Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3, 1088-1095 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 10 January 2025; Revised: 21 February 2025; Accepted: 24 February 2025; Published: 13 March 2025 * Correspondence: jmaribel16@gmail.com Impact of the implementation of a digital tool with artificial intelligence in the financial assessment of MSMEs in Chimbote Juana Maribel Manrique Plácido1*, Luis Alberto Torres Garcia2, Luis Miguel Mosquera Aldana3, Norma Socorro Ramirez Tineo4 1,2,3,4Catholic University of Los Angeles of Chimbote, Chimbote, Perú; jmaribel16@gmail.com (J.M.M.P.) ltorresg@uladech.edu.pe (L.A.T.G.) luismosqueraldana@gmail.com (L.M.M.A.) normiramirez.t@gmail.com (N.S.R.T.). Abstract: This study analyzes the impact of implementing a digital tool based on artificial intelligence in the financial assessment of MSMEs in Chimbote, specifically through the design and implementation of a digital tool. A quasi-experimental design was used with two groups of MSMEs (control and experimental). The results showed that the tool significantly improves indicators such as liquidity, management, solvency, and profitability, reduces errors, and optimizes financial processes, increasing precision and efficiency in strategic decision-making. The methodology, with a quantitative approach and quasi-experimental design, included pre- and post-test surveys. This study concludes that the integration of AI in financial assessment is essential to modernize accounting practices and strengthen business competitiveness, and it recommends promoting training and the use of complementary technologies to enhance its implementation. Keywords: Artificial intelligence, Decision-making, Digital tool, Financial assessment, MSMEs. 1. Introduction Artificial intelligence has established itself as a key tool that enhances and optimizes current digital financial processes, as well as in other business sectors. Its use in financial assessment is growing steadily, allowing companies to increase their competitiveness and efficiency through the automated analysis of large volumes of data. These technological solutions offer business owners the ability to automate financial evaluations that previously required human intervention, aiming to improve strategic decision-making and business management [1-4]. Moreover, artificial intelligence has had a significant impact on financial inclusion globally [5, 6] driven by structural changes such as globalization and recent health crises [7]. AI-based digital tools enable companies to innovate in financial management by providing real-time data that fosters operational efficiency and optimizes managerial decisions in increasingly competitive markets [8]. Supported by AI, these decisions are based on more accurate analyses, allowing companies to respond quickly to changes in the economic environment [9]. In accounting, artificial intelligence automates repetitive tasks through algorithms, enabling accountants to focus on analysis, reducing errors, and improving decision-making efficiency [10]. These algorithms facilitate the creation of tools that enhance transparency and accuracy in audits and algorithmic impact assessments in financial decision-making [11]. The application of these technologies is fundamental to regulating and properly managing the use of AI in business financial assessment [12]. This evolution, as highlighted by Ayuso and Esteban [13] has allowed machines not only to replicate human processes but also to anticipate possible changes through continuous learning. A study was conducted on Baltrans Export Cía. Ltda. to assess its financial situation. Using quantitative and qualitative methods, the researchers analyzed accounting reports and conducted https://orcid.org/0000-0002-6880-1141 https://orcid.org/0000-0002-5958-494X https://orcid.org/0000-0001-5524-5695 https://orcid.org/0000-0001-7991-8380 1089 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1088-1095, 2025 DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate interviews. The results revealed deficiencies in net profitability, negatively affecting business performance. Despite achieving a financial efficiency index of 74.5%, indicating adequate management, the study highlighted areas needing improvement to optimize financial decision-making and ensure sustainable growth [14]. Artificial intelligence is also recognized as a key driver in global financing, helping companies process financial data more quickly and accurately [15]. The use of robotics and expert systems in financial assessment is emphasized, adapting these technologies to the specific characteristics of each company [16]. AI-assisted tools are useful for managing daily financial tasks, improving the efficiency of business processes [17]. Business financial management must adapt to emerging threats such as identity theft or money laundering [18]. Integrating AI technologies allows companies to anticipate risks and optimize financial decision-making through the automated analysis of large volumes of data [9]. According to [19] the strategic use of artificial intelligence in the financial industry enables companies to gain superior competitive advantages both nationally and internationally [20, 21]. The use of digital tools with artificial intelligence in accounting has revolutionized the way companies manage the calculation of financial indicators. By analyzing large volumes of data, AI allows accountants to automate repetitive tasks, optimize auditing processes, and perform complex financial calculations with greater precision and speed. This not only improves efficiency in detecting financial anomalies but also facilitates data-driven decision-making. By implementing AI, companies can minimize human errors, detect irregularities early, and strengthen their internal control systems, ensuring more robust and reliable financial management [22, 23]. By understanding and analyzing financial ratios, MSMEs can make more informed decisions in various key areas of management. Liquidity ratios, for example, allow these companies to evaluate their ability to meet short-term financial obligations, such as paying suppliers or managing salaries, while activity ratios reveal the efficiency with which they manage assets such as inventories and accounts receivable. This information is crucial for MSMEs to identify areas for improvement, optimize resources, and develop strategies that promote sustainable growth in the market [24-26]. The study contributes to streamlining business decision-making through the integration of digital tools and artificial intelligence (AI), optimizing financial assessment. AI-based tools improve the efficiency and accuracy of accounting processes, driving their digital transformation. Additionally, they redefine the role of accountants, who must complement their expertise with advanced technological skills. This approach facilitates greater adaptability and competitiveness in a constantly evolving accounting environment [27-29]. Micro and small enterprises (MSMEs) in Chimbote face significant challenges in financial decision- making due to the lack of advanced technological tools that enable them to accurately evaluate their financial indicators. Dependence on manual and outdated processes leads to errors, delays, and limited responsiveness to changes in the economic environment, directly impacting their competitiveness and sustainability. In a context of digital transformation, MSMEs must quickly adapt to new technologies such as artificial intelligence (AI), which can automate financial assessment and provide more accurate, real-time analysis. However, many MSMEs have not adopted these tools due to a lack of knowledge or resources, exacerbating their situation in an uncertain economic environment. The implementation of a digital tool based on AI could significantly improve financial decision-making, but its concrete impact on performance is unknown, justifying the need for a study to evaluate this effect on MSMEs in Chimbote. Given the mentioned problem, the following general objective was proposed: To evaluate the impact of implementing a digital tool based on artificial intelligence in the financial assessment of MSMEs in Chimbote, with the aim of optimizing precision and efficiency in strategic decision-making. 1090 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1088-1095, 2025 DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate 2. Literature Review 2.1. Digital Financial Tool with Artificial Intelligence (AI) A digital financial tool with AI is a technological solution that automates the analysis of financial data and measures key ratios such as liquidity, profitability, and debt. By leveraging artificial intelligence, these tools identify patterns, predict financial issues, and provide recommendations to enhance decision-making and risk control. They facilitate real-time analysis and help companies, including micro-enterprises, evaluate their financial performance and increase their competitiveness [30-32]. AI-powered digital tools are transforming financial assessment in companies by automating processes and improving the accuracy of risk evaluations. Technologies such as predictive analytics models and machine learning allow the identification of patterns within large volumes of historical data, making it easier to predict future events. These tools not only help mitigate financial risks but also optimize strategic decision-making. Data analytics platforms enable financial managers to centralize risk management, monitor results, and adjust strategies with greater agility and efficiency, strengthening companies' ability to face economic uncertainties and changing regulations [33, 34]. Artificial intelligence is defined as the simulation of human intelligence in machines, capable of solving complex tasks in various business environments [3]. These advancements allow for more efficient management of large volumes of financial data, streamlining key processes for business decision-making [35]. Although AI does not aim to replace human experts, its integration into financial management complements analytical efforts, enhancing profitability and operational efficiency in companies [36, 37]. Various AI techniques, such as expert systems, neural networks, genetic algorithms, and fuzzy logic, are used to implement digital tools. In the accounting field, expert systems and artificial neural networks are the most commonly employed [38]. 2.2. Expert Systems Expert systems are computer programs that simulate human reasoning in specific areas of knowledge. They collect, process, and store information, enabling decision-making based on predefined rules. These systems provide users with reliable recommendations and optimal solutions for complex problems, even in uncertain situations. By utilizing a structured database with specialized knowledge, expert systems replicate the ability of a human to solve problems, proving highly useful in fields such as medicine, finance, and engineering [39]. 2.3. Neural Networks Neural networks are computational models inspired by the functioning of the human brain. They are composed of layers of interconnected nodes (artificial neurons) that process information in parallel. These networks can learn from data patterns and are capable of performing complex tasks such as image recognition, trend prediction, and the analysis of large volumes of information. Thanks to their self- learning and adaptive capabilities, neural networks are used in applications such as artificial intelligence, finance, medicine, and technology [39]. 2.4. Financial Assessment Financial assessment is a process that allows companies to analyze various investment alternatives to select the most profitable one, where through this evaluation, the costs and benefits associated with projects are compared, helping to determine their profitability and ability to generate corporate value. The main indicators used are the Net Present Value (NPV), the Internal Rate of Return (IRR), and the payback period, where this analysis is crucial to optimize resources and ensure the economic viability of the selected investments [40, 41]. Financial assessment is also a process that allows analyzing a company's economic health through tools such as ratio analysis and the interpretation of financial statements, being fundamental to identify 1091 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1088-1095, 2025 DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate the organization's operational efficiency, liquidity, profitability, and debt level. Based on this data, informed decisions can be made to improve financial management and long-term sustainability, as financial assessment is key to anticipating potential risks, optimizing resource use, and defining strategies for business growth and expansion [42]. 3. Methods This study employed a quantitative quasi-experimental design, a method that allows evaluating whether an intervention, in this case, the digital tool with AI, has a direct effect on financial assessment by comparing an experimental group with a control group through pre-tests and post-tests to measure the results [43, 44]. The sample size used in the analysis involved 40 MSMEs, categorized into an experimental group of 20 that used the digital tool with AI, and a control group of 20 that did not use it. For data collection, a questionnaire with 20 questions was utilized, with a pre-test applied before the intervention and a post-test applied afterward to measure the impact of this tool on financial assessment. To ensure the instrument's reliability, the Cronbach's Alpha coefficient was evaluated, achieving a value above 0.70. Data were analyzed using SPSS 27, and the Mann-Whitney U test was applied to assess significant variations between the groups. 4. Results and Discussion Evaluate the impact of using the digital tool through a pre- and post-test to measure its effectiveness in improving financial assessment in MSMEs in Chimbote. Table 1. Frequency Levels of the Liquidity Ratios Dimension Pre-test Post-test Level Control Group Experimental Group Control Group Experimental Group f % f % f % f % Deficient 11 55.0 13 65.0 11 55.0 0 00.0 Little efficient 9 45.0 7 35.0 9 45.0 12 60.0 Efficient 0 00.0 0 00.0 0 00.0 8 40.0 Total 20 100.0 20 100.0 20 100.0 20 100.0 Source: SPSS V.27 In Table 1, it is observed that in the pre-test, both the control group and the experimental group have a majority of participants with deficient levels (55% and 65%, respectively), while the rest exhibit an inefficient level. After the intervention, in the post-test, the experimental group shows significant improvement, with 60% reaching an inefficient level and 40% an efficient level. Meanwhile, the control group maintains the same levels as in the pre-test, indicating that the digital tool applied to the experimental group had a positive impact on the financial assessment of liquidity ratios. Table 2. Mann-Whitney U Test for Liquidity Ratios Test Statistic Liquidity Ratios Pre-test Liquidity Ratios Post-test Mann-Whitney U 183.000 5.000 Sig. 0.640 0.000 The data show that there were no statistically significant differences between the two groups before the intervention, as reflected in the pre-test. In contrast, the post-test results indicate a significant difference, with a sig. value of 0.000, demonstrating that the intervention applied to the experimental group had a positive effect on improving liquidity ratios. 1092 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1088-1095, 2025 DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate Table 3. Frequency Levels of the Management Dimension Source: SPSS V.27 The results show that the control group has 70% at a deficient level, while the experimental group shows 40% at this level and 5% at an efficient level. In comparison, in the post-test, the control group maintains 65% at the deficient level, while the experimental group improves significantly with only 15% at this level and 35% reaching an efficient level, indicating that the intervention was effective in improving management. Table 4. Mann-Whitney U test for Management Test Statistic Management Pre-test Management Post-test Mann-Whitney U 155.500 47.000 Sig. 0.216 0.000 Table 4 shows that in the pre-test, there were no significant differences between the groups before the intervention. However, in the post-test, statistically significant differences were detected, indicating that the intervention with the digital tool using AI notably improved management in the experimental group. Table 5. Frequency Levels of the Solvency Ratios Dimension. Pre-test Post-test Level Control Group Experimental Group Control Group Experimental Group f % f % f % f % Deficient 13 65.0 10 50.0 13 65.0 0 00.0 Little efficient 7 35.0 10 50.0 7 35.0 12 60.0 Efficient 0 00.0 0 00.0 0 00.0 8 40.0 Total 20 100.0 20 100.0 20 100.0 20 100.0 In the pre-test, the control group has 65% at a deficient level and 35% at an inefficient level. Following the intervention, the control group maintains a high percentage at the deficient level, while the experimental group significantly improves, reaching 40% at an efficient level. This indicates that the tool had a positive effect on the solvency of the experimental group. Table 6. Mann-Whitney U Test for Solvency Ratios. Test Statistic Solvency Pre-test Solvency Post-test Mann-Whitney U 168.000 16.500 Sig. 0.364 0.000 The results in Table 6 show that there were no notable differences between the groups before the intervention. However, in the post-test, a significant difference is observed, indicating that the intervention with the digital tool had a positive and considerable impact on the solvency of the experimental group. Pre-test Post-test Level Control Group Experimental Group Control Group Experimental Group f % f % f % f % Deficient 14 70.0 8 40.0 13 65.0 3 15.0 Little efficient 6 30.0 11 55.0 7 35.0 10 50.0 Efficient 0 00.0 1 5.0 0 00.0 7 35.0 Total 20 100.0 20 100.0 20 100.0 20 100.0 1093 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1088-1095, 2025 DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate Table 7. Frequency Levels of the Profitability Ratios Dimension. The results show that, in the pre-test, the control group has 80% of participants at a deficient level, while the experimental group has 70%. In comparison, in the post-test, the control group maintains 65% at a deficient level, while the experimental group shows significant improvement, with 40% reaching an efficient level. This indicates substantial progress in the group that used the digital tool with AI. Table 8. Mann-Whitney U Test for Profitability Ratios. Test Statistic Profitability Pre-test Profitability Post-test Mann-Whitney U 134.000 0.000 Sig. 0.071 0.000 Table 8 shows that there were no significant differences between the control and experimental groups in the pre-test. However, in the post-test, statistically significant differences were detected, indicating that the intervention with the digital tool using AI significantly improved profitability in the experimental group. 5. Conclusion The design of the digital tool based on AI met the requirements of end users, achieving a high usability score and validating its functionality for financial assessment, where the active participation of users in the design and validation phases ensured that the tool reflects their specific needs, standing out as an innovative and adaptable solution. The implementation of the tool in the experimental group significantly improved indicators of accuracy, error reduction, and user satisfaction, greatly surpassing the control group. Additionally, the tool proved to be a key resource for optimizing financial processes and increasing the operational efficiency of MSMEs. The pre- and post-test analysis showed that the tool positively impacted financial indicators of liquidity, management, solvency, and profitability, achieving statistically significant results, confirming that the digital tool not only improves financial processes but also contributes to strengthening business competitiveness. It is concluded that the digital tool based on AI optimizes financial assessment in MSMEs in Chimbote, improving accuracy, reducing errors, and contributing to more efficient and sustainable financial management. Furthermore, the tool enables companies to anticipate changes in the economic environment, consolidating itself as a strategic technological solution. The positive impact evidenced in MSMEs in Chimbote suggests that the digital tool has the potential to be implemented in other regions or economic sectors with similar financial modernization needs, which could contribute to local and national economic development. Pre-test Post-test Level Control Group Experimental Group Control Group Experimental Group f % f % f % f % Deficient 16 80.0 14 70.0 13 65.0 0 00.0 Little efficient 4 20.0 6 30.0 7 35.0 12 60.0 Efficient 0 00.0 0 00.0 0 00.0 8 40.0 Total 20 100.0 20 100.0 20 100.0 20 100.0 1094 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1088-1095, 2025 DOI: 10.55214/25768484.v9i3.5421 © 2025 by the authors; licensee Learning Gate Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Q. N. M. Diestra, A. J. C. 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