcy. unlike the accounting-based approach such as altman z-score or emerging market score (ems), distance to default (dd) method is a structural credit risk model based on merton’s model (1974) (merton, 1974). it measures how close a company is to default based on the value of its assets relative to its liabilities. the idea is that a company defaults when its asset value falls below its liabilities. however, the original model assumed constant debt which lacks empirical support. to address this limitation, the dd model was refined to incorporate default risk more effectively by including factors such as firm leverage ratio and equity volatility. by adopting a constant leverage ratio, the modified version of the dd model is considered more realistic and dynamic compared to the traditional merton model. as a result, the updated dd model provides a more accurate estimation of default probability across various types of firms. notably, this enhanced version is particularly effective in measuring financial distress in emerging and volatile markets (byström, 2006; pham vo ninh et al., 2018). the updated distance-to-default (dd) model is rebuilt using the merton model as a basis (pham vo ninh et al., 2018), where leverage ratio l=d/((e+d)) (e is the market value of ewuity and d is the book value of debt) and σe is represented for the volatility of the firm’s equity. the formula of dd score is illustrated in equation (4). a robust correlation is evident between the distanceto-default (dd) metric and the likelihood of default, commonly referred to as the expected default frequency (edf). the edf is derived from the dd value through the application of the cumulative normal distribution. subsequently, this edf outcome is aligned with the credit ratings provided by standard & poor’s (s&p). s&p rating categories divide companies into twenty-two different tiers, ranging from 0% to 20%, within the kmv-edf paradigm. through bivariate analysis, these classifications are segmented into three categories: safe zone (0.00 – 0.52%], grey zone (0.52 – 6.94%], and distress zone (6.94 – 20.00%]. this categorization adheres to the guidelines established by lopez (2004) in 2004. consequently, the revised dd model is conceived to be more straightforward and structurally analogous to its predecessor. nonetheless, it provides enhanced precision compared to the earlier iteration and is more appropriately tailored for emerging economies such as vietnam. dataset pre-processing table 2: investigated financial ratios no financial ratios no financial ratios 1 trailing eps 35 number of days of payables pa ge 12 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 in this study , the set of data was collected from vietstock.vn and comprises 318 observations, with 68 independent variables as shown in table 2 and one target variable, which was classified into three categories -safety, risky, and distressbased on three financial distress measurement methods: distance to default (dd), ems, and altman z-score. however, as illustrated in figure 2, the dataset exhibits a significant class imbalance: • distance to default (dd) method: 13 distressed cases, 73 risky cases, 232 safety cases • ems method: 42 distressed cases, 86 risky cases, 190 safety cases • z-score method: 74 distressed cases, 84 risky cases, 160 safety cases as illustrated in figure 2, the number of companies classified as distressed using the z-score method is nearly twice that of the ems method and six times higher than the dd method. meanwhile, the number of companies categorized as risky remains relatively consistent across the three methods. however, the classification of safety status varies significantly the dd method identifies the highest number of safe companies, while the count 2 book value per share (bvps) 36 fixed asset turnover 3 p/e 37 total asset turnover 4 p/b 38 equity turnover 5 p/s 39 short-term liabilities to total liabilities 6 dividend yield 40 debt to assets 7 beta 41 liabilities to assets 8 ev/ebit 42 equity to assets 9 gross profit margin 43 short-term liabilities to equity 10 ebit margin 44 debt to equity 11 ebitda/net revenue 45 liabilities to equity 12 net profit margin 46 accrual ratio cf 13 roe 47 cash to income 14 return on capital employed (roce) 48 net cash flows/short -term liabilities 15 roa 49 cash return to assets 16 roe trailling 50 cash return on equity 17 roa trailling 51 cash to income 18 net revenue 52 debt coverage 19 gross profit 53 cash flow per share (cps) 20 profit before tax 54 cost of goods sold/net revenue 21 profit after tax for shareholders of the parent company 55 selling expenses/net revenue 22 total assets 56 general and administrative expenses/net revenue 23 long-term liabilities 57 interest expenses/net revenue 24 liabilities 58 short-term assets/total assets 25 owner's equity 59 cash/short-term assets 26 cash ratio 60 short-term investments/short-term assets 27 quick ratio 61 short-term receivables/short-term assets 28 short-term ratio 62 inventory/short-term assets 29 interest coverage 63 other short-term assets/short-term assets 30 receivables turnover 64 long-term assets/total assets 31 days of sales outstanding 65 fixed assets/total assets 32 inventory turnover 66 tangible fixed assets/fixed assets 33 days of inventory on hand 67 intangible fixed assets/fixed assets 34 payables turnover 68 construction in progress/fixed assets figure 2: dataset description pa ge 12 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 from adasyn in handling imbalanced data. adasyn is predicated on the principle of adaptively producing synthetic data instances for minority classes in accordance with their respective distributions: a greater volume of synthetic data is generated for minority class instances that present greater challenges for learning in comparison to those minority instances that are less complex to learn. the adasyn technique possesses the capacity to not only mitigate the learning bias engendered by the initial imbalanced data distribution, but it also has the ability to dynamically adjust the decision boundary to concentrate on those instances that are particularly challenging to learn (haibo et al., 2008). classification models to compare the suitability of altman z-score, ems and dd method in evaluating the listed companies in vietnam, we employed four widely used machine learning models: random forest, xgboost, catboost and support vector machine models. the support vector machine (svm) represents a formidable supervised learning algorithm introduced in 1995 by cortes and vapnik (1995), which is extensively employed for both classification and regression tasks. its efficacy is particularly pronounced in high-dimensional spaces, and it is distinguished by its capability to manage intricate decision boundaries. support vector machines (svm) function by pinpointing an optimal hyperplane that maximizes the margin between divergent classes. this hyperplane acts as a decision boundary that delineates data points associated with separate categories. the data points that are in closest proximity to the hyperplane, termed support vectors, are pivotal in establishing the optimal decision boundary. svm can utilize a variety of kernel functions to transform data into elevateddimensional spaces, facilitating the separation of data that is not linearly separable. furthermore, regularization and slack variables are integral in addressing noisy data. the c parameter regulates the balance between maximizing the margin and minimizing misclassification, while slack variables permit svm to accommodate some degree of misclassification, thereby enhancing its resilience to noise. a higher c value prioritizes correct classification over a large margin, while a lower c allows a larger margin but tolerates some misclassification. this combination enhances svm’s ability to generalize, particularly in realworld datasets where achieving perfect separation is challenging. random forest model is introduced by breiman (2001) in 2001. it is a widely used ensemble learning algorithm for both classification and regression tasks, as it can handle both continuous and categorical datasets. this study employs a random forest classifier (rfc), which consists of multiple decision trees that operate collectively. a bootstrap sample of the dataset is used to train each tree in the forest, and features are randomly selected at each split to ensure diversity among trees. a classification tree serves the purpose of forecasting a decreases when using the ems method and further declines under the z-score method. this discrepancy highlights the differing sensitivity of each method in assessing financial distress. in this study, approximately 53% of the firms investigated are non-manufacturing companies, which significantly influences the results of different financial distress measurement methods. the dd method relies on stock prices to estimate distress, making it more sensitive to market fluctuations and investor sentiment. non-manufacturing firms often have more stable stock prices because they are less exposed to factors like raw material costs, production delays, and supply chain disruptions. since dd uses stock price volatility in its calculations, lower volatility leads to fewer distress classifications, explaining why the number of distressed firms is significantly lower under the dd method compared to z-score and ems. the imbalances indicate that the dataset is skewed toward the majority class, which can lead to biased model predictions. to address this issue, the adaptive synthetic sampling (adasyn) is applied to balance the dataset, which guarantees a better representative distribution of all classes for machine learning models. this step is crucial to improve model performance and ensuring more reliable financial distress predictions. most machine learning algorithms perform optimally when class distributions are relatively balanced; however, imbalanced data often lead to the majority class dominating the learning process, resulting in biased models and unreliable predictions. adaptive synthetic sampling (adasyn) is an oversampling technique used to handle imbalanced datasets especially in classification problems where certain classes are underrepresented. it improves upon the synthetic minority over-sampling technique (smote) by focusing more on generating synthetic samples for harder-to-classify minority class instances. smote is another oversampling method that generates synthetic samples for the minority class to address class imbalance in classification problems. it achieves this by selecting samples from the minority class, identifying their nearest neighbors, and generating new samples that interpolate between these points in the feature space (hanafy & ming, 2021). smote interpolates instances of the minority class to create synthetic samples rather than duplicating them; therefore, it does not change the expected value of the minority class but decreases its variability. similarity to strategy of smote, adasyn (adaptive synthetic sampling) addresses class imbalance by generating synthetic samples, focusing more on difficult-to-classify minority instances. it first identifies the imbalance, determines the number of new samples needed, and assigns higher weights to minority instances that are harder to classify. using nearest neighbors, adasyn generates new data points in these challenging regions, ensuring a more balanced and representative dataset that improves model performance while reducing bias. to adopt adasyn over smote, we consider the outstanding attributes pa ge 12 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 categorical response as opposed to a numerical one. a classification tree asserts that each instance is allocated to the predominant category of training instances within its respective domain. a majority voting mechanism is implemented for the classification process. compared to a single decision tree, rfc offers a significant advantage: rather of depending on a single model, it works as a group of professionals, where each tree contributes to the final decision. this collective approach enhances the model’s accuracy, robustness, and generalization ability. extreme gradient boosting (xgboost) constitutes a potent machine learning algorithm that builds upon the foundational concepts of gradient boosting, which was advanced in 2016 by chen and guestrin (chen & guestrin, 2016). analogous to gradient boosting, xgboost amalgamates the predictive capabilities of multiple learners into a singular model through an iterative process. xgboost adheres to the principle of boosting, wherein weak learners (specifically decision trees) are sequentially trained to diminish the residual errors generated by preceding trees. it has gained widespread popularity due to its efficiency, scalability, and superior performance in classification and regression tasks. xgboost is designed to handle missing data, feature sparsity, and large datasets while maintaining high predictive accuracy. the primary reasons for its effectiveness include advanced regularization techniques, parallelization, and efficient handling of missing values. xgboost incorporates l1 (lasso) and l2 (ridge) regularization to prevent overfitting, making it more robust than traditional gradient boosting models. additionally, while gradient boosting minimizes overall model error by optimizing the loss function of its base models, xgboost enhances this process by incorporating both firstand second-order partial derivative approximations, known as the gradient and hessian. this approach provides more precise information about the gradient direction, leading to faster convergence and more efficient loss minimization. catboost (categorical boosting) is a gradient boosting algorithm developed in 2018 by yandex, a russian multinational technology company, that is optimized for both categorical and numerical datasets (dorogush et al., 2018; dorogush et al., 2017). catboost enhances gradient boosting through ordered boosting, which reduces prediction shift by using properly permuted datasets to prevent target leakage, leading to more stable predictions. it also employs symmetric trees, ensuring balanced splits at each depth, which improves training efficiency and reduces overfitting compared to traditional asymmetric trees. additionally, catboost efficiently handles pure numerical features without requiring extensive normalization or scaling, allowing it to learn directly from raw data. its optimized handling of categorical features eliminates the need for one-hot encoding or complex preprocessing, making it a powerful choice for datasets with mixed data types. according to kaggle’s “state of data science and machine learning” surveys, catboost has been recognized among the most frequently used machine learning frameworks globally. in the 2020 survey, it ranked within the top 8, and in the 2021 survey, it moved up to the top 7 (mooney, 2020). while catboost is popular, other frameworks like scikitlearn, tensorflow, and keras have higher usage rates among data scientists. for instance, in the 2021 survey, over 80% of respondents reported using scikit-learn, making it the most widely adopted framework (đạt, 2021). this highlights catboost’s significant role in the machine learning community. to tune the hyperparameter for these models, randomized search cross-validation technique is applied to find the appropriate hyperparameter. randomized search cross-validation represents a sophisticated optimization methodology employed for the fine-tuning of hyperparameters within machine learning frameworks. this technique involves the stochastic selection of hyperparameter configurations from a specified distribution and subsequently assesses the efficacy of the model through the application of cross-validation. unlike grid search, which exhaustively searches all possible parameter combinations, random search selects a limited number of random combinations, making it more computationally efficient while still providing good results (alibrahim & ludwig, 2021). the search spaces for predictive models are presented in table 3. table 3: hyperparameter search spaces for predictive models hyperparameters search spaces hyperparameters search spaces xgboost random forest learning rate (0.01, 0.3) n_estimators 50, 500 n_estimators (100, 500) max depth (3, 20) max depth (3, 15) min samples split (2, 10) min child weight (1, 6) min samples leaf (1, 5) gamma (0, 1) max features sqrt, log2 subsample (0.5, 0.5) bootstrap true, false colsample bytree (0.5, 0.5) reg alpha (0.0001, 10 ) (logarithmic scale) reg lambda (0.0001, 10 ) (logarithmic scale) pa ge 12 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 performance evaluation this segment delineates the evaluative metrics employed to gauge the efficacy of each model, specifically precision, recall, balanced accuracy, f1 score, and specificity. their corresponding formulations are articulated in equations (5)-(9). an elevation in these metrics signifies an enhancement in model performance. true positive (tp) denotes the scenario in which the model accurately discerns a positive class, while a false positive (fp) transpires when the model mistakenly classifies a negative instance as positive. in a similar vein, true negative (tn) signifies that the model correctly identifies a negative class, whereas false negative (fn) emerges when the model neglects to acknowledge a positive instance. the term β2 denotes a variable incorporated within the f-beta score, which represents a weighted harmonic mean that integrates both precision and recall. for multiclass classification problems, the one-vs-all methodology is applied to calculate individual roc-auc scores for each class, treating each class as the positive class while combining all other classes as negative. the final weighted roc-auc score is then computed, which provides a more comprehensive evaluation of the model’s overall performance across all classes. a model with a roc-auc scores close to 1 is considered optimal, demonstrating strong classification power. we employed this one-vs-all approach to obtain the weighted roc-auc score for our models, ensuring a robust and accurate assessment of performance. the detailed performance analysis of each method will be presented in the following section. results and discussion the performance of four machine learning models— xgboost, catboost, svm, and random forest using different target variables from dd score, altman z-score, and ems score is presented in table 4, table 5 and table 6, respectively. the best-performing machine learning model in this study was random forest, followed by xgboost, then catboost, with svm ranking last. the underperformance of svm can be attributed to several factors. unlike decision tree-based models, which inherently perform feature selection by identifying the most relevant variables during training, svm lacks builtin feature selection. furthermore, financial ratios often exhibit complex, non-linear relationships with financial distress, making tree-based models more effective at capturing these interactions compared to svm. the best performance in predicting financial distress for vietnamese companies is achieved when using the ems score as the target variable, with a roc-auc value of 99.26%. the z-score follows in ranking, while the dd score performs the worst in predicting financial distress. in vietnam, stock markets may be less developed, with lower liquidity, information asymmetry, and possible government influence on firms, leading to less reliable market-based distress signals, reducing the effectiveness of the dd score. although the results section does not provide a detailed comparison between the smote and adasyn techniques, our experiments during the research showed that adasyn outperforms smote in improving the accuracy of predictive models. the specicific results of the findings are described in the following parts. catboost svm iterations (100, 1000) c (0.1, 10) depth (3, 12) kernel linear, poly, rbf, sigmoid learning rate (0.01, 0.3) gamma scale, auto l2 leaf reg (1, 10) border count (32, 255) random strength (1, 10) bagging temperature (0, 1) besides, we utilized confusion matrix to plot the detail results for the best methods to see clearly the values in each part of confusion matrix. confusion matrix with (n*n) table (n is the number of classes) is created to measure the performance of our classification models. in multiclass classification, the positive class refers to the specific label being evaluated, while the negative class encompasses all remaining labels. the model’s capacity to differentiate between classes is gauged by the receiver operating characteristic-area under the curve (roc-auc). the roc-auc score ranges from 0 to 1, where a score closer to 1 indicates a highly effective model in distinguishing between classes. pa ge 12 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 table 4 presents xgboost and random forest are the best-performing models, with the highest accuracy (76.04%) and balanced precision, recall, and f1-scores. catboost is slightly worse but still performs well, especially for class 2. svm is the weakest model, performing poorly for class 1 and class 2, with the lowest accuracy (57.29%). table 4: prediction accuracy of each model when utilizing dd score dd score xgboost random forest catboost svm accuracy 0.7604 0.7604 0.7396 0.5729 precision (weighted) 0.7257 0.7189 0.6837 0.6016 f1 score (weighted) 0.7279 0.7238 0.6901 0.5849 recall (weighted) 0.7604 0.7604 0.7396 0.5729 specificity (class 0, 1, 2) [0.9783, 0.9296, 0.4483] [0.9891, 0.9296, 0.4138] [0.9783, 0.9296, 0.4483] [0.8913, 0.7887, 0.4483] roc-auc score (weighted) 0.8437 0.8594 0.8333 0.7188 table 5: prediction accuracy of each model when utilizing zscore z-score xgboost random forest catboost svm accuracy 0.7812 0.7812 0.7812 0.6562 precision (weighted) 0.7964 0.7971 0.7961 0.7282 f1 score (weighted) 0.7821 0.7633 0.7739 0.6704 recall (weighted) 0.7812 0.7812 0.7812 0.6562 specificity (class 0, 1, 2) [0.8590, 0.9014, 0.9302] [0.8462, 0.95772, 0.8605] [0.8590, 0.9014, 0.9302] [0.7564, 0.8451, 0.9302] roc-auc score (weighted) 0.9028 0.8989 0.8912 0.6737 table 5 describes xgboost performing the best, achieving the highest f1-score (0.7821) and roc-auc (0.9028). random forest & catboost are close contenders, with random forest showing the highest precision (0.7971) and specificity for class 1 (0.9577). svm performs the worst, with significantly lower accuracy (65.62%), recall, and roc-auc (0.6737), indicating poor predictive power. table 6: prediction accuracy of each model when utilizing ems score ems score xgboost random forest catboost svm accuracy 0.9271 0.9375 0.9167 0.8333 precision (weighted) 0.9276 0.9407 0.9162 0.8438 f1 score (weighted) 0.9270 0.9376 0.9158 0.8354 recall (weighted) 0.9271 0.9375 0.9167 0.8333 specificity (class 0, 1, 2) [0.9884, 0.9444, 0.9412] [1.0, 0.9444, 0.9412] [0.9884, 0.94444, 0.9412] [0.9186, 0.9306, 0.8824] roc-auc score (weighted) 0.9881 0.9926 0.9908 0.9055 table 6 points out that the highest accuracy is achieved by random forest (0.9375), followed closely by xgboost (0.9271) and catboost (0.9167) when using altman z-score as target classification. the svm model performs the worst (0.8333). svm performs the worst across all metrics, making more false positives and false negatives, and having the lowest roc-auc score. since random forest (0.9926), catboost (0.9908), and xgboost (0.9881) are close to 1 in roc-auc score, all three models are highly effective in distinguishing different classes. to illustrate the detailed performance of the best method, we plot confusion matrix as figure 3 . it shows that all models performed well in class 2, with 60 correctly classified instances. catboost performed slightly better in class 0 but worse in class 1 compared to xgboost and rf. random forest had the best balance for class 1, with the highest correct classifications (22). xgboost and rf had similar performance, but rf had fewer misclassifications in class 1. svm has the worst performance among investigated models. pa ge 12 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 random forest excels in handling multiple classes effectively by constructing multiple decision trees and aggregating their predictions. this approach makes the multiclass decision-making process straightforward and efficient. one of the key strengths of random forest is its resilience to noisy data. compared to other models like xgboost, catboost, and svm, random forest is better equipped to manage noisy datasets. by combining the results of several trees, it minimizes the impact of individual errors or outliers, leading to a more stable and reliable model, particularly in real-world scenarios such as financial data, where noise and outliers are common. the best performance with 99.26% at roc-auc value achieves from models using ems score as the target variable to predict. this implies that the vietnamese stock market can be explained by ems score. the emerging market score is a composite measure that accounts for various economic and financial factors specific to emerging markets. in the case of vietnam, which is considered an emerging market, the ems score captures key macroeconomic indicators, financial health, and market performance indicators that are critical in understanding the stability and risk of companies within that market. given the unique economic conditions, volatility, and risks associated with emerging markets like vietnam, the ems score serves as an effective predictor of financial distress and stability for companies listed in such markets. by using this score as the target variable in the model, the machine learning algorithms can leverage the specific characteristics and risk factors of the vietnamese market, leading to higher prediction accuracy. the roc-auc score of 99.26% indicates that the ems score is highly informative and predictive in this context. conclusion in this study, we aimed to predict the financial health of listed companies in vietnam using a combination of financial ratios and advanced machine learning techniques. by leveraging methods such as distance to default (dd), emerging market score (ems), and altman z-score, we developed models capable of assessing the financial status of companies. among the machine learning figure 3: confusion matrix of predictive model performance when using ems scores pa ge 12 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 115-127, 2025 algorithms tested, random forest demonstrated superior performance, achieving an impressive roc-auc score of 99.26%. the ems score, when combined with machine learning models, proved to be an effective predictor of financial distress, outperforming traditional methods and highlighting the potential for applying machine learning in the context of financial health prediction. our study also emphasized the importance of data balancing techniques, specifically the adaptive synthetic sampling (adasyn) method, which significantly enhanced the performance of the models in predicting financial distress. this underscores the critical role of data preprocessing in improving model accuracy, especially in cases involving imbalanced datasets common in financial markets. while random forest excelled in this study due to its resistance to noise and ability to handle multiclass classification, xgboost and catboost remain powerful alternatives. the integration of the ems score demonstrated its value in financial distress prediction, especially in emerging markets like vietnam. future research could explore expanding these models to other markets 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(2025). there are still many reasons to be optimistic. vietnam.vn. https://www.vietnam.vn/en/van-conhieu-ly-do-de-lac-quan pa ge 1 pa ge 14 3 american journal of financial technology and innovation (ajfti) cloud-based accounting and financial performance of listed deposit money banks in nigeria ajibola, hussein olamilekan1*, fasina, oludare olakunle1, akinbode, peter sunday2 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.5653 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: july 05, 2025 accepted: august 08, 2025 published: september 10, 2025 with the financial environment changing so quickly these days, the nigerian banking industry is under increasing pressure to modernize its operations, improve service quality, and maximize financial performance. one innovation that is becoming more popular is cloud accounting, which is a digital accounting paradigm that provides cost-effectiveness, scalability, and real-time data access. this study, therefore, examined cloud-based accounting and the financial performance of listed deposit money banks in nigeria. the study employed ex-post facto research design. ten of the 14 deposit money banks that were listed in nigeria as of the end of 2024 were used as the sample size in this study. during the data extraction process, the study used secondary sources, specifically from the annual reports and accounts of the selected banks from 2015–2024. the data gathered was analyzed with the use of descriptive statistics, correlation and regression analysis. software cost (β=7.966379, p-value = 0.0002) and training cost (β=12.50473, p-value = 0.0000) appeared to have a positive and significant impact on the return on assets of nigerian listed deposit money banks, according to the regression results. as a result, the study came to the conclusion that software and training expenses may be used by current and prospective investors to predict the return on assets of the chosen deposit money banks in nigeria. to facilitate the broad use of cloud-based accounting in nigerian deposit money banks, the research suggested that the government, trade groups, and financial institutions emphasize training and skill development in technology infrastructure. keywords cloud-based accounting, financial performance, return on assets, software cost and training cost introduction the performance of the financial services sector has a major influence on the whole economy, making it a pillar of economic growth. deposit money banks are essential to the mobilization of savings, the granting of credit, and the facilitation of investment and commerce in nigeria. however, because of rising competition, inefficiencies in conventional systems, and high operating expenses, these banks’ financial performance has been a recurring worry. a bank’s capacity to turn a profit, control expenses, and maintain its competitiveness in a changing market is largely reflected in its financial performance (olokoyo et al., 2019). due to the increasing need for operational efficiency and the quick development of technology, the global banking industry has undergone a substantial digital transformation in recent years. cloud-based accounting has become one of these advances’ most important tools for increasing data accessibility, automating financial procedures, and boosting decision-making accuracy (adebayo & okonkwo, 2023). using web-based software that is housed on distant servers to carry out accounting tasks including data entry, reporting, and analysis is known as cloud accounting. banks and other companies may work together across branches or departments and access financial data in real-time with this paradigm (ibrahim & oladele, 2022). adoption of cloud accounting is not free, but research shows that it improves asset usage overall. according to the research of ofurum and obi (2024), training expenses had a positive but negligible correlation with return on assets, but software acquisition costs had a negative but negligible correlation. by improving system usage and stability, this shows that initial investments in cloud-based accounting do not always degrade financial performance over time, increasing return on assets. in the quickly changing financial environment of today, the nigerian banking industry is under increasing pressure to improve service delivery, modernize processes, and maximize financial performance. cloud accounting is one innovation that is becoming more and more popular. it is a digital accounting paradigm that provides cost-effectiveness, scalability, and real-time data access. according to adebayo and okonkwo (2023), cloudbased accounting systems are widely acknowledged as strategic instruments that enhance organizational agility, decision-making, and the accuracy of financial reporting. but because to the central bank of nigeria’s drive for digital innovation and safe data management, cloud-based technology adoption by deposit money banks (dmbs) in nigeria is progressively picking up steam (ezeani & udeh, 2024). notwithstanding these advancements, a substantial knowledge vacuum still exists about the precise impact of cloud accounting adoption on financial performance in the nigerian banking industry. there is still uncertainty over the financial rationale behind 1 department of accountancy, federal polytechnic ilaro, ogun state, nigeria 2 crown heritage college of health technology and management, ilaro, ogun state, nigeria * corresponding author’s e-mail: jblhussein@gmail.com pa ge 14 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 the migration of numerous nigerian banks from conventional on-premises accounting systems to cloudbased platforms. according to nwachukwu and hassan (2025), these restrictions may make it more difficult to use cloud technologies effectively and raise questions about their capacity to improve important performance metrics like return on assets. numerous deposit money institutions in nigeria are still facing difficulties, including expensive software purchase and training expenses. according to onifade et al. (2023), high software costs had a negative effect on nigerian banks’ financial performance, indicating that such investments might not yield immediate returns without careful cost-benefit analysis. on the other hand, ofurum and obi (2024) showed that training investments can have a positive, albeit occasionally negligible, impact on financial performance, emphasizing the need for strategic training programs that are in line with organizational goals. the adoption of cloud-based accounting tends to improve return on assets (roa), according to a number of studies, including those by ezejofor et al. (2024), ofurum & obi (2024), akadi & olaoye (2024), ebere et al. (2024), ighosewe et al. (2024), odunayo et al. (2023), okika & udeh (2023), ajape et al. (2023), onifade et al. (2023), peters & fred horsfall (2023), oyewobi & adeyemi (2023), daniel (2024), ejabu & edet (2024), agaji (2023), and odukwu et al. (2023). there are currently very few panel data studies that isolate these distinct cost influences on return on assets, which is the gap. this study fills that vacuum by assessing the impact of cloud-based accounting on financial performance of nigerian listed deposit money institutions. in doing so, it aims to achieve the following specific objectives: i. to examine the effect of software cost on return on assets of listed nigerian deposit money banks. ii. to evaluate how training costs affect return on assets of listed nigerian deposit money banks. literature review conceptual review financial performance how successfully a company uses its assets to manage its operations and turn a profit is referred to as its financial performance. it analyzes a company’s overall financial health at a given time and may be used to evaluate how well a business is doing within its industry or across all sectors (ajirole, 2019). one important source of data for assessing a company’s success is its financial statements, which are a result of accounting. they record sales, costs, and profits for a specific time period, as well as information on changes in owners’ wealth and the sources and uses of funds throughout that time (ndukwe, 2018). financial performance may be calculated or examined using a number of metrics, but each one concentrates on a different aspect of the performance (folajimi et al., 2020). it shows the general state of a company’s finances over a given time frame. the technique of analyzing financial statements to determine an organization’s operational and financial characteristics is known as financial performance analysis; in this study, return on assets is used as a proxy for financial performance. aduda et al. (2017) asserted that when assessing financial performance, ratio analysis is a helpful technique. it may be used to assess an organization’s financial efficiency, liquidity, profitability, and solvency ratios as well as its capacity to pay back loans within a given time period. for instance, abdulazeez et al. (2018) used return on equity and return on asset to measure the performance of nigerian listed conglomerate companies and their inventory management. oladipupo and okafor (2017), on the other hand, used return on assets and the tobin q ratio to gauge financial success. return on assets, however, was used in this study as a stand-in for financial performance because it is one of the metrics that is most affected by poorly managed cloud-based accounting. return on assets (roa) return on assets is a crucial indicator of a manufacturing company’s profitability. it is defined as the ratio of revenue to total assets. it evaluates how well managers of manufacturing firms can use their resources to generate a profit. the efficiency with which the business uses its resources to generate income is also demonstrated. it further demonstrates how well business management makes use of all available resources to produce net income (khrawish, 2017). according to sehrish et al. (2019), roa establishes the amount of profit generated per asset. it shows the effectiveness with which a business uses its assets or financial resources to generate profits. simply said, roa indicates management effectiveness and shows how well a manufacturing company’s management uses its resources to generate profits. a manufacturing company’s profitability or strong performance may be clearly determined by a high return on assets (roa) ratio (bentum, 2020). cloud-based accounting accounting software is often bought as a package and set up locally on a user’s desktop computer (dimitriu & matei, 2015). in contrast, cloud accounting delivers on-demand accounting services via the vendor’s webbased apps, accessible at any time and from any location (christauskas & miseviciene, 2022). cloud computing, together with blockchain, big data analytics, and artificial intelligence (ai), has completely changed the accounting process and corporate environment in recent years (ionescu, 2019; wattana viriyasitavat & hoonsopon, 2019; viriyasitavat et al., 2019; yoon, 2020). cloud accounting is the practice of managing financial transactions, reporting, and data storage using internetbased software that is housed on distant servers as opposed to a business’s local computer systems. this technology facilitates improved cooperation across organizational units, scalability, automation of repetitive accounting operations, and real-time access to financial pa ge 14 5 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 data (adebayo & okonkwo, 2023). virtual accounting systems, online accounting, web accounting, e-accounting, real-time accounting, and cloud accounting software are other names for cloud accounting (ionescu, 2019). cloud accounting is different from traditional accounting in a number of ways, including the kind of software license (rent vs. buy), the location of the system (cloud vs. user site), and the maintenance and support costs (included in the package vs. separately purchased). the combination of the fundamentals of cloud computing and the operations of the accounting information system gave rise to cloud accounting (khanom, 2017). cloud accounting makes it easier to handle financial data more quickly and accurately, which is crucial for financial reporting, regulatory compliance, and strategic decisionmaking in the banking industry. the cloud platform facilitates the integration of various financial systems, including core banking and customer relationship management (crm) technologies, which improves overall operational efficiency (ibrahim & oladele, 2022). software cost an organization’s financial outlay for purchasing, subscribing to, or creating accounting software applications—especially those housed on cloud platforms—is referred to as software cost. monthly or yearly subscription fees, license fees, api integrations, and upgrades for more functionality or greater user access are common software expenses in cloud accounting. under the cloud model, these expenses are frequently categorized as operational rather than capital costs because the majority of cloud-based technologies function as pay-as-you-use rather than one-time purchases (okoye & ofoegbu, 2023). certain nigerian banks have paid exorbitant software subscription prices without seeing a corresponding increase in the quality of their financial reporting or asset performance. a lack of connectivity with other operational tools, overlapping system functionality, or poor software vetting are frequently the causes of this gap. software expense either increases or decreases bank profitability, depending on how well it is used. the link between cost and performance is also influenced by the kind of cloud accounting software that is used, such as sage cloud, xero, quickbooks online, sap cloud, or locally created solutions (ebere et al., 2024). the way banks handle their it budgets and relate software expenditure to quantifiable results like profitability and operational effectiveness will be affected by this change. when software expenditures are in line with internal capabilities, they may have a substantial impact on financial performance. when properly implemented, cloud accounting solutions automate a wide range of banking tasks, including risk reporting, financial reconciliations, compliance monitoring, and real-time ledger administration. these improvements improve decision-making and asset utilization, two important factors that affect return on assets, by lowering manual processing mistakes and expediting reporting deadlines (inegbedion et al., 2022). training cost training costs are the monetary outlays made to improve staff members’ abilities, competences, and knowledge needed to effectively use cloud-based accounting systems. with the goal of maximizing user contact with cloud software, training in the context of deposit money banks includes workshops, seminars, digital onboarding sessions, certification programs, and recurring retraining. many cloud accounting applications include sophisticated capabilities that must be continuously learned by staff members in order to be fully utilized (inegbedion et al., 2022). the cost of training is crucial to the financial success and successful deployment of cloud accounting systems. without proper training, even the most advanced software could be misused or underutilized, which would reduce operational effectiveness and put banks at risk for noncompliance. according to akadi and olaoye (2024), employees with proper training are more likely to use cloud tools efficiently, reduce input errors, produce realtime reports, and make accurate decisions—all of which improve a bank’s financial performance. in their digital migration phases, nigerian banks that made investments in ongoing staff development reported notable gains in transaction speed, financial correctness, and risk mitigation, all of which were connected to better financial performance. these banks saw training to be an asset that facilitated performance rather than a cost. a badly designed program or one that is not in line with employee duties, on the other hand, may squander money and not produce quantifiable financial benefits (abubakar & bala, 2023). conceptual model figure 1: conceptual model source: researchers (2025) pa ge 14 6 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 theoretical review technology acceptance model (tam) in 1986, fred davis created the technology acceptance model (tam), which was publicly published in 1989. it’s among the most popular frameworks for figuring out how people adopt and use new technologies. according to the technology acceptance model, perceived utility (pu) and perceived ease of use (peou) are two fundamental perceptions that influence a person’s desire to use a system and subsequently forecast actual system utilization. the degree to which an individual thinks that utilizing a certain technology will improve their performance at work is known as perceived usefulness in the context of the technology acceptance model framework. how much someone thinks utilizing the system will be effortless is known as perceived ease of use. these opinions affect how users feel about utilizing the system, which in turn affects how they embrace it (davis, 1989). bank accountants are more inclined to embrace a cloud accounting system, for example, if they find it userfriendly and think it would improve reporting efficiency. venkatesh and davis (2020) claimed that the technology acceptance model was expanded by include outside factors such corporate culture, user training, and system features. its relevance in financial and accounting technology applications has been further confirmed by recent empirical research. inegbedion et al. (2022), for instance, discovered that the adoption of cloud-based financial systems by nigerian banks was highly impacted by perceived utility and convenience of use. likewise, abubakar and bala (2023) showed that adoption rates in financial institutions are positively impacted by users’ faith in digital infrastructure, which is reinforced by training and system upgrades. but throughout the years, the technology acceptance model has been criticized on a number of occasions. one significant criticism is that, because it focuses mostly on individual behavior, it has a narrow scope for explaining adoption at the organizational level. bagozzi (2017) contended that the technology acceptance model oversimplifies the many organizational and social variables that affect how people use technology. in order to capture the impact of external and environmental factors, such as cost implications, regulatory pressure, and competitive dynamics factors that are highly relevant to cloud adoption in financial institutions, others, like legris et al. (2023), had proposed combining the technology acceptance model with other theories. additionally, the technology acceptance model’s detractors point out that it ignores postadoption behaviors that are essential to comprehending performance results, including system maintenance or long-term integration. although the technology acceptance model has limitations, it is a good supporting theory for this study, particularly when analyzing the factors that affect deposit money institutions’ acceptance and deployment of cloud accounting systems. while assessing the impact of software and training costs on financial performance is the primary focus of the study, the technology acceptance model offers a behavioral perspective to comprehend the motivations behind bank investments in these domains. for instance, a high training cost may boost perceived usability, and ongoing system maintenance may raise perceived utility, which in turn may accelerate the rate of technology adoption and long-term use (ama et al., 2025). the technology acceptance model is especially useful for describing why certain banks are more successful than others at implementing cloud accounting. by capturing cost components like software and training, the model assists in connecting user-centric aspects like perceived operational advantages and ease of system learning to investment decisions. cloud-based accounting adoption’s behavioral dimension and possible impact on performance are explained by the technology acceptance model in nigeria, where digital transformation is continuous and differs throughout institutions. innovation diffusion theory (idt) everett rogers developed the innovation diffusion theory (idt) in 1962 in his seminal work, titled diffusion of innovations. how, why, and how quickly new ideas and technology spread within a social system are all explained by the theory. rogers (2003) asserts that five essential characteristics—relative advantage, compatibility, complexity, trialability, and observability—are necessary for an invention to be adopted. an organization like a bank’s acceptance and adoption of innovations like cloud accounting are influenced by these variables. using the innovation diffusion theory, afolabi and hassan (2023) explained how mobile accounting technologies were adopted by nigerian banks, pointing out that perceived benefits and compatibility were powerful predictors of adoption. according to igwe et al. (2022), banks that could clearly see the benefits of complete cloud integration made investments more quickly. these data support the idea that how these innovations are seen in banking contexts affects internal decisions about how much to spend on software, training, and servicing. critics counter that the innovation diffusion model largely ignores external factors like industry pressure, financial limits, and regulatory policies in favor of an excessive emphasis on organizational or individual perception. according to lyytinen and damsgaard (2001), the innovation diffusion model fails to adequately explain post-adoption behavior, which is essential for comprehending performance outcomes following the integration of a technology. in this study, however, the innovation diffusion model is still applicable as a supplementary theory that describes the cloud accounting adoption phase in deposit money institutions. although analyzing the impact of cloudbased accounting expenses on financial performance is the study’s main goal, the innovation diffusion model offers background information for comprehending how and why banks first choose to incur these expenses. afolabi and hassan (2023) found that banks are more likely to make major investments in software and training elements that are quantified as independent variables if they believe that cloud accounting is compatible, beneficial, and simple to deploy. pa ge 14 7 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 empirical review ikwuo et al. (2025) investigated how cloud accounting might be strategically used to maximize shareholder wealth in nigeria’s pharmaceutical industry. the study analyzed shareholder wealth using return on equity (roe) and concentrated on two main variables: the adoption of cloud accounting software and its intensity. in order to gather secondary data from five listed pharmaceutical companies over a ten-year period (2014–2023), an expost facto research approach was used. using robust least-squares regression analysis, the hypotheses were examined. utilizing cloud accounting software increased return on asset in a statistically significant way (p = 0.0056), according to the data, suggesting that using cloud solutions improves shareholder returns. nevertheless, roe was significantly impacted negatively by cloud accounting software intensity, which indicates deeper or more complicated usage (p = 0.0147). this implies that while simple adoption has advantages, excessive expenditure or exceptionally complicated cloud solution integration may degrade the industry’s financial performance. the effect of cloud accounting on tier 1 banks’ operational efficiency in nigeria was examined by enaibre et al. (2024). as crucial components of cloud technology integration, the study concentrated on cloud accounting expenses, client interfaces, and delivery methods. the capacity of the banks to efficiently offer services and optimize procedures was measured using operational efficiency as the dependent variable. in order to examine the correlations between the variables, the study used partial least squares structural equation modeling (pls-sem) using smart-pls 4.0. according to the findings, the client interface and delivery method had a good effect on bank performance, however cloud accounting expenses had a negative effect on operational efficiency. the associations between cloud accounting features and operational efficiency were also found to be strengthened by technological proficiency, which was an effective mediating factor. in order to achieve complete efficiency improvements, the research highlights the necessity for banks to match their internal capabilities with the deployment of technology. the impact of cloud accounting on the financial performance of nigerian listed deposit money institutions was investigated by daniel (2024). the study employed return on assets (roa) to assess financial performance and computerized accounting systems (cas) and accounting software (as) as important indicators of cloud accounting. the study used an ex-post facto research approach and analyzed secondary data gathered from 15 listed deposit money institutions between 2013 and 2022. the study employed panel regression analysis to examine the correlation between the variables. the results showed that the financial performance of both computerized accounting systems and accounting software was positively and significantly impacted. this suggests that the use of cloud-based accounting technology improves return on assets for nigerian listed deposit money institutions. the association between cloud accounting and organizational performance was investigated by onyebuchukwu and ojimini (2024) among a subset of businesses in the port harcourt area that used cloud accounting systems. as stand-ins for cloud accounting software, the study looked at sap cloud platform and quickbooks online. customer and staff satisfaction were used to gauge organizational effectiveness. the pearson’s product moment correlation (ppmc) approach was used to assess the direction and intensity of the variability-to-variable connection. the results showed that quickbooks online significantly increased customer satisfaction, an indication of better customer service and client involvement. employee satisfaction was also shown to increase with sap cloud platform, indicating that the platform helps improve internal operations and workflow efficiency. according to these findings, cloud accounting supports organizational performance on both an internal and external level. a study by ezejofor et al. (2024) looked at the connection between cloud accounting expenses and nigerian deposit money institutions’ financial results. return on assets (roa) was employed as a financial performance metric, and the study concentrated on two cost components related to cloud accounting: software procurement and server servicing. ex-post facto research methodology was used, and secondary data from five publicly traded manufacturing companies was collected during an 11-year period from 2012 to 2022. using e-views 9.0 software, multiple regression analysis was used to examine the data. although the cost of server maintenance improved financial performance, the effect was not statistically significant, according to the data. in contrast, the cost of software purchase had a negative and negligible impact on financial performance, indicating that investments in cloud accounting components could not result in quantifiable financial improvements for the companies under assessment right away. onifade and dedire (2024) examined the effect of cloud computing technology on the financial performance of nigerian listed deposit money banks. with return on assets (roa) as the financial success metric, the study concentrated on three essential cloud computing components: automated chatbot banking services (acbs), deep learning machines (dlm), and machine learning solutions (mls). to account for bank-level variability, the study used panel estimated generalized least squares (egls) with cross-section weights and encompassed ten deposit money institutions. the findings showed that roa was statistically unaffected by acbs, dlm, and mls. the results of this study indicate that although cloud-based intelligent technologies are increasingly being incorporated into banking operations, their influence on short-term financial results, such asset returns, might not yet be significant or quantifiable. olaoye and akadi (2024) investigated the effect of cloud-based accounting systems on the operations of a few nigerian deposit money institutions. the pa ge 14 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 study looked at two aspects of cloud-based accounting systems as stand-ins for digital accounting integration: structural capital and human capital. both financial and operational performance criteria were used to evaluate the bank’s success. targeting all 38 deposit money banks in nigeria, a survey research design was used. thirty-four banks were chosen using taro yamane’s sample technique, however because of accessibility issues, only twenty banks ultimately took part. of the 300 distributed questionnaires used to gather data, 279 were judged suitable for study. the findings showed that bank performance and cloud-based accounting systems were strongly positively correlated. digital accounting plays a crucial role in improving banking operations, as evidenced by the r-value of 56.20% and r-square of 55.70%, which specifically showed that the adoption of cloud-based systems considerably explained variances in the performance of the banks under study. ozondu et al. (2024) looked at how cloud accounting affected nigerian deposit money institutions’ performance, specifically focusing on self-service transaction reporting (str) and virtualized transaction reporting (vtr) as stand-ins for cloud accounting. the productivity and profitability metrics were used to measure performance. data from a sample of nigerian deposit money institutions was gathered using a crosssectional survey study approach. the study’s findings showed that cloud accounting and financial success were significantly positively correlated. in particular, the banks’ operational efficiency and profitability were shown to be increased by the usage of vtr and str, indicating that the incorporation of cloud-based reporting systems significantly improves organizational performance in the nigerian banking industry. the impact of cloud accounting adoption on organizational performance in the domain of financial reporting among nigerian listed corporations was investigated by fadipe (2023). platform as a service (paas), software as a service (saas), and infrastructure as a service (iaas) were the three main facets of cloud accounting adoption that were the focus of the study. the timely delivery of financial reports served as a gauge for the caliber of financial reporting. survey research designs and ex-post facto research designs were combined. structured questionnaires were used to collect primary data from accounting staff, while secondary data was gathered from 20 listed companies between 2010 and 2022. regression analysis using ordinary least squares (ols) was used. both saas and paas were found to have a noteworthy and favorable effect on the timeliness of financial reporting, demonstrating their efficacy in improving financial disclosure procedures. nevertheless, iaas showed no discernible impact on reporting timeliness, indicating little control over this facet of the caliber of financial reporting. akai et al. (2023) investigated how cloud accounting affected the caliber of financial reports from a few nigerian banks. software as a service (saas) and infrastructure as a service (iaas) were utilized as stand-ins for cloud computing in this study, and the qualitative traits listed in the iasb conceptual framework were employed to gauge financial reporting quality (frqt). utilizing primary data gathered from 212 respondents at a few chosen banks, a survey research approach was used. using robust ordinary least squares (ols) regression, the data were examined. the results showed that infrastructurebased cloud services significantly improve the quality of financial reporting, suggesting that they play a key role in enhancing the reliability and applicability of financial statements. while software solutions may facilitate reporting procedures, their direct influence on the caliber of financial reporting may differ throughout institutions, as seen by the positive but statistically insignificant effect that saas demonstrated. the fundamentals of cloud accounting information systems and their effects on nigerian businesses’ operational efficiency were examined by beredugo (2023). a survey research design was used for the study, and information was gathered from 385 respondents from 32 businesses in four different nigerian economic sectors. the study concentrated on how key components of cloud accounting affect operational performance, paying special attention to how users view its implementation. the results showed that respondents’ opinions on how much cloud accounting improves operational efficiency did not differ significantly. notably, the study raised issues with the higher danger of illegal access that comes with using cloud services. this implies that although cloud accounting could have operational advantages, adoption and adoption in nigerian businesses are still largely dependent on concerns about data security and access control. kpan et al. (2023) investigated the impact of cloud accounting practices on the caliber of financial data for a subset of nigerian companies. while the accuracy, timeliness, and dependability of financial reports served as indicators of the quality of financial information, the study concentrated on data storage, data efficiency, and data mining as stand-ins for cloud accounting. structured questionnaires were given to chosen companies as part of a cross-sectional survey study strategy. to examine the data, descriptive statistics and ordinary least squares (ols) regression were employed. the results showed that cloud accounting considerably improves the quality of financial data in every category that was assessed. data mining (β = 0.809, p < 0.05), data efficiency (β = 0.647, p < 0.05), and data storage (β = 0.828, p < 0.05) all showed gains, suggesting that cloud technologies have a favorable impact on how financial data is processed, stored, and used in businesses. omar et al. (2023) investigated the dangers of cloud accounting and how they affect the caliber of financial reports. to learn more about respondents’ perceptions of cloud computing’s impact on financial reporting, the study used a survey research approach. despite concentrating on the risk aspects of cloud accounting, the pa ge 14 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 banks’ profitability and effective asset use were enhanced by the implementation of this cloud-based solution. the report emphasizes the strategic importance of integrated cloud technology in enhancing nigerian commercial banks’ financial results. since the results of the previously examined research on the financial performance of nigerian listed deposit money banks and cloud-based accounting did not significantly agree, this study sought to advance the field by evaluating the following null hypotheses: ho1: software costs do not significantly affect return on assets of listed deposit money banks in nigeria ho2: training cost has no significant on return on assets of listed deposit money banks in nigeria materials and methods an ex post facto research design was applied in this study. this research strategy was chosen in order to gather important data on the state of a certain phenomena during a period of naturally occurring therapy without changing the situation. additionally, by characterizing and summarizing the data collected for the study, this design enables the researcher to give a thorough knowledge of the investigation’s objectives and contributing variables (fleetwood, 2023). data for this study was collected from secondary sources, specifically the annual reports and accounts of ten (10) nigerian listed deposit money institutions. the following deposit money banks are listed: first bank nig. plc., gtb plc., stanbic ibtc plc., access bank plc., fcmb plc., fidelity bank plc., sterling bank plc., uba plc., and wema bank plc. furthermore, the obtained and computed data encompassed ten (10) years, from 2015 to 2024. to examine the gathered data, the study used both descriptive and inferential statistics (regression analysis and correlation). model specifications a model was used to look at how cloud accounting proxies affected financial performance over time. software and training costs were used to measure cloud-based accounting costs. as a gauge of financial performance, return on assets was employed. as shown below, the study used multiple regression to evaluate the connection between the independent and dependent variables: roa = f(swc, trc) ………. i the model has been formulated to suit the study as follows: roa = α + β1swc + β2trc + e ……….(ii) where; roa = return on assets swc = software cost trc = training cost α = constant value β1, β2 = coefficient of regression e = error term results and discussion the results of the study on cloud-based accounting and study’s conclusions showed that, in principle, using cloud computing lowers storage expenses and transmission and writing mistakes. according to these results, cloud accounting may improve the overall quality and accuracy of financial statements by lowering manual mistakes and the operating expenses related to traditional accounting infrastructures, even in the face of worries about data security and system dependability. the impact of cloud accounting and related expenses on the performance of nigerian listed manufacturing companies was investigated by okere (2022). utilizing a mixed-method approach that combined an ex-post facto framework with a survey research methodology, the study collected primary and secondary data to provide a thorough understanding of cloud accounting deployment in the chosen organizations. with business performance acting as the dependent variable, the study concentrated on cloud accounting adoption and costs as the independent factors. cloud accounting and associated expenses significantly affected manufacturing enterprises’ performance, according to the research. in particular, the study showed that integrating cloud accounting systems enhanced organizational performance, even if expensive components can be problematic if they are not well matched with businesses’ cost structures. the findings highlight how adopting new technologies and controlling costs might help the nigerian industrial sector use cloud systems more efficiently. the impact of cloud computer-based accounting on the corporate financial performance of a subset of nigerian listed industrial enterprises was evaluated by abidde (2021). the research used the netsuite program as a stand-in for cloud computer-based accounting, and return on equity (roe), return on assets (roa), and return on capital employed (roce) were used to gauge the financial performance of the company. using an ex-post facto research approach, the study covered the years 2009–2012 before adoption and 2013–2016 after adoption. the released yearly financial reports of six publicly traded industrial companies provided secondary data. according to the results, there was no statistically significant impact of netsuite deployment on roa, roe, or roce. the financial performance metrics did not show any significant changes after adoption, despite the fact that operational efficiency was shown to increase. this suggests that the short-term operational rather than financial advantages of cloud accounting for these companies may be greater. egbe (2020) looked at how cloud-based accounting software affected the financial performance of nigerian commercial banks that were listed, specifically focusing on oracle financial cloud usage. key indices for evaluating financial success were return on equity (roe) and return on assets (roa). only 15 quoted commercial banks were included in the study, which used a survey research approach and covered the years 2009–2016. the results showed that oracle financial cloud significantly affected return on equity and return on assets, indicating that the pa ge 15 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 financial performance of listed deposit money banks in nigeria are shown in this section. several robustness tests were conducted to improve the validity of the findings, in addition to trend, descriptive, correlation, and regression analysis on the collected data. presentation of result the descriptive data, which include the parameters’ minimum, maximum, average, standard deviation, and jarque-bera, are displayed in table 1 above. according to the findings, the average values for roa, swc, and trc table 1: descriptive results roa swc trc mean 3.143964 9.655949 8.498013 median 1.007052 9.436273 8.507384 maximum 127.6364 10.86541 10.13577 minimum 0.023629 8.143015 6.446537 std. dev. 11.80631 0.928489 0.892629 skewness 9.908269 -0.093733 -0.138815 kurtosis 104.7786 1.297652 2.136351 jarque-bera 53757.87 14.66567 4.114840 probability 0.000000 0.000654 0.127783 sum 377.2756 1158.714 1019.762 sum sq. dev. 16587.29 102.5889 94.81764 observations 100 100 100 source: e-view output, 2025 were 3.143964, 9.655949, and 8.498013, respectively. since swc has a highest average value of 9.655949, compared to trc’s 8.498013, it is clear from the average value that it is an excellent predictor of the dependent variable (roa) among the independent variables. the variables’ maximum values were found to be 10.13577, 10.86541, and 127.6364, respectively. as an illustration, the variables’ lowest values are 0.023629, 8.143015, and 6.446537, respectively. additionally displayed were the standard deviation numbers, which were 11.80631, 0.928489, and 0.892629, respectively. furthermore, the study found that the probability values (0.000000 and 0.000654) of the jarque-bera test are less than the 0.05 significant threshold, indicating that the data gathered for roa and swc are not normally distributed. according to the study’s findings, the trc data are normally distributed since the jarque-bera probability value (0.127783) is higher than the 0.05 significant level. the correlation results between swc, trc, and the adopted variable roa are shown in table 2 above. according to the data, swc and trc have a weakly positive association with roa; their respective correlation table 2: correlation analysis correlation t-statistic probability roa swc trc roa 1.000000 ---- swc 0.078507 1.000000 0.3940 ---- trc 0.160785 0.612389 1.000000 0.0794 0.0000 ---- source: e-view output, 2025 values are 0.078507 and 0.160785. the p-value of 0.0000 in the preceding table can be viewed as statistically significant because it is below the 0.05 significant threshold. consequently, the regression analysis may be conducted using the fixed effect regression model. the table below contains the previously defined coefficient for the regression model, as indicated below: roa = 54.72383 + 7.966379swc + 12.50473trc the aforementioned equation demonstrates that swc and trc positively impact roa, with corresponding coefficient values of 7.966379 and 12.50473. this means that the roa of the chosen deposit money institutions will grow by 7.966379 and 12.50473 accordingly if each pa ge 15 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 swc and trc rises by one unit. additionally displayed were the independent variables’ t-calculated values, which were 6.886906 and 3.811974, respectively. the predictors’ two t-cal values exceed the t-tab of 2. furthermore, the 0.0002 and 0.0000 probability values that corresponded to them were displayed. being below the 0.05 significant level, the p-values for swc and trc may be categorized as statistically significant. moreover, the regression’s r-squared statistic was 0.557380, indicating that swc and trc account for 55.74% of the overall changes in roa, with other parameters not used in this study accounting for the remaining 44.26%. the table above also displayed the variance analysis of the regression, with the f-statistic— which was 10.26793 with a probability value of 0.000000— as one of the important data points. it appears that the model the study created is statistically significant because the p-value is below the 0.05 cutoff. as the value is more than 1.5, the durbin-watson statistic result, however, was 2.390749, indicating the absence of auto-correlation. further evidence that the study’s parameters are sound comes from here. interpretation of results test of hypotheses ho1: software costs do not significantly affect return on assets of listed deposit money banks in nigeria with a matching probability value of 0.0002 and a t-cal of 3.811974 in the regression table, the swc was deemed statistically significant as the p-value was less than the 0.05 significant threshold. as a result, the study fails ti accepts the first hypothesis stated above and affirms that software cost significantly affects return on assets of listed deposit money banks in nigeria. ho2: training cost has no significant on return on assets of listed deposit money banks in nigeria with a matching p-value of 0.0000 and a t-cal value of 6.886906 from the previously provided regression table, the training cost was considered statistically significant because it was below the 0.05 significant threshold. thus, the study fails to accept the above-stated null hypothesis and restates that training cost has significant effect on return on assets of listed deposit money banks in nigeria. discussion of findings the study investigated how listed nigerian deposit money banks’ financial performance is affected by cloudbased accounting costs. however, the study found that while some of the empirical studies examined for this investigation had different results, others are pertinent to the findings of the study, as stated below: the study comes to the conclusion that the return on assets of listed nigerian deposit money banks is significantly affected by cloud-based accounting, as measured by software cost. this result does support the technology acceptance model’s premise that an entity’s performance would be improved by perceived technological utility, such as cloud-based accounting. furthermore, while the outcome is not in accordance with the findings of enaibre et al. (2024), ezejofor et al. (2024), and onifade & dedire (2024), it is in accordance with the findings of ikwuo et al. (2025), daniel (2024), olaoye and akadi (2024), ozondu et al. (2024), and egbe (2020). additionally, it was demonstrated that training expenses significantly affects return on assets of nigerian listed deposit money banks. the results also support the presumption of the technology acceptance model, which links user-centric elements like perceived operational advantages and ease of system learning to bank performance. the findings of olaoye & akadi table 3: hausman test test summary chi-sq. statistic chi-sq. d.f. prob. cross-section random 26.620086 2 0.0000 source: e-view output, 2025 table 4: regression analysis (fixed effect) variable coefficient std. error t-statistic prob. c 54.72383 24.80370 2.206277 0.0295 swc 7.966379 2.089830 3.811974 0.0002 trc 12.50473 1.815725 6.886906 0.0000 r-squared 0.557380 mean dependent var 3.143964 adjusted r-squared 0.503096 s.d. dependent var 11.80631 s.e. of regression 8.322434 akaike info criterion 7.185067 sum squared resid 7341.868 schwarz criterion 7.510275 log likelihood -417.1040 hannan-quinn criter. 7.317135 f-statistic 10.26793 durbin-watson stat 2.390749 prob(f-statistic) 0.000000 source: e-view output, 2025 pa ge 15 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 143-154, 2025 (2024), ozondu et al. (2024), akai et al. (2023), and egbe (2020) are in agreement with this result; however, studies conducted by onifade and dedire (2024), enaibre et al. (2024), and ezejofor et al. (2024) do not support it. conclusion in line with the research findings, the study concludes as follows: regression table results showed that software cost has a significant impact on return on assets of nigerian listed deposit money banks, and the study concludes that software cost has a significant impact on return on assets of nigerian listed deposit money banks and can therefore be used to predict return on assets of the chosen banks. the results also showed that the return on assets of nigerian listed deposit money banks is significantly affected by training costs. furthermore, the results showed that the return on assets of nigerian listed deposit money institutions is significantly impacted by training costs. the study comes to the conclusion that as training costs have a favorable impact on the return on assets of nigerian listed deposit money banks, their importance in influencing return on assets cannot be overstated. recommendations the underlisted recommendations were made in accordance to the findings and conclusion of the study: nigerian listed deposit money banks should increase their investment in accounting software as it’s a creative strategy to increase their return on assets and provide them a competitive edge over rivals. the study concluded that the cost of training has a major impact on the financial performance of nigerian deposit money banks. as a result, it is therefore suggested that the nigerian government, trade associations, and financial institutions give training and skill development in technology infrastructure top priority in order to facilitate the broad implementation of cloud-based accounting in nigerian deposit money banks. references abdulmunim, o. 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(2020). cloud accounting risks and mitigation strategies: evidence from australia. accounting forum, 44(4), 421–446. pa ge 1 pa ge 17 7 american journal of financial technology and innovation (ajfti) ai-driven fraud detection in digital banking: ml approach for secure and transparent financial transactions oreoluwa abimbola serifat1*, roseline c. igah2, kehinde m balogun3, gershom randy mensah4, emmanuel niiboye odai4 volume 3 issue 1, year 2025 issn: 2996-0975 (online) doi: https://doi.org/10.54536/ajfti.v3i1.5168 https://journals.e-palli.com/home/index.php/ajfti article information abstract received: august 25, 2025 accepted: september 23, 2025 published: october 25, 2025 the convenience of digital banking services has transformed the global financial industry and is now available to consumers all over the world. as with any advancement, there’s an increase in associated risk. in this case, we have an upsurge in fraudulent activity, the mobility of cybercriminals, and their more advanced technologies to breach vulnerabilities within digital infrastructures. indeed, financial crimes are constantly evolving like the rest of technology and society. those who monitor and manually analyse systems are no match for the speed at which criminals can devise new rule-of-thumb schemes. this article examines how artificial intelligence and machine learning can reform the detection of fraud within digital banking systems. the research analyses different techniques of ai and ml, supervised learning, unsupervised learning, ensemble, and deep learning approaches, while also observing their uses in practical fraud detection systems. the paper also analyses the ethical and legal concerns involving the use of ai within banking, considering data issues, algorithmic discrimination, and other contentious aspects of legal compliance, including quasi-legal frameworks like gdpr and pci-dss. it also explores some of the newer directions in ai, like quantum computing, explainable ai (xai), and federated learning, and their potential implications to improving fraud detection systems performance. finally, the focus of this paper has been on a continuing effort and partnership across sectors in building resilient, secure, transparent financial systems. ai, ml, and blockchain technologies enhance the capability to prevent fraud in digital banking, while ensuring and maintaining customer trust and security in financial transactions. keywords artificial intelligence (ai), digital banking, fraud detection, machine learning (ml) introduction digital banking represents a fundamental change in the finance by transforming the ways that people access and handle their money, for example (mohmmed et al., 2024). digital banking, through online and mobile banking, or use of digital or ewallets, has increased access to financial services by making them more accessible, convenient, and efficient (barroso & laborda, 2022; oduro et al., 2025). consumers can now execute various banking transactions from any location at any moment which leads to enhanced financial inclusion and equal access to banking services. digital banking solutions enable businesses to enhance operational processes while cutting operational expenses and offering customized services to their clients (bueno et al., 2024). the expansion of the digital landscape creates greater opportunities for financial fraud to occur. online transaction growth leads to a highly susceptible financial environment for numerous criminal activities (kipngetich, 2025). fraudsters exploit digital platforms to target financial system vulnerabilities using advanced techniques to overcome conventional security barriers. as digital platforms become essential for daily banking operations, there has been a substantial rise in fraudulent activities including identity theft, card-not-present fraud, account takeovers and money laundering (adeyeri et al., 2023). traditional fraud detection methods that depend on rule-based algorithms and human supervision fail to match the speed and complexity of current fraudulent activities (ismaeil, 2024). as the fraud increases, the need for automated, innovative and real-time detection solutions is evident. banking is realizing that traditional fraud detection has its weaknesses; high rates of false positives, expensive manual review process, and lack of ability to expose new, emerging fraud patterns. this realization has given rise to a more nuanced application of techniques, especially utilizing artificial intelligence and machine learning (ai/ml) (olowu et al., 2024). the need to adapt to new schemes and the capability of ai technologies in large data analysis, pattern recognition, and adaptation to new threats make it an increasingly important resource for improving the fraud detection systems in use. by leveraging these technologies, banks can prevent and minimize fraud before it occurs, enabling secure, efficient and transparent financial transactions (ismaeil, 2024). this evolution towards fraud detection via ai, addresses the need for better intelligence, scalability, and real time capabilities in a more digital and online oriented industry. what is certain is that as the digital banking system has brought much benefit, it needs to be secured against new digital fraud threats, and thus protected just as much. 1 department of data analytics, nexford university, usa 2 department of management science and information systems, oklahoma state university, usa 3 department of mathematics, austin peay state university, tennessee 4 northeastern university, massachusetts, usa * corresponding author’s e-mail: oreoluwaabimbolaserifat@gmail.com pa ge 17 8 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 hence the objective of this paper is to discuss how ai and ml could be fruitfully employed to improve the detection of fraud in digital banking as a novel and dynamic way to maintain security, efficiency, and transparency in a world where fraud in digital banking is becoming increasingly prevalent. the types of fraud in digital banking, ai and ml solutions to detect and deter fraud, practical examples of successful implementations that enhance the security and robustness of digital banking against ever-changing fraud techniques, and bolstering consumer confidence in banking transactions. literature review digital banking has been a disruptive innovative force in financial services, changing how people and businesses interact with their finances in a big way (bueno et al., 2024). it is technology, internet access, and demand for reduced and efficient banking services that have motivated the transformation from physical to digital banking (iwedi, 2024). for instance, with digital banking customers can check their account balances, transfer money, request loans, and pay bills online in their houses or anywhere by using their mobile phones (javaid et al., 2022). also, financial institutions have strategically leveraged this technology to provide a spectrum of e-payment opportunities and services to their clientele (igah & luse, 2024). among the essential services of digital banking are online payments, which are transactions that help the transfer of money between individuals and businesses through the internet (abdelrhman, 2025; windasari et al., 2022). the result has been that e-commerce has exploded as consumers now can purchase goods and services easily. mobile banking applications take this convenience a step further, allowing individuals to view their accounts and execute transactions from their mobile phones and thus transforming banking into an even more accessible medium than before (ezie et al., 2023; rahman et al., 2024). wallet applications like apple pay and google pay or apps from individual banks help consumers save and manage their payments information digitally, enabling fast and secure transactions without requiring physical cards (khando et al., 2022). while digital banking has offered many advantages including financial inclusion for the unbanked, userfriendliness, costefficiency, etc, it has also increased the attack surface available to cybercriminals. in other words, the advantages of digital banking have also been problematic, as cybercriminals have taken advantage of online platform weaknesses to obtain personal information and carry out unauthorized illegal transactions, and therefore online banking has become subject to fraud and cybercrime activities (aziz & andriansyah, 2023; mallesha & hymavathi, 2024; roszkowska, 2021).as a result, the financial sector faces increasing pressure to implement robust security measures to protect customers from the growing risk of fraud. fraud in digital banking digital banking fraud is the unauthorized and illegal utilization of digital banking systems to steal, manipulate and compromise financial data for an individual’s own benefit. as a result, more and more fraudsters are taking advantage of online banking, utilizing multiple methods of exploiting weaknesses inherent in digital banking systems. these include, among the more common, identity theft (venigandla & vemuri, 2022), where fraudsters obtain personal data to pose as clients and gain access to their accounts. phishing is another form of scam in which customers are tricked into revealing sensitive information such as passwords and account numbers (adaji et al., 2024). the second most frequent form of fraud is transaction manipulation, where the fraudster is actually the one who initiates the transaction exploiting a loophole in the payment mechanisms (adeyeri, 2024; oduro et al., 2025). another related issue of grave concern is also money laundering, where criminals can disguise the source of illegal funds through sophisticated transactions on digital banking platforms that seem authentic (bello & olufemi, 2024; olowu et al., 2024; khodabandehlou et al., 2024). also, without adequate security measures, funds can be moved illegally across borders using digital payment systems, making it even more difficult to get any form of control or trace these funds. consumer trust in the banking system is thus diminished as these scams are expensive for banks and other financial institutions to deal with (adeyeri, 2024). the increased use of digital banking has made the implementation of advanced fraud detection systems capable of addressing and recognizing such fraudulent behaviours a necessity. traditional fraud detection methods most banks use rule-based systems and human monitoring to catch fraud when it happens. in rule-based systems a predefined set of rules is utilized to identify potentially suspicious behaviour, for example, these sorts of rules could indicate abnormally large transactions or simultaneous multiple withdrawals from various locations. goyal et al., 2025; metha, 2025). these systems have the ability to detect patterns of fraud that have been established, but they are limited in that they cannot evolve to detect emerging patterns of fraud. traditional rule systems tend to be rigid; they can only catch fraud patterns that are in line with existing parameters and are unsuccessful at recognizing new fraud strategies that might be unpredicted patterns of fraud identified by existing parameters and would be ineffective in recognizing new fraud strategies that might not have been predictive of fraud patterns by existing parameters and would be ineffective at recognizing new fraud strategies that might not have been anticipated (bello et al., 2023; ikemefuna et al., 2024). fraud monitoring is also completed by humans, who review flagged transactions that were detected by rules or other non-automatic fingerprints of alleged fraudulent pa ge 17 9 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 activity (hilal et al., 2021). but human oversight adds an even more rigid layer of control, as it is highly laborious and also subject to mistakes given the enormous number of daily transactions. plus, humans reviewing cases can be overwhelmed by the volume and complexity of rampant fraudulent activity, causing delays in the detection and subsequent action (bello et al., 2022). combined, these traditional techniques can produce high false positive rates which burden banking staff and lead to unneeded investigations. just as fraudsters have continued to enhance their techniques, the conventional systems have become less effective to deal with the increasing complexity of this digital fraud and this has opened the door to more sophisticated, ai-based solutions for fraud detection. machine learning in fraud detection fraud detection has changed with ai and ml as they help financial institutions, “analyse huge data sets in real-time and recognize intricate patterns that signal the possibility of fraudulent behaviour” (adhikari et al., 2024; odufisan et al., 2025). unlike traditional rule-based systems, ml algorithms are trained on historical data and thus are adaptive in detecting patterns of both historical and emerging fraud. supervised learning, unsupervised learning, and reinforcement learning are now commonly used in fraud detection systems by machine learning techniques (sarker, 2021; hernandez aros et al., 2024). supervised learning involves training a model using a labelled dataset with transactions already labelled as fraudulent or legitimate (afriyie et al., 2023). the model will then learn the patterns that distinguish these two in order to predict the probability of fraud in future transactions. unlike, unsupervised learning is used for finding anomalies in unlabelled data (venigandla & vemuri, 2022). this is particularly advantageous in identifying novel or unknown patterns of fraud that are actually not found in the historical data. also, reinforcement learning, where the model learns by interacting with the environment, becomes an effective fraud detection tool as it constantly improves the predictions of fraudulent actions (sharma, 2024). ai and ml applications to fraud detection writ large has been the subject of study in a couple of papers. in 2020 li et al. concluded that individual algorithms performed worse than random forests and gradient boosting in the detection of credit card fraud, supporting the results of this study. another example of research emphasizing the use of rpa and ai in online banking fraud detection is by wang et al. (2019), who recommended hybrid rpa and ai predictive analytics system to be used as a cutting-edge approach for online banking fraud detection. also, oduro et, al. (2025) pointed out the potential of machine learning models to improve accuracy in fraud detection and decrease the false positive rate in digital banking systems. materials and methods the study is based on secondary data concerning the use of artificial intelligence and machine learning in fraud detection in digital banking from among a general pool of published research articles. the required data was obtained by systematically identifying peer reviewed journal articles, conference proceedings, and industry reports retrieved through google scholar, ieee xplore, elsevier, and other reputable journals. selected articles were those that addressed digital fraud detection systems that implemented the use of ai and ml, specifically in regards to the categories of fraud detection systems of supervised learning, unsupervised learning, reinforcement learning, and ensemble methods. a method and finding section were extracted from each study of interest that focused on fraud detection in banking. a focus was on these techniques as employed in actual banking scenarios, specifically data preprocessing, model selection, and algorithm performance. results & discussion ai and ml techniques for fraud detection ai and ml thus represent an integral part of fraud detection systems, particularly in the context of online banking. these technologies allow banks to sift through figure 1: machine learning (ml) in fraud detection pa ge 18 0 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 massive volumes of transaction data and detect minute patterns in transactions that may be suggestive of fraud. while legacy fraud detection is primarily rule-based and human supervised, ai and ml algorithms apply knowledge to new data in real-time to adapt to shifting patterns of fraud. ai and ml’s main advantage in fraud detection is its capacity to handle, or “scale up”, large datasets and find patterns within them (adeyeri, 2024). by detecting both established as well as new patterns of fraud, these algorithms can aid in the prevention of fraud before it occurs. ai technologies can identify patterns of anomalies that would be hard to catch by human analysts or static rules-based systems. rather, through their ability to use historical data and becoming better at predicting fraud over time, ai and ml “empower banks to detect new forms of fraud even before they happen” (odufisan, 2025). as the advancement of fraud schemes have also advanced detection methods are becoming obsolete. this ability to scale and adapt fraud is an important characteristic of ai and ml models. overall, these technologies help banks not only to identify fraud as it is happening but also to anticipate and stop fraud before it occurs, increasing the security of the digital banking experience. common ml techniques supervised learning afriyie et al., 2023 highlights that supervised learning is one of the most commonly employed methods in machine learning to detect fraud. this includes training a model on a labelled dataset in which there is knowledge of the outcome of each transaction whether it was fraudulent or legitimate. the model then “learns” to correlate patterns or groupings of input features, transaction amount, time, location, etc. with the target outcome fraud versus non-fraud. fraud detection often employs “decision trees” and “random forests” which are common fraud detection supervised learning algorithms (salunke et al., 2025). it forms a model shaped like a tree by repeatedly partitioning the data based on values of the features. the nodes are points at which a decision is made on a given feature and the leaves are the predicted classification as either fraud or non-fraud (adeyeri, 2024; afriyie et al., 2023; johora, 2024). random forests are collections of decision trees. they aggregate multiple decision trees to increase accuracy and overfitting, which is a flaw of decision trees. since it combines predictions from multiple trees, random forests are efficient tools, especially when dealing with large and feature-rich data (ismaeil, 2024). li et al. (2020) on top of that offered, as an example, logistic regression, decision trees and neural networks; who studied the efficiency of different machine learning methodologies, including logistic regression, decision trees and neural networks, in detecting credit card fraud. with regard to accuracy and detection rate, the research found that ensemble methods, such as random forests and gradient boosting, outperformed single algorithms. logistic regression is a well-known algorithm that applies to supervised learning approaches for the detection of fraud (adeyeri, 2024). specifically, “it is a statistical method that “models the probability of a binary dependent variable, for example, presence versus absence or fraud versus no fraud, as a function of one or more independent variables”. logistic regression can also be an efficient and interpretable approach when there is a linear relationship between the features and the outcome. it is also useful in interpreting the importance of various features in the prediction of fraudulent transactions (venigandla & vemuri, 2022). unsupervised learning this is particularly relevant in the context of fraud detection in which obtaining label data is often hard but worse still it is impossible to obtain. on the other hand, supervised learning does not require labelled data to train the model. instead, it seeks patterns and outliers in the data that do not adhere to normality (khodabandehlou et al., 2023). k-means and more generally “clustering methods” are frequently included among the techniques used for unsupervised learning within the specific domain of fraud detection (huang et al., 2024). clustering categorizes similar data points based on characteristics. for example, k-means partitions the data into k clusters of like transactions. most of which are “anomalous” or “fraudulent” transactions. this would assist in detecting outliers in the data points which are often indications of a possibility of fraud (ali et al., 2021). another common approach used for fraud detection is the ‘anomaly detection’ algorithms, unsupervised (venigandla & vemuri, 2022; rojan, 2024). these are algorithms based on rare or anomalous patterns within the data which are unlike normal behaviour. anomaly detection has been done by means of “isolation forests” and “one-class svm” (support vector machines) amongst other methods. (wei et al., 2023). these models learn what the normal behaviour of the dataset is and treat anything that deviates from it as suspicious. in particular, anomaly detection could be very useful to detect a novel pattern for fraud that has never been previously experienced, and thus becomes an important technique to deploy against new fraud tactics. deep learning “deep learning” is, in contrast to other traditional ml models, a more powerful approach to searching on massive datasets for more complex patterns of fraud, as it uses artificial neural networks containing multiple layers to model very complex relations in the data instead of traditional ml models (xuan et al., 2021; bello & olufemi, 2024). on top of that, large amounts of unstructured data like images, or transaction texts/narratives, which are harder to deal with by classical models, are also easily accommodated by these models. two examples of deep learning models that are used effectively for fraud detection are convolutional neural pa ge 18 1 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 networks (cnns) and recurrent neural networks (cnns) (adeyeri, 2024). convolutional neural networks, typically employed for image recognition, have the potential to be utilized for fraud detection as they excel at detecting patterns in an input of sequences or time series data, such as sequences of transactions (bello et al. 2022; bello & olufemi 2024; chowdhury 2024). rnn’s, and specifically “long short-term memory (lstm)” networks, are focused on spotting temporal dependencies within the data and therefore have good potential to be utilized for fraud detection in cases where the sequence of the events in relevant (bhuiyan et al., 2025; mienye et al., 2024; muthunambu et al., 2024). an example could be that an rnn can recognize fraud by the transactional pattern, or by the attempted login pattern recognition. deep learning methods have the advantage of being able to detect more complex patterns of fraud than what simpler machine learning methods would be able to detect. these types of models are bolstered when exposed to more data and so are particularly suited to be used in environments such as digital banking where also the fraudsters are constantly evolving their methods. ensemble methods the “ensemble methods” combines the predictions of multiple models to improve accuracy and robustness (olowu et al., 2024). the theory behind ensemble methods is that by ensembling many models that each have some strengths and weaknesses we can form a stronger, more accurate model. “boosting”, which focuses on those training instances that are harder to classify by changing their weights iteratively, is one of the most popular ensemble methods. “gradient boosting” and “xgboost” are widely used boosting techniques which have proven effective when used for fraud detection tasks (ganaie et al., 2022; khan et al., 2023). “bagging” or bootstrap aggregating is another form of ensemble method as described by hernandez t al., 2022; vens, 2013. bagging takes multiple models (usually decision trees) trained on multiples subsets of data and averages they predictions. it is also a mechanism to reduce variance and avoid overfitting, especially for decision trees. an example of bagging is the “random forest” algorithm, which aggregates many decision trees for better predictions. detecting fraud using integrated systems based on nlp, anomaly detection, and supervised learning offers 15-25% higher detection levels than using any of the three types of models individually, as shown in a sample study of 25 large financial institutions (olowu et al., 2024). ensemble methods have special applicability for fraud detection, since the trade-off between false positives and false negatives in that context is very important. data preprocessing high quality data is a requisite condition for effective performance of machine learning models. the first and most important step when creating any fraud detection model, is data preprocessing; this step ensures that the data is cleaner and structured for ease of use. data cleaning typically deals with issues of missing, duplicate, or inconsistent data. records may be incomplete for technological reasons, such as system errors, or human reasons, such as input error. such gaps must be filled because they are sources of bias or inaccuracy in predictions. common methods include imputation by filling values with mean, median, or mode, or excluding records with missing values (alam et al., 2023). this is often called “feature extraction” the process of determining which variables (or features) found in the data are useful in order to improve the accuracy of the model. for instance, the features of interest in the case of fraud might be the transaction amount, time, location and number of transactions. identifying these characteristics in the input data is useful to provide lower dimensionality to the dataset and to allow the model focus on the important variables to make the prediction (cherif et al., 2022; islam et al., 2025). these are known as “feature transformation” techniques that can also be used to normalize or standardize the features so that all variables are treated equally in the model. for example, the learning from data which contains large numerical values might be biased if they are not normalized. two common ways of transforming data are known as “z-score normalization” and “min-max scaling”, which normalize the data to a common range or distribution (bello et al., 2024). effective preprocessing is important for the training of accurate fraud detection models. every properly cleaned and well-engineered data can significantly improve the performance of machine learning algorithms, which can in turn lead to more reliable fraud detection. model evaluation and metrics model evaluation is a crucial step in fraud detection modelling to ascertain that the models used are performing effectively and can be relied on. several performance metrics are analysed in order to assess the model’s capability of being not only effective at detecting fraud, but also being able to minimize false positives, as well as minimizing frauds that are missed. a common metric to apply is “accuracy”, which may not be a sufficient measure in the case of fraud detection as there is an imbalanced class problem in which the fraudulent transactions are a very small count as compared to the legitimate ones (tejesh et al., 2025). instead “precision” and “recall” are usually more useful. precision is the percentage of actual positive cases of fraud that were predicted to be fraud out of all cases that were predicted as fraud, and recall means the percentage of actual positive cases of fraud that were correctly predicted by the model (dangsawang & nuchitprasitchai, 2024). the “f1score”, being the harmonic mean of precision and recall provides a single measure that is used to balance both and is particularly useful in cases of class imbalance. receiver operating characteristic area under the curve pa ge 18 2 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 (roc-auc) analysis is another commonly used metric, which examines the true-positive versus false-positive ratio across various thresholds (trucco et al., 2019). these measures help account for the fact that there is a trade-off between detecting fraud and annoying a nonfraudulent customer. these metrics are hence optimized since, in order to provide useful and accurate outputs, aibased fraud detection tools need to be able to detect fraud in real time, while not impinging unduly on legitimate users. applications of ai-driven fraud detection in digital banking in the last few years, a number of banks and financial institutions have adopted aiml systems successfully into their fraud detection processes and have seen major benefits in their fight against fraud. this is especially clear in the case of “ai-based credit card fraud detection systems”. for example, big banking companies like american express and citibank already implement real-time ai solutions that mine massive datasets of transactions by identifying patterns within this data (mejia, 2019; owen, 2021). the systems flag these unusual behaviours, such as when international transactions of high dollar amount suddenly increase or spending habits shift rapidly, as potentially fraudulent. these ai models keep learning from every new transaction they analyse, thus getting better over time in identifying new types of fraudulent schemes and minimizing false positives. the other is “anti-money laundering (aml) systems”, which also have been successful in deploying ai to detect fraud. these days, banks and financial institutions apply ai to detect suspicious transactions connected with money laundering processes (oyedokun et al., 2024). for instance, systems can identify transaction networks in patterns typical of money laundering, such as layering and integration stages. these examples capture the use of ai for the augmentation of fraud detection systems, demonstrating its capability as a solution to combat more complicated forms of financial crime that cannot be addressed through conventional methods. ai-powered fraud prevention systems ai-driven fraud prevention technology is now embedded in the core of banking technology infrastructure and offers automated, real-time fraud prevention solutions. rather, banks want to deploy machine learning models for security by means of analyzing transactions on the fly. these can automatically identify cases where the behaviour of a specific user deviates from what is considered normal – for instance, either an unusual transaction or a connection from an unusual place – and notify the authorities to investigate. working in real time is one of the major benefits of ai for fraud prevention. while rule based systems are checked and worked on in batches or through review by a human, an ai system has the capacity of running continuously and being reactive in real-time to flags of suspicious behaviour. should the ai then determine a transaction to be suspicious, it may automatically block it from going through or request additional verification from the customer, preventing possibilities for a fraudulent transaction, while still allowing for a frictionless experience for valid customers. plus, ai models are very effective in minimizing false positives, or transactions wrongly identified as fraudulent. the high number of false alerts in conventional fraud detection systems can inundate bank employees and result in customer dissatisfaction. on the contrary, machine learning algorithms can train based on historical data so to increase time by time their accuracy in classifying legitimate versus fraudulent transactions. also, ai fraud prevention systems are continuously updated to be ahead of new fraud strategies. they also improve detection as the fraudsters get better by adapting to new schemes, all without the need for humans to intervene. integration with existing systems another important move towards securing transactions is the deploying of ai fraud detection systems integrated with banks’ current infrastructures. most banks already have their established processes handled through legacy systems and any ai implementation needs to blend into existing systems without hindering active operations. therefore, the secret to achieving and maintaining this integration is to use apis (application programming interfaces) and data pipelines between the old and the new technology (adeleke et al., 2024). an example of this type of system are transaction monitoring systems, in which data from several banking services, like mobile banking, online payments, and atms, are collected in real time and introduced into machine learning models for fraud detection purposes. banks are able to forward transactions data to ai, that in turn detects fraud patterns. it enables an easy flow of information between platforms so that suspicious activities can be acted upon right away. also, ai systems can more readily and easily be connected to cloud computing platforms that provide the processing power to leverage use scale data analyses. cloud computing offers the possibility of storing big data sets and training and deploying machine learning models without the need for expensive local infrastructure. this is helpful for smaller financial institutions that may not have the means to develop and maintain such difficult fraud detection systems. in addition, when ai is merged with other methods, it gives room for banks to enlarge their capabilities to prevent fraud. in line with digital banking, the number of transactions is increasing, and these transactions can all be handled in real time through ai systems that are capable of learning patterns of fraud as the volume of such data increases, thereby, providing a strong level of security as banks go digital. challenges and limitations using ai for detecting fraud in the banking sector include pa ge 18 3 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 to anonymising data employed in training these ais and customers would have to provide their consent. plus, independently of gdpr, “payment card industry data security standards (pci-dss)” is another relevant mandate that governs ai systems when payment data is involved. pci-dss mandates that all financial institutions and any third-party vendors engage in rigorous security practices to protect cardholder information. specifically, regarding sensitive payment data, it is critical that ai systems designed to address fraudulent credit card transactions are constructed with a view not to violate these standards (onyekwuluje et al., 2025; shaul & ingram, 2007). also, “anti-money laundering (aml)” laws also define the role of banks and financial institutions in detecting money laundering. it is, therefore, very important that banks adhere to regulations regarding money laundering and that ai systems are designed in such a way that they can effectively recognize patterns of money laundering in order to safeguard financial transactions and keep them secure and intact (oztas et al., 2024). data privacy keeping user data private without compromising on effective fraud detection is among the biggest issues for ai systems in the digital bank arena. but, as financial institutions increasingly turn to ai models to sift through large pools of sensitive customer data to identify potential indicators of fraud, the response to this risk cannot compromise individuals’ privacy to achieve security. most machine learning and deep learning applications of ai must be fed large sets of data, often including past transactions, customer information, and even biometric data. these processes and storage also create privacy concerns about the data since if a breach occurred an individual’s financial information could be made public. to alleviate the aforementioned issues, banks should adopt international data protection measures. this includes practices like encrypting and anonymizing data, securely storing data to prevent unauthorized access, and other measures to avoid data leaks. plus, also in line with data minimization principles, their collection and processing should not exceed what is necessary to detect fraud. also, ai systems should necessarily be developed in line with privacy laws like gdpr, which grants individuals access and control rights over their data, including the ability to consult, amend, or delete it. financial institutions should also ensure explainability of the ai models that detect fraud so that customers are aware of and can question data-usage by ai systems that might negatively impact them. finding the right equilibrium between effective fraud detection and data privacy requirements is crucial to ensure trust and legal compliance in the context of digital banking practices. future directions and innovations promising technologies such as federated learning, explainable ai (xai) and quantum computing, are several benefits, although, it is not without challenges. privacy is one, as ai often needs access to highly-sensitive data on customers in order to operate, increasing the chance of breaches. likewise, the expensive financial cost of executing more complex ai innovation, like deep learning networks, may be an obstacle for some banks. moreso, low prevalence of fraudulent transactions makes it difficult to obtain high-quality labelled data to train the models, resulting in imbalanced datasets. these challenges need to be overcome to use the potential of ai to help in fraud prevention. ethical and regulatory considerations the employment of artificial intelligence and machine learning technologies specifically in fraud detection has brought about considerable ethical issues, specifically regarding “bias”, “fairness”, and “transparency” (adhikari et al., 2024). among these is machine learning bias. these models are usually trained on past data that can include societal biases. if biases in the datasets used to train the ai models are not carefully filtered out, the ai systems can reproduce these biases with unsound and unmeritocratic results. an ai trained on biased historical data could, for example, identify some population group as more likely to commit fraud, despite being actually no more at risk than others. ai fairness is yet another primary issue. artificial intelligence fraud detection must be fair in that there is no abusing of a human being; no one is discriminated against, all customers are treated equitably, regardless of race, gender, socio-economic status, etc. this is especially problematic within financial services, where discriminatory conduct can lead to financial harm, loss of banking access, or being wrongfully accused of fraud. “transparency” is also of great importance in the context of using ai for fraud detection. banks should only use ai-based decision models that are transparent and the reasons for decisions can be explained. particularly when the ramifications are significant, such as blocking a transaction or freezing an account, it is important for both customers and regulators to comprehend the reasoning behind the outcomes of ai models. the absence of transparency can promote “black boxes” in ai systems, raising issues of trust concerning fairness and accountability (ismaeil, 2024). regulatory compliance ai technology fraud detection programs are covered by much of the same financial regulations, as any acceptable program must use in order to be legally practiced. this is particularly true for sectors that involve sensitive financial data like banking. perhaps more importantly, the most pertinent of these frameworks is the “general data protection regulation (gdpr)” which sets stringent protocols around personal data collection, processing, and storage (adaji et al., 2025). ai systems for fraud detection would also need to be regulated under gdpr to safeguard customer information, translating pa ge 18 4 https://journals.e-palli.com/home/index.php/ajfti am. j. financ. technol. innov. 3(1) 177-187, 2025 poised to take the future of ai and ml in fraud detection to thrilling new levels (odeyemi et al., 2024). because quantum computing can process large sets of data with far greater speeds than classical computers, this technology can revolutionize fraud detection. these systems could be orders of magnitude faster and efficient in the search for fraudulent patterns, patterns that could be analysed even in real time, patterns that previously could not be. a rising second area of interest is explainable artificial intelligence (xai), which aims at increasing transparency and interpretability of ai models. since ai is now moving into more critical domains such as fraud detection, we need to ensure that the decision-making process is interpretable to humans. because xai provides more transparency into how models come to conclusions, it can help foster trust amongst customers and regulators. one new technique, called federated learning, is a new way of training machine learning models in which the modelling occurs on distributed devices or servers rather than a centralized server, helping financial institutions to cooperate in building a better fraud detection system without the need to share sensitive customer data. the benefits include improved privacy and security as well as, more effective training of models across institutions. collaboration and cross-industry solutions a rise in the complexity of fraud as well as reliance on digital banking explains the growing collaboration between industries to produce more efficient fraud detection systems. collaborative effort between banks, tech companies and regulators is needed to combat the ongoing evolution of digital fraud. partnerships between the banks and tech organizations could provide the opportunity to create the best and newest technologies based on current trends including artificial intelligence, machine learning and cyber security. regulators also have an important role in enforcing legal and ethical issues with regards to fraud detection through the use of ai. international cooperation is also necessary since fraudulent schemes are frequently international. collaborating with foreign institutions can provide the ability to exchange information, deal with international fraud and consolidate financial security methods and protocols. the role of blockchain another technology that could meaningfully complement aipowered fraud detection is blockchain, which has been described as a way to increase transaction security and transparency. as a distributed and tamper-proof record of transactions, blockchain technology makes it possible for transactions to be public and permanent. concerning fraud detection, blockchain technology can minimize the ability to commit fraudulent transactions such as manipulating transaction information, stealing identities, and laundering money. one illustration of this is the transparency of blockchain where once a transaction is imprinted it cannot be changed creating a secure audit record. it becomes very difficult to alter transaction data or be fraudulent without detection. in addition, ai systems for fraud detection can take advantage of the decentralized structure of blockchain to improve accuracy of fraud detection by comparing transaction data across different networks. ai and blockchain combined can contribute towards a more robust and trustworthy financial system. conclusion this study looked at how artificial intelligence or, ai and machine learning or ml have transformed the detection and prevention of fraud in digital banking. digital banking contributes to accessibility, efficiency, and financial inclusion. the downside to this is also a rise in more complex forms of fraud, requiring advanced technology to combat it . existing traditional fraud detection practices which are mostly rule-based, can no longer meet the complexity and changing nature of the digital fraud. ai and ml provide a more flexible and anticipatory mode to the detection of fraud. these technologies are able to recognize intricate patterns as well as alerts to fraud instantaneously which hugely increase the capability of detecting fraud with minimal false positives. several ml methods such as supervised learning, unsupervised learning, deep learning, and ensemble methods were reviewed and have each been successfully used to fight fraud. on top of that, ai systems make fraud prevention more efficient because they continuously learn and adapt as new threats arise. the ethics and regulation of deploying ai for fraud detection is extremely important, such as aspects of bias, transparency, and privacy of data. financial institutions are obliged to abide by regulations like gdpr, pci-dss, and aml laws, while at the same time maintain effective fraud detection mechanisms and the need to protect customer data. as digital banking is new, so must the security mechanism. while the use of ai and ml in fraud detection is a game changer, the constant need for development and research is imperative to keep up with more complex and advanced forms of fraud. only through the cooperation between banks, technology companies, and regulators can we come up with complete and effective fraud prevention solutions. in the future, ai, machine learning and blockchain technology integrated can have the potential to build a safe, secure, transparent and resilient digital banking environment that will build trust and protect customer assets in digital economy and digital banking. references abdelrhman, a. b. 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