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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



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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 
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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 
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(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 



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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.



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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 



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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.



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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 



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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.

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