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 cross- sectional 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 (Al- Najjar & 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 (Vergara- Fernández 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 risk- free 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. (2023). 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