Submission Deadline-Today
June 2024 Issue : Publication Fee: 30$ USD Submit Now
Submission Deadline-20th July 2024
Special Issue of Education: Publication Fee: 30$ USD Submit Now

Influence of Credit Risk Identification on the Financial Performance of Commercial Banks in South Sudan

Influence of Credit Risk Identification on the Financial Performance of Commercial Banks in South Sudan

Simon Songa, Dr. Brian Singoro, Dr. Fred Gichana Atandi

Department of Economics, Finance, and Accounting, Kibabii University, Bungoma Kenya

DOI: https://dx.doi.org/10.47772/IJRISS.2023.701073

Received: 28 September 2023; Revised: 02 October 2023; Accepted: 05 October 2023; Published: 04 November 2023

ABSTRACT

Banks rely on their profitability to mitigate the chaotic nature of the dynamic environment. High financial performance contributes to the financial institutions and market development of nations. Banks’ primary function is financial intermediation, but this function entails a risk of its own which is the risk of default. The purpose of this study was to determine the influence of credit risk identification on the financial performance of commercial banks in South Sudan. The research was anchored on the following three theories: Asymmetric information theory, portfolio theory, and credit risk theory. The study adopted an explanatory research design. The target population for the study was 711 employees of the 28Commercial Banks in South Sudan. The sample size was 195 respondents comprising Branch Managers, Credit Risk Managers, and Loan Officers of the Commercial Banks selected using the purposive sampling method since they are the ones exclusively dealing with lending matters daily. Primary data was used. Structured questionnaires were used, which included closed-ended to collect the primary data. The study conducted validity and reliability analysis and found a Cronbach of 0.708. Kaiser-Meyer-Olkin (KMO) Measured of Sampling Adequacy of credit risk identification was at 0.732 which was above 0.5. Data was analyzed using descriptive statistics involves the use of mean, standard deviations, percentages, and frequencies, while inferential statistics involves the use of correlation and multiple regression analysis through Statistical Package for Social Sciences (SPSS). The confidence level was 95% with an error margin of 5%. The findings showed that the combined effect of credit risk identification activities influenced the financial performance of commercial banks in South Sudan positively. The study concluded that credit risk identification activities significantly influence the financial performance of commercial banks. This study recommends commercial banks’ managers and loan officers focus on enhancing their credit risk identification practices by implementing comprehensive and effective risk assessment frameworks. This can include facilitating the understanding of credit risk identification based on the products the banks offer, conducting thorough credit assessments before offering credit, evaluating potential risks associated with new loan services or products before launching the product, and engaging external experts to ensure a holistic approach to risk identification.

Keywords: Credit Risk, Financial Performance, Commercial Banks, Asymmetric information theory, ROA, ROE.

INTRODUCTION

Background of the Study

For banks to maintain sustained profitability and increased competition both in global and domestic environments, banks must respond strategically to issues arising from credit risk. This will entail the formulation and adoption of strategies that will guarantee the full repayment of both the principal and interest from the loan provided to clients(Mbiti et al., 2018). Credit risk management is congruent with the profit-maximizing goal of the bank and is evidenced by the emphasis of commercial banks on offering house and individual loan packages to productive low-risk clients. The volume and the extent of loss generated by credit risk compared to other risks are severe and can easily lead to bank failures (Richard et al, 2008). Over the past, there has been an increased number of significant bank challenges in both developed and developing nations. Credit issues, particularly a lack of strength in credit risk management, have been recognized to be a part of the main causes behind banking challenges. Economic and financial problems have been associated with the absence of risk management and a reluctance to embrace risk-based management practices that are more proactive (Bessis, 2015). Morgan (1997) claimed that credit risk has emerged as the primary risk management concern of the modern era.

Credit risk arises when a financial organization, such as a lender, is anticipating a payment contractually agreed upon between the financial institution and a counterparty, and the counterparty falls short of fulfilling its responsibility under this agreement, or defaults. In some situations, the payments will be late or not made at all. This causes troubles with cash flow, which has a negative effect on bank liquidity, and this is particularly consequential in that the majority of a normal bank’s balance sheet is associated with credit (Aldoseri & Worthington, 2022). Credit risk is a phenomenon that occurs when there is a modification or underestimate in the evaluation of the counterparty’s creditworthiness.  Historically, high non-performing loans have been evident as the driving force of numerous commercial bank failures and this has been evident with the collapse of numerous banks in both developed and emerging countries. These include banks in the United Kingdom, Northern Rock Bank (O’Connell, 2017), in Scotland, Pioneer Mutual Bank (Williams, 2021), Ireland Anglo Irish Bank which had non-performing loans (NPLs) worth over 87€ million (Chu, 2014), in Nigeria Savannah Bank Plc, Societe General Bank Ltd and Alpha Merchant Bank Ltd, in Kenya, Dubai, Chase, and Imperial (Masinde, 2016), in Tanzania, Twiga Bancorp (Tanzania Invest., 2018) and Greenland bank and Crane Commercial Bank in Uganda which had non-performing loans (NPLs) worth 122.9 % Senyonyi (2017) search for business advantage across the globe in the 1960s describe the birth of Credit risk management. In Uganda, Shieler et al., (2017) found that efficiency in credit risk management techniques such as credit risk identification, and credit risk appraisal has a greatly positive effect, and credit risk monitoring, and credit risk mitigation have a moderate positive relationship on the micro-finance firm’s financial performance.

In South Sudan, the bank sector has been characterized by precarious political instability, weakened public institutions, and widespread corruption (Ferullo, 2018). The macroeconomic imbalances have hampered efforts to maintain macroeconomic stability and the organization of the country’s financial sector IMF (2020). Asset quality measures in terms of non-performing against loans increased from 18.7% in 2014 to 54.9% in 2016. Liquidity measured in terms of liquid assets to total assets outstanding at 78.7% in 2014 as compared to 46.7% in 2016 (IMF., 2020). Overall, the banking sector performance in South Sudan indicates a negative image. This resulted in some banks closing down due to weaknesses in managing their credit policy which included South Rock Bank in 2019, Liberty Commercial Bank in October 2020, and Charter One Bank in October 2021 Central Bank of South Sudan (2020). It is from the foregoing background that this study aims to determine the influence of credit risk identification on the financial performance of commercial banks in South Sudan.

Statement of the Problem

Banks rely on their profitability to resist the chaotic nature of their climate. High financial performance contributes to the progress and development of nations. Banks’ primary function is financial intermediation, but this function entails a risk of its own which is the risk of default. South Sudan’s banking industry has been faced with several challenges, including myriad internal issues as well as macroeconomic constraints, the high inflation rate hit 830 % while the currency value traded at 123 units again the US dollars as compared to 2.95 in 2015 (Jefferis & Haas, 2017).  The credit situation is a catastrophe. The credit growth rate of 61.2% in 2017 as compared to 11.7% in 2016, an increase from 13.5 as compared to 16.16% and also from -19.9 percent in 2020 percent, compared to 20.4 percent in the fiscal third quarter ended in March 2021 (South Sudan Economic Brief, 2021).The Central Bank of South Sudan (2013) pointed out that the transition from Islamic banking to Conventional banking following the country’s independence in 2011 caused an imbalance in the structuring of a new financial system which jeopardized the bank stability.

To continue to survive in a dynamic market, banks require strategies that center on how to minimize credit risk at the lowest possible cost. The techniques should effectively respond to the problems that banks encounter in attempting to recover loans issued to the public as well as interest. If advanced and competitive approaches are properly implemented, banks can attain long-term and sustainable profitability and competitive advantage. Commercial banks have used various strategies which have resulted in various performance outcomes. Commercial banks have employed different strategies which have generated different performance results. With the various outcomes, one could wonder just what the most effective techniques or approaches are for a bank to implement to entirely remove credit risk or loan defaults.

Credit risk management technique is a topic of concern in financial institutions today and there is a need to come up with better strategies to provide better outcomes for future success. Proper credit risk management techniques reduce credit risk and, consequently, the volume of loan losses. Financial health is a top priority for all managers in the banking industry. Strategic credit risk management is equally critical for commercial banks. Management needs to minimize the risk of loan default since the loss of principal and interest weakens the banks’ financial health; also, banks operate with the goal of maximizing profits to individuals, which includes the duty of offering loans to assist individuals in boosting their standard of living. These duties may jeopardize commercial banks’ financial viability if management becomes lax in their lending procedures for evaluating and assessing the credit risk of individual borrowers. This study attempts to determine the influence of credit risk identification activities on the financial performance of commercial banks in South Sudan.

Purpose of the Study

To determine the effect of credit risk identification on the financial performance of commercial banks in South Sudan.

Research Hypothesis

Ho; There is nosignificant relationship between credit risk identification on the financial performance of commercial banks in South Sudan.

Scope of the Study

The study covered the 28 commercial banks that are subjected to the Central Bank of South Sudan concentrating on main offices in Juba City (CBSS 2021). These banks consisted of banks located in Juba since major banks have their busiest and main offices in Juba and most of their headquarters are in the capital Juba.

Conceptual Framework

The conceptual framework aids in formulating the researcher’s major ideas and lays out the researcher’s instruction and direction (Shikalepo, 2020). The conceptual framework has been formulated in this research to indicate the effect of credit risk identification on the financial performance of commercial banks. The arrows aid in the comprehension of the relationship and the effects of the variables.

Figure. 1 Conceptual Framework

Source: Researcher (2023)

LITERATURE REVIEW

Asymmetric Information Theory

The theory of Asymmetric Information also referred to as imperfect information was developed by Akerlop in (1970). With the asymmetric information put forth, there is an information imbalance when it comes to screening potential bank candidates. According to the theory, there is a situation in banking where some agents in banking transactions have access to more information than others (Ekumah & Essel, 2003). The theory is based on the idea that one party in a transaction has access to enough information than the other and may choose to withhold the information from the other party for personal gain. About the banking business, lenders have little knowledge of the future ability of the borrower to fulfill their credit obligations. Borrowers unveil few details to lenders, which makes it difficult for lenders to accurately assessment of the borrower of the creditworthiness (Binks & Ennew, 1997). Although the lender may consider the borrower’s credit record as proof of their financial strength, very little information is typically disclosed. Due to this, the lender finds it challenging to determine how often the borrower defaults on or responsibly manages his loan (Auronen, 2003).  This will make it difficult to distinguish between good and bad borrowers, which could result in moral hazard and adverse selection. It is difficult for the lender to determine whether it is likely that the customers will default (Onuko et al., 2015).

Accorded to (Kipyego & Wandera, 2013)failure of lenders to arbitrarily choose borrowers, the same rate is applied to both deserving and undeserving borrowers, causing the bank to overcharge deserving borrowers at the expense of deserving borrowers. When borrowers accurately reveal their financial status to lenders, the bank is more likely to make informed lending decisions, which lowers the risk of those decisions and the amount of non-performing assets.

Despite being a very practical theory, the theory has its flaws. The first flaw of the theory is that it only applies to extremely simplified versions of markets with a limited number of possible actors or states, making it difficult to manipulate mathematically to account for the complexities found in real-world markets (Auronen, 2003). Second, it only takes into account asymmetries application in one direction in this case, the borrower, even though the lender may also limit the borrower’s access to information, which could lead to default. However, there may be also information gaps that work in favor of the other party. Third, presumes that borrowers are always aware of the factors that could make it difficult for them to repay debt, but this may not always be the case because some factors are such that, as unpredictability, debtors are unable to know about them (Auronen, 2003). The theory is applicable because it suggests that if borrowers disclosed all relevant financial data to lenders when applying for credit, the banks would be in a better place to make educated credit decisions and mitigate the risks connected with extending credit to borrowers, i.e. that bank’s failure to have enough information of the customer puts the lender at risk of loan default in even that borrower fails to repay, it can be a challenge for the bank to track them, which poses a threat to the financial stability of the bank. On the other hand, customer’s failure to get enough access to the information about the product of the loan and the banks put customers at risk of being unable to access loan terms and conditions which can also lead to loan defaults, and poor repayment performance of loans (Edwin & Omagwa, 2018). When credit risk is decreased, the amount of NPA decreases as well, giving financial organizations a better portfolio quality (Onuko et al., 2015).

Portfolio Theory

The theory of Portfolio frequently referred to as the Theory of Modern Portfolio was developed by Markowitz (1927), through a sequence of articles published in 1950. James (1983) and William (1934) among other scholars through the subsequent years. The portfolio theory describes the connection between the risk associated with investment and the expected return on investment by using investment portfolios (Wong, 2013). According to the theory, the method of efficient portfolio creation to the pricing of assets of an individual, since some reason for risk associated with individual assets can be disregarded or diversified away by maintaining a suitable asset mixture.

Modern portfolio theory is one of several important and pivotal economic models that are applied to finance and investments. Markowitz (1991) developed the concept of portfolio selection and efficient diversification of investment in 1959. According to Markowitz (1927), investors employing statistical strategies can predict the risk exposure, which is probably going to have an impact on their expected return on asset portfolios. This will provide portfolio benefits of diversification, after evaluating the risk, they then formulate approaches to lessen and eventually eliminate the risk. Hence, the relevant goal of the portfolio theory selects the ideal mix of these assets for portfolio efficiency.

Maier-Paape & Zhu (2018), asserted that the theory of portfolio holds the premise that firms should pay more attention to the importance of carrying out commercial transactions after taking into account the risks and expected returns. Esfahani et al., (2016) pointed out that the theory of portfolio allows organizations to incorporate a specific business approach for a specific expected return based on a specific value of the risk. According to Lee et al., (2016), Portfolio theory attempts to optimize returns or reduce risks from an investment transaction. This implies that organizations should expect some risk when implementing business techniques like credit risk management as long as the risks are lower than the expected returns.

Despite though modern portfolio theory being recognized broadly around the globe and also practical by several investment firms, the theory has also been criticized by several individuals, including those in the field of behavioral economics who question the modern portfolio theory’s underlying assumptions about the extent to which investors can be considered rational and their reasonableness in their return expectations(Westfall & Kvilhaug, 2021). The theory has been criticized, for instance, for taking into account previous performance, even though this does not necessarily guarantee the outcome that may occur in the future (Lukomnik & Hawley, 2021). Taking into account the previous performance occasionally contributes to overlooking the more recent events, which may not have been present when historical information was taken into account but may have a significant role in making decisions. However, this theory also presumes that returns on assets within a class of assets are distributed normally, which is demonstrated to be incorrect for individual stocks because changes in the correlations between asset classes over time are possible (Thakur & Vaidya, 2020).Credit is one of the major risky portfolios in banks, the portfolio theory asserts that banks should put in place credit risk management techniques that help reduce portfolio risk which would eventually increase the financial performance (Francis et al., 2022). Banks that expand financial services are more likely to have sound financial performance than those that do not have. Therefore, the theory is applicable in determining the link between credit risk management techniques and financial performance.

Credit Risk Theory

The theory of credit risk was developed by Macleod (1889) and Merton, (1974). According to the theory, the worth of credit depends on the borrower’s commitment to pay back his loan and the right that the lender obtains to demand from the borrower.  The theory describes the root causes of credit default and provides the rationale for lenders’ initiatives to identify, assess monitor, and control credit risk exposure (Chatterjee et al., 2020). The theory, however, was later criticized for not systematically differentiating between the various types of credit because it was unable to describe whether the credit supply consisted of paper currency, coins, bank notes, deposit accounts, or even tangible goods like products and or services (Lapavitsas, 1991).

Thakor (2016) further developed the credit risk theory by modifying it to bank credit risk management theory. According to the author, credit risk theory is grounded in rational learning, which leads to revisions in inferences about banking skills in a setting where those skills may or may not matter, depending on the state of macroeconomics that may be influenced by investor sentiment or changes in the real estate sector. Thakor (2016) suggests that commercial banks can select between a reasonably safe loan and a potentially more lucrative risky loan, with the kind of borrowing selected being publicly observable. The likelihood that the loan will be repaid depends on the extent to which the risk is associated with the credit. According to theory, there are two kinds of risk environments, including high and low risk, which are mutually exclusive. In high-risk environments, default risk is believed to be so high that investors are only willing to fund low-risk loans, even when the bank is considered highly skilled, while in a low-risk environment, riskier loans are funded if the bank is deemed capable of managing these risks (Thakor, 2016).

The credit risk theory states that every financial institution should create a credit risk measurement strategy to make it simple to identify, evaluate, monitor, and control credit risk to enhance financial performance (Francis et al., 2022). The theory’s limitation is that it is founded on rational learning, which results in revisions in inferences about banking abilities to successfully manage credit risk. Consequently, it is a trial-and-error approach that relies on learning from mistakes, but it fails to explain how banks can better prevent credit risk and improve financial performance (Wakaria, 2016). Despite the criticism, the theory is still valid and important in today’s study. The main advantage of employing this theory in this study is that it provides direction on the theoretical sources of credit risk as well as the necessary framework for extracting credit and credit risk information from the market. The theory can therefore be used to investigate how credit risk management procedures influence the financial success of commercial banks in South Sudan.

Empirical Literature Review

The first important phase of the risk management technique comprises the identification of risk. Risk identification aims to identify all pertinent risks at an early stage so that the bank can decide whether or not to extend credit. As a result, risk identification must adopt a comprehensive strategy to pinpoint the credit risk roots and classification of risk. Bülbül et al., (2019) pointed out that businesses must learn to anticipate disruptions in advance so that risks can be appropriately evaluated and mitigation measures can be put into place.

Karanja & Simiyu (2022)conducted a study on the effect of credit management practices and loan performance of microfinance institutions in Kenya. The study adopted a descriptive research design. The study adopted a linear regression model to analyze the data. The results showed that the debt policy was effective, and the findings indicated that the bank checks the customer’s creditworthiness before any loan was been issued. The study recommended that microfinance institutions develop and execute effective credit risk management practices through credit risk management information systems.

In Kenya, Karanja et al., (2018) examine the influence of credit risk monitoring on the lending performance of commercial banks performance in Nairobi County, Kenya. The research used a descriptive survey design. The data were analyzed with the help of descriptive statistics and logistics analysis (binary). The findings showed that credit risk monitoring has a significant influence on commercial bank’s lending performance. The authors further, conclude the effect credit risk monitoring activities on lending performance have significantly on the success of the whole industry.

Another study by Shieler et al., (2017) conducted a study on the effect of credit risk management techniques and financial performance of microfinance institutions in Kampala, Uganda. The study used the census survey method. Pearson’s linear correlation coefficient was used in the analysis of the data. The results show that credit risk identification and credit measurement have a strong significant positive effect on the financial performance of microfinance institutions in Uganda whereas, credit risk control, and credit risk monitoring have a moderately good link to the microfinance institutions’ performance. The study recommends that microfinance institutions should consistently emphasize good credit risk identification, credit risk appraisal, credit risk monitoring, and credit risk reduction approaches to achieve maximum financial performance.

Paul & Maniagi (2020) investigated the impact of credit risk management practices on the financial performance of micro-finance institutions in Nairobi, Kenya. The study used descriptive research design; and a sample size of 95 respondents.  Both descriptive statistics and inferential statistical analysis were utilized to analyze the data. The finding indicates that credit risk management procedures, credit risk grading procedures, credit risk identification procedures, credit risk control procedures, and credit reminder procedures all played significant roles in the financial success of micro-finance institutions in Kenya. The authors recommend that for firms to be more competitive to cope with the increasingly dynamic environment, management should keep on improving their credit risk management techniques.

In South Sudan, Olobo et al., (2021) investigated the impact of credit risk management techniques on the bank’s performance in Juba, South Sudan. The study adopted a cross-sectional survey. The study also used cluster, purposive, and sampling random methods to pick the sample size of 124 respondents from the target population. The data were analyzed with the help of a multiple regression model. The finding indicates that there is a significant positive link between credit risk management and banks’ financial performance.

In Uganda, Francis et al., (2022) examine the influence of credit risk management practices and loan performance of commercial banks in Mbarara City, Uganda. The study adopted a correlational research design and sample random sampling to select the sample size of 115 respondents from the population. The finding of the study showed a strong and significant relationship between credit risk assessment and loan results.  The authors concluded that banks thoroughly examine customers’ income outlay statements and other financial information and ensure a thorough measurement of the hazards that clients face and the techniques that they put in place to mitigate those risks.

Mbiti et al., (2018) conducted a study on the contribution of credit risk management techniques to the financial performance of commercial banks in Kilifi County, Kenya. The study adopted a descriptive research design. The findings showed that in banks with collaterals and credit protection in place, their profitability rises by controlling credit losses register changes in their profitability performance. The study concludes that credit protection is one of the important variables among credit risk management techniques for handling risks related to customers who operate in diverse customer-risk scenarios.

In Kenya, (Misiko et al., 2022) studied the credit risk management practices and financial performance of the Government Found Youth Group Enterprise in Webuyeeastsub-County, Bungoma County, Kenya. The study used a descriptive research design in describing and explaining the variables. The findings of the study showed that credit term policies (R = 0.335, p = 0.025) have a positive significance relationship with financial performance. The study concludes that the important training of government-funded youth businesses on credit term policies is to boost healthy financial performance and safeguard their survival in the dynamic environment for a long time by knowing their clients and retaining them.

In South Sudan, Mogga et al., (2018) studied the influence of credit risk management practices on commercial bank financial performance in Juba City, South Sudan. The study employed a descriptive research design to examine the influence of credit risk management on bank performance. The researcher used both descriptive and inferential statistics for the data analysis. The findings of the study showed that there is a positive, and significant relationship between credit risk management techniques on the financial results of commercial banks.

RESEARCH METHODOLOGY

This study used an explanatory research design and correlational research design being suitable for both preliminary and exploratory study allowing data collection, summarizing, presentation and interpretation for the reason of interpretation. Explanatory research design is a study approach employed to collect and analyze the data to determine and understand the features, opinions, behaviors, or attitudes of a specific population or sample. Saunders et al., (2018) argued that explanatory research design is suited for investigations that evaluate the causal effect between study variables. Explanatory research design enables the researcher to explain a phenomenon or problem in the shape of a causal effect between credit risk identification and financial performance. Primary data was collected using structured questionnaires. The study also employed a descriptive method of presentation including the use of tables and figures.

The target population for the research was 711 staffof the 28 Commercial Banks in South Sudan according to Central Bank of South Sudan (2021) staff-published report. The sample size was 195 respondents’ mostly Branch Managers, Credit Managers and Loan Officers using purposive sampling approach. The primary justification for selecting these groups was derived from the fact that, in the current banking dynamics, all branch managers, credit managers, and loan officers, regardless of the different divisions, are tasked with the role of facilitating loans in the banking organizations where they are working and have a deep idea on how credit risk identification influence financial performance and since, they are also the first committee on loans approvals and, in most situations, they heavily depend on the banks’ performance.  A pilot study was carried out where factor analysis of the questionnaire was determined which was above the threshold of 0.3(Costello & Osborne, 2005). Cronbach alpha was used to test for reliability stood at 0.708. Kaiser-Meyer-Olkin (KMO) measure of Sampling Adequacy of cash controls was at 0.732 which was above 0.5 proved that data set was fit for analysis. All citations were duly acknowledged

Reliability of the Research Instruments

Reliability was established by coding and keying in the data and conducting a reliability analysis. For the findings to be more trustworthy, the responses have to be consistent. In general, the higher the consistency, the more trustworthy they will be. The instruments utilized in this study have a satisfactory Cronbach’s Alpha of at least 0.7, indicating their reliability. An unreliable set of instruments would be indicated by a coefficient below 0.30, indicating weak agreement between the results of the two assessments. Reliability analysis findings are as indicated in Table 1 showing that the tools were reliable to be used in the final data collection and analysis.

Table 1: Reliability of the Research Instrument

Reliability Statistics
Variables Cronbach’s Alpha No. of Items
Credit risk identification 0.708 07

Source: Field Data (2023)

Data analysis

This study used regression analysis to determine the variability in credit risk identification of commercial banks by a set of independent variables viz; financial performance.

Model

Y= β0 + β1X1+ ε.

Where; Y = Financial Performance

X1 = Credit risk Identification

β0 = Constant Term, β1= Beta coefficients, ε= error term

FINDINGS

Response Rate

The sample size of the participants was 195. After the distribution of the questionnaires to the selected sample of 195 respondents, only 155 were able to successfully fill and return the questionnaires leading to a return rate of 79.5%. The return rate of 79.5% agrees with the recommendation by Zikmund (2010), that a response rate above 50.0% is sufficient for the generalization of the outcome of the results. The distribution is shown in Table 2.

Table 2: Response Rate

Description Total percentage
Targeted respondents 195 100%
Returned 155 79.5%
Not returned   40 20.5%

Source: Researcher (2023).

Descriptive Analysis

Data entry and screening were part of the data processing. The screening process included identifying and managing unrealistic values and missing data. The descriptive analysis was used to discover impossible values, and any impossible value was identified and corrected. The researcher guaranteed that the data was free of implausible numbers at the end of the process.

Credit Risk Identification on the Financial Performance of Commercial Banks in South Sudan

The study sought to find out whether commercial banks in South Sudan have effective systems and ways in place to enhance credit risk identification. This was achieved by measuring the respondents’ agreement levels with specific statements related to Credit Risk Identification within the context of commercial banking in South Sudan. This is because the availability of effective systems can help banks and affiliate financial institutions identify potential risks that are associated with lending.

Table 3: Credit risk identification

Credit Risk Identification 1 2 3 4 5 Total Mean Std Dev.
The bank has an effective system in place to improve credit risk identification when customers are taking a loan 14 11 37 49 44 155 3.62 1.218
The bank has intense competitive pressure on loan borrowing which makes it impossible to obtain accurate information on borrowers’ creditworthiness 24 74 31 18 8 155 2.43 1.051
Entering new markets may tempt management to make loans without conducting a thorough financial analysis of the borrowers. 9 6 35 63 42 155 3.79 1.067
There are formulated internal guidelines apart from those stipulated by the Central Bank of South Sudan on methodology to access borrowers when making loan decisions 8 14 32 46 55 155 3.81 1.167
Faster lending decisions based on the features of borrowers and the anticipated value of collateral have exposed banks to credit risk. 9 9 46 45 46 155 3.71 1.128
The banks have techniques for analyzing additional financial resources that the borrower can use to pay off the credit. 5 15 40 39 56 155 3.81 1.127
The bank has a policy in place on how to rate credit risk identification in terms of satisfaction 15 33 26 37 44 155 3.40 1.351
Average Score 9 17 34 47 48 155 3.68 1.186

Source: Field Data (2023)

The study aimed to ascertain participants’ opinions on credit risk identification about the financial performance of commercial banks in South Sudan. Descriptive findings were as indicated in Table 3. The independent variable had seven (7) items. The averages and standard deviations of the respondents’ replies were calculated using a five-point Likert Scale of Strongly Disagree (SD = 1), Disagree (D = 2), Neutral (N = 3), Agree (A = 4), and Strongly Agreed (SA = 5). The question of whether the bank has an effective system put in place to improve credit risk identification when customers are taking loans had an average score of 3.62, shows a moderate level of agreement among respondents regarding the effectiveness of the system, and a relatively higher standard deviation of 1.218 indicates that some commercial banks might not have robust measures in place to identify credits risks that come with lending to their borrowers. With a mean that rounds off to agree, it means that the majority of the commercial banks have put in place effective systems to identify risks associated with lending to the borrowers. The question of whether intense competitive pressure on loan borrowing, which may hinder obtaining accurate information on borrowers’ creditworthiness had an average score of 2.43 implies a moderate level of agreement among respondents. The standard deviation of 1.051 indicates a relatively low dispersion of responses, suggesting a consensus among the participants that banks are constantly exploring new markets in pursuit of expanding their operations. Entering new markets may tempt management to make loans without conducting a thorough financial analysis of the borrowers had an average score of 3.79 indicates a moderate level of agreement among respondents regarding the temptation for management to make loans without conducting a thorough financial analysis when entering new markets, and the standard deviation of 1.067 suggests a more diverse range of opinions among the participants. The findings agreed with those of (Karanja, 2019), on strategies adopted by commercial banks on credit risk identification established that there were new entry strategies which were implemented without proper market scanning and these have exposed commercial banks to risk of lending to a clique of borrowers who may not be in need of credit. When the respondents wereasked whether there are formulated internal guidelines apart from those stipulated by the Central Bank of South Sudan on methodology to access borrowers when making loan decisionsan average score was 3.81, shows that most of the respondents nearly agree that their other policies and regulations developed by banks to regulate how borrowers access loans. The standard deviation of 1.167 indicates a relatively moderate dispersion of responses, implying some level of acceptable consensus among the participants. Regarding faster lending decisions based on the features of borrowers and the anticipated value of collateral have exposed banks to credit risk had an average score of 3.71 suggests a moderate level of agreement among respondents, and a standard deviation of 1.128 indicates a moderate dispersion of responses, implying some consensus among the participants. The question of whether the banks have techniques for analyzing additional financial resources that the borrower can use to pay off the credit had an average score of 3.81 suggests the respondents moderately agree that the commercial banks have well-formulated ways for analyzing the financial resources a borrower can use to offset their credit. The standard deviation of 1.127 indicates a relatively low dispersion of responses, implying some level of consensus among the participants. Furthermore, respondents were asked if the bank has a policy in place on how to rate credit risk identification in terms of satisfaction had an average score of 3.40 suggests a moderate level of agreement among respondents, indicating that many of the respondents might not be aware of any policies the banks have in place for rating credit risk identification, particularly regarding satisfaction. The higher standard deviation of 1.351 indicates a relatively higher dispersion of responses, implying a greater diversity of opinions among the participants. This could mean that some banks have policies for rating credit risk identification while others don’t or the existing policies are not as robust or effective.

Linear Regression between credit risk identification and financial performance of commercial banks in South Sudan

To analyze the effect of credit risk identification on the financial performance of commercial banks in South Sudan, the study sought to test for the following hypothesis;

H0; There is no effect of credit risk identification on the financial performance of commercial banks in South Sudan. The results are shown in Table 4;

Simple Linear Regression Analysis between Credit Risk Identification and Financial Performance of Commercial Banks in South Sudan

This study performed simple linear regression to examine the linear relationship between credit risk identification and the financial performance of commercial banks in South Sudan. The results of the study were indicated in the table below.

Table 4: Simple Linear Regression

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics
R Square Change F Change df1 df2 Sig. F Change
1 .130a .017 .011 2.67525 .017 2.638 1 153 .016
a. Predictors: (Constant), B0
b. Dependent Variable: F0

 

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 18.878 1 18.878 2.638 .016b
Residual 1095.018 153 7.157
Total 1113.897 154
a. Dependent Variable: F0
b. Predictors: (Constant), B0

 

Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 25.620 1.133 22.618 .000
B0 .073 .045 .130 1.624 .016 1.000 1.000
a. Dependent Variable: F0

Credit risk identification and the financial performance of commercial banks in South Sudan. The model summary indicates that the linear regression model has an R-squared value of 0.017, suggesting that approximately 1.7% of the variability in the financial performance of commercial banks can be explained by the credit risk identification variable. The adjusted R-squared value of 0.011 suggests that the model accounts for a very small portion of the variance while considering the number of predictors.

The ANOVA table further supports the findings of the regression analysis. The regression analysis yielded a significant F-statistic of 2.638, indicating that the overall model is statistically significant at the 0.05 level. This implies that the credit risk identification variable has a notable impact on the financial performance of commercial banks in South Sudan. Examining the coefficients, the constant term (intercept) is 25.620 with a standard error of 1.133. This indicates that when the credit risk identification variable is zero, the financial performance of commercial banks in South Sudan is estimated to be 25.620. The credit risk identification coefficient is 0.073 with a standard error of 0.045. This suggests that for each unit increase in credit risk identification, the financial performance of commercial banks in South Sudan is expected to increase by 0.073 units. The significance level of 0.016 indicates that this relationship is statistically significant at the 0.01 level.

Financial Performance = 25.620 + 0.073 credit risk identification

This model means that a unit increase in credit risk identification will result in 0.073 increases in the financial performance of commercial banks in Southern Sudan. However, this is only true when holding all other factors that influence the financial performance of banks and other similar financial institutions in the country constant.

SUMMARY, CONCLUSION, AND RECOMMENDATIONS

The purpose of the study was to determine the effect of credit risk identification on the financial performance of commercial banks in South Sudan. The results of the study indicated that credit risk identification has a positive effect on the financial performance of commercial banks in South Sudan. The analysis yielded a marginal influence, which indicates an improvement in credit risk identification would result in an improvement in the financial success of commercial banks, with all other factors influencing financial performance kept constant. The significance analysis revealed that the effect of credit risk identification on financial success was statistically significant, and implied that the alternate hypothesis was adopted. The majority of participants believed that credit risk identification had a positive effect on financial performance. It was discovered that there was an ongoing credit review with accurate credit grading, suitable amount, and reporting to management, which enabled commercial banks to monitor credit risk and fix credit issues in a timely and appropriate manner. The study showed that the dire to enter a new environment has lured banks to lend without sufficient financial and economic analysis. Further, the study showed that there was subjective decision-making by the banks especially when the borrower appears to have met the credit approval criteria. However, the study revealed that there is an issue with credit risk identification which results from competitive pressure and credit growth since they tend to make a time constraint on obtaining reliable data.

These practices can be concluded to have contributed more in exposing banks to credit risk, hence affecting financial performance. The study also concludes that there is more credit defaulting, and unregular loan repayments and this result is a decline in lending across the entire commercial banks sector.

RECOMMENDATIONS AND POLICY IMPLICATIONS

The findings of the study showed a positive significant effect of credit risk identification on the financial performance of commercial banks in South Sudan. Therefore, the managementof the commercial bank should ensure the use of the right credit risk identification tools and also ensure that bank officials supervise and monitor the competitive pressure and credit growth as they tend to put a time constraint on getting accurate data of borrowers. CBoSS should make it a requirement that commercial banks measure competitive pressure and credit growthin terms ofevaluating potential risks associated with new loan services or products,

AUTHOR CONTRIBUTIONS

Simon Songa Peter sought study authorization from the relevant government institutions like the Graduate School of Kibabii University and the National Ministry of Higher, Science, and Technology (NMHST). He developed the study methodology that comprised of study instruments that were employed in the data collection. The researcher further analyzed, interpreted, and discussed the data. He conducted a literature review that included background information on the research concepts as well as the theoretical context. He trained and supervised the research assistants and oversaw the collection of primary data. The researcher also coded the collected questionnaires before entering and analyzing the data using SPSS software. Dr. Rashid Fwamba,, Dr. Brian Singoro and Dr. Fred Gichana Atandi ensured that the published article conformed to the journals’ formatting guidelines.

ACKNOWLEDGMENT

I would like to extend my sincere appreciation to my supervisors, Dr. Brian Singoro and Dr. Fred Gichana Atandi, for their invaluable guidance and support throughout this study. I express gratitude for the assistance provided by my family and friends during my academic pursuits. Furthermore, I express my gratitude to the Kibabii University community for their invaluable support in terms of spiritual and ethical guidance.

CONFLICT OF INTEREST

The authors affirm that there are no conflicts of interest about the publishing of this Manuscript. Furthermore, the authors have diligently adhered to many ethical considerations, such as plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publishing and/or submission, and redundancy.

REFERENCES

  1. Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.
  2. Aldoseri, M., & Worthington, A. C. (2022). Credit Risk Management and Practices in Islamic and Conventional Banks: An Emerging Market Perspective. In In Handbook of Banking and Finance in Emerging Markets (pp. 366-377). Edward Elgar Publishing.
  3. Auronen, L. (2003). Asymmetric Information: Theory and Applications. Seminar of Strategy and International Business at Helsinki University of Technology. Helsinki.
  4. Bessis, J. (2015). Risk Management in Banking, Wiley, Third Edition.
  5. Binks, M. R., & Ennew, C. T. (1997). Smaller Businesses and Relationship Banking: The Impact of Participative Behavior. Entrepreneurship Theory and Practice, 21(4), 83–92. https://doi.org/10.1177/104225879702100406
  6. Bülbül, D., Hakenes, H., & Lambert, C. (2019). What influences banks’ choice of credit risk management practices? Theory and evidence. Journal of Financial Stability, 40, 1–14. https://doi.org/10.1016/j.jfs.2018.11.002
  7. Chatterjee, S., Corbae, D., Dempsey, K., & Rios-Rull, J.-V. (2020). A Quantitative Theory of the Credit Score: National Bureau of Economic Research.
  8. Costello, A. B., & Osborne, J. W. (2005). Best practices in expolratory factor analysis: Four recommendations for getting the most from your analysis.10.
  9. Edwin, K., & Omagwa, J. (2018). Credit Management Practices and Financial Performance of Microfinance Institutions in Nairobi Central Business District, Kenya. International Journal of Scientific and Education Research, 2(04). http://ijsernet.org/www.ijsernet.org
  10. Ekumah, E. K., & Essel, T. (2003). Information is Power: The Problem with Credit Accessibility. In Cape Town: University of Capetown.
  11. Esfahani, H. N., Sobhiyah, M. hossein, & Yousefi, V. R. (2016). Project Portfolio Selection via Harmony Search Algorithm and Modern Portfolio Theory. Procedia – Social and Behavioral Sciences, 226(October 2015), 51–58. https://doi.org/10.1016/j.sbspro.2016.06.161
  12. Francis, A., Tamwesigire, C., & Eton, M. (2022). Credit Risk Management Practices and Loan Performance of Commercial Banks in Uganda. Business Perspective Review, 4(1), 16–28. https://doi.org/10.38157/bpr.v4i1.394
  13. James, L. (1983). Corporation Finance: Risk and Investment. London: National Bureau of Economic Research, Harvard University. In National Bureau of Economic Research.
  14. Jefferis, K., & Haas, A. (2017). Moving from a Fixed to a Floating Exchange RateS : The Case of the South Sudanese Pound. International Growth Centre, 2015–2018.
  15. Karanja, G. J. (2019). Credit Risk and Lending Performance of Commercial Banks in Kenya. August, 1–259.
  16. Karanja, J., Bichanga, J., & Kingoriah, G. K. (2018). Influence of Credit Risk Monitoring on Lending Performance of Commercial Banks in Nairobi County, Kenya. International Journal of Business Management and Economic Review, 1(05), 171–189. https://doi.org/10.2139/ssrn.3235582
  17. Karanja, S. G., & Simiyu, E. M. (2022). Credit Management Practices and Loan Performance of Microfinance Banks in Kenya. Journal of Finance and Accounting, 6(1), 108–139. https://doi.org/10.53819/81018102t6009
  18. Kipyego, K. D., & Wandera, M. (2013). Effects of Credit Information Sharing on Nonperforming Loan : The Case of Kenya Commercial Bank Kenya. European Scientific Journal, 9(13), 168–193.
  19. Lapavitsas, C. (1991). The Theory of Credit Money: A Structural Analysis. Science & Society. Marxist Monetary Theory, 55(3), 291–322. https://doi.org/10.1163/9789004272712_003
  20. Lee, H., Cheng, F., & Chong, S. (2016). Markowitz Portfolio Theory and Capital Asset Pricing Model for Kuala Lumpur Stock Exchange. International Journal of Economics and Financial Issues, 6(3S), 59–65. http://econjournals.com/index.php/ijefi/article/view/2607/pdf
  21. Lukomnik, J., & Hawley, J. P. (2021). Examining the limits of modern portfolio theory. https://www.top1000funds.com/2021/05/examining-thelimits-%0Aof-modern-portfolio-theory/
  22. Macleod, H. D. (1889). The Theory of Credit. Scotland.
  23. Maier-Paape, S., & Zhu, Q. J. (2018). A General Framework for Portfolio Theory Part I: Theory and Various Models. Risks, 6(2), 1–35. https://doi.org/10.3390/risks6020053
  24. Markowitz, H. (1991). Efficient Diversification of Investments in Selection. Cambridge, MA: Basil Blackwell. http://garfield.library.upenn.edu/classics1992/A1992
  25. Markowitz, H. M. (1927). Modern Portfolio Theory and Investment Analysis. In New York; Amazon Inc. Amazon Inc.
  26. Mbiti, J., Lugogo, F., & Koech, R. (2018). Contribution of Credit Risk Management Strategies on Financial Stability: A Case of Commercial Banks in Kilifi County-Kenya. European Journal of Business and Management Www.Iiste.Org ISSN, 10(17), 1–12. Www.Iiste.org
  27. Merton, R. C. (1974). On the Pricing of Corporate Debt : The Risk Structure of Interest Rates. The Journal of Finance, 29(2), 449–470.
  28. Misiko, J. W., Rashid, F., & Arnety, N. (2022). Credit Terms as a Credit Management Practice And Financial Performance of Government Youth Funded Enterprises in Webuyeeast Sub-County, Bungoma County, Kenya. Journal of Economics and Finance (IOSR-JEF), 13(4), 30–39. https://doi.org/10.9790/5933-1304023039
  29. Mogga, J. P., Mwambia, F., & Kithinji, M. M. (2018). Effect of Credit Risk Management on the Financial Performance of Commercial Banks in Juba City, South Sudan. International Academic Journal of Economics and Finance, 3(2), 93–116.
  30. Olobo, M., Karyeija, G., Sande, P., & Khoch, S. (2021). Credit Risk Management Practices and Performance of Commercial Banks in South Sudan. Journal of Financial Risk Management, 10(03), 306–316. https://doi.org/10.4236/jfrm.2021.103018
  31. Onuko, L. K., Muganda, M., & Musiega, D. (2015). Effect of Credit Risk Management on Loan Portfolio Quality of Tier One Commercial Banks in Kenya. International Journal of Business and Management Invention ISSN, 4(7), 46–53. www.ijbmi.org46%7C
  32. Paul, S. M., & Maniagi, M. (2020). Effects of Credit Risk Management Practices on Financial Performance of Microfinance Institutions in Nairobi. Journals in Business & Management, 1(2), 29–34.
  33. Saunders, M., Lewis, P., & Thornhill, A. (2018). Research Methods for Business Students. Eighth Edition. In Synthese (Vol. 195, Issue 5, p. 833).
  34. Shieler, B., Emenike, K. O., & Amu, C. U. (2017). Credit Risk Management and Financial Performance of Microfinance Institutions in Kampala, Uganda. Journal of Banking and Financial Dynamics, 1(1), 29–35. https://doi.org/10.20448/journal.525.2017.11.29.35
  35. Shikalepo, E. E. (2020). Shikalepo. Academia Accelerating the World’s Research, 1(2), 1–8.
  36. Thakor, A. V. (2016). The Highs and the Lows: A Theory of Credit Risk Assessment and Pricing through the Business Cycle. Journal of Financial Intermediation, 25, 1–29. https://doi.org/10.1016/j.jfi.2015.06.003
  37. Thakur, M., & Vaidya, D. (2020). Modern Portfolio Theory (MPT) Definition, Assumptions, Examples. https://www.wallstreetmojo.com/modern-portfolio-theory/
  38. Wakaria, S. (2016). Effects of Credit Information Sharing on Nonperforming Loan : The Case of Kenya Commercial Bank Kenya.
  39. Westfall, P., & Kvilhaug, S. (2021). Modern Portfolio Theory (MPT). Invest opedia.Com. https://www.investopedia.com/terms/m/modern portfolio theory.asp
  40. William, S. F. (1934). Portfolio Theory and Capital Markets. In New Jersey: McGraw-Hill.https://doi.org/10.2307/2978700
  41. Wong, M. C. Y. (2013). Bubble Value at Risk: A Counter cyclical Risk Management Approach. Singapore: In John Wiley & Sons.

Article Statistics

Track views and downloads to measure the impact and reach of your article.

3

PDF Downloads

[views]

Metrics

PlumX

Altmetrics

Paper Submission Deadline

Subscribe to Our Newsletter

Sign up for our newsletter, to get updates regarding the Call for Paper, Papers & Research.

    Subscribe to Our Newsletter

    Sign up for our newsletter, to get updates regarding the Call for Paper, Papers & Research.