Credit Risk Management – Research Ideas

Author Ritika Sharma (MBA in Finance)

Credit Risk Management

Introduction

Banks provide financial services to channel funds from depositors to investors for profit. Commercial banks are vital to a nation’s economy (Brahmana et al., 2018; Prakash et al., 2017). Commercial banks serve as financial intermediaries to redirect funds from the surplus sector to the deficit sector profitably and sustainably. Financial stability is vital for any nation, so financial institutions should be well managed. The velocity of loan creation in an economy significantly influences the productive activities of a nation. Interest on loans and advances is a commercial bank’s primary income source (Ahmed et al., 2018).

The primary cause of banks’ financial problems is directly related to credit standards for borrowers (Tian, 2021; Lam et al., 2018). The primary objective of credit risk management is to reduce risk impact on business organisations, including commercial banks (Bouteille & Coogan-Pushner, 2021; Levy & Zhang, 2019). Loans account for commercial banks’ credit risk exposure, as they usually account for a substantial part of their equities and financial liabilities (Kauko, 2012; Muye & Muye, 2017). Commercial banks must have an effective credit risk management system (Kimondo et al., 2012). What motivates this study is the essential role of commercial banks in mobilising financial resources for investment by providing credit facilities (including loans) to businesses and investors. Interest on loans and advances are commercial banks’ primary income sources. Banks are susceptible to various risks by providing credit facilities, including liquidity and credit risks (Bolarinwa et al., 2019; Kargi, 2011).

There are several studies on the impacts of credit risk management on the financial performance of commercial or deposit money banks in Nigeria, but their findings vary. The findings of some of the studies indicated that credit risk management positively impacts the financial performance of deposit money banks in Nigeria (Nwude & Okeke, 2018; Alalade et al., 2015). Echobu and Okika (2019) study’s revealed that non-performing loans and impairment loan charge-offs negatively impact the financial performance of banks.

This study examined the impacts of credit risk management on the financial performance of commercial banks, using five (5) firsttier banks in Nigeria as a case study. The banks selected for the study include Access Bank, Guaranty Trust Bank (GTB), First Bank, Zenith Bank, and United Bank for Africa (UBA). The research objectives are to examine the relationship between non-performing loans and the financial performance of Nigerian commercial banks, and establish the relationship between expected credit loss impairment provisions and the financial performance of Nigerian commercial banks.

The credit derivatives market is rapidly developing. Taking credit derivatives of all types, about $5,000 billion in debt will have been protected against default during 2004. Whilst the range of available instruments and their application continues to increase, the market still lacks, at the time of writing, the transparency and liquidity of more traditional, exchange-traded instruments. However, a number of institutional attempts to standardise the market, for example, by the International Swaps and Derivatives Association (ISDA) and the emergence of active credit derivative indices such as iBoxx, conceived by a consortium of banks, and Trac-X, launched by Morgan Stanley and JP Morgan (now managed by Dow Jones), are beginning to fashion the credit derivatives market into a more generally useful form. No attempt is made here to consider all of the available instruments and applications of credit derivatives for the corporate user. Rather, we illustrate the potential usefulness of these instruments by focusing in on two particular kinds of instrument: the credit default swap and the total return swap.

Principal credit derivative products

The range of credit products available is limited largely by the perceived need for them and the creative imaginations of the financial engineers putting the products together. Since they are traded over-the-counter, credit derivatives can be tailored to suit the particular needs of the purchaser. However, under the tutelage of the institutional market for such instruments, the following four main types of contract have emerged in the drift towards standardisation[4] ([3] CFOEurope.com, 1998a):

(1)] Credit default swaps (CDSs)

(2)] Total return swaps (TRSs)[5] (3)] Credit spread options (4)] Credit-linked notes

Risk Transformers

To avert a failed credit life cycle, we have what we term “risk transformers”, which are various measures and strategies open to banks to mitigate the probable manifestation of risk transmitters’ contagion. We shall now examine some salient risk transformers.

The ubiquitous evidence of well-aligned risk transformers is a good financial performance which is traceable to improvement in a lender’s quality of risk assets, otherwise known as “credit quality”. Credit quality is the ratio of non-performing loans to total loans and advances (NPLR). A reduction of this ratio, for instance, from ten percent (10%) to five percent (5%) will mean a fifty percent (50%) deflation in non-performing loans and indicates that the bank’s quality of risk assets is at ninety-five percent (95%) level, which, of course, will cause improvement in the revenue of the bank and enhancement of its financial performance. Interestingly, Bikker (1999) and Kosmidou (2008) opine that banks’ performance can be measured in different ways, which include competition, concentration, efficiency, productivity, and profitability.

Commercial Banks

Commercial banks take deposits and lend for consumption and investment purposes (Echobu & Okika, 2019; Elshaday et al., 2018). Commercial banks are also Deposit Money Banks (DMBs) (Agbamuche et al., 2022; Ajao & Oseyomon, 2019; Apochi & Baffa, 2022; Njoku et al., 2017). Commercial banks’ lending activity gives rise to income, but they can incur losses due to non-payment of loans by borrowers (Kumar & Kishore, 2019; Suganya & Kengatharan, 2018). Commercial banks generate income through interest paid on loans by borrowers. However, commercial banks’ borrowers defaulting (default risk) in repaying their loans affect their performance (Bouteille & Coogan-Pushner, 2021; Witzany, 2017). Default risk arises when borrowers default and fail to meet their obligations. Default risk may result from a poor assessment of the borrowers’ creditworthiness and non-compliance with sound lending principles (Levy & Zhang, 2019; Tian, 2021)

Return on Asset (ROA)

Return on Assets (ROA) is a financial performance indicator. ROA is the measure of efficiency that determines how well the banks use their scarce resources to generate profits (Kiptoo et al., 2021; Muye & Muye, 2017; Kauko, 2012). ROA is widely used to compare a company’s efficiency and operational performance as it looks at the returns generated from the assets financed by the company (Tian, 2021; Lam et al., 2018). It is the ratio of net income to the total asset. A higher ratio is an indication of better financial performance.

The most common measure of bank financial performance is profitability. Profitability is measured by using Return on Assets (ROA), Return on Equity (ROE) and Cost of Income Ratio (Nwosu et al., 2020; Nwude & Okeke, 2018). The study uses the Return on Assets (ROA) as the dependent variable (Nwosu et al., 2018). Return on Assets (ROA) was computed as the net profit (income) divided by the total assets. ROA measures the ability of management to acquire deposits at a reasonable cost and invest them in profitable investments (Hosna et al., 2019; John & Okika, 2019). However, banks are expected to bear some bad loans and losses in their lending activities. The bank’s objective is to minimise such losses to enhance its profitability.

Expected Credit Loss Provision (ECL)

Expected Credit Loss Provision (ECL) is a non-cash expense for banks to account for future losses on loan defaults (Tian, 2021; Lam et al., 2018). ECL was used as a credit risk management proxy in this study because commercial banks operate on the assumption that a certain percentage of loans will default or become slow paying. As a result, banks make provision for a percentage as an expense when calculating their pre-tax incomes. This guarantees a bank’s solvency and capitalization if a default occurs. The loan loss provision allocated each year increases with the riskiness of the loans a given bank makes (Fakhrunnas & Imron, 2019; Nelly et al., 2019; Njoku et al., 2017). A bank making a small number of risky loans will have a low loan loss provision compared to a bank taking higher risks (Malik & Shafie, 2021; Mudanya & Muturi, 2018). Banks’ loan loss provision is paramount in affecting their profitability.

Credit risk management involves identifying potential risks, estimating their consequences and impacts, monitoring activities exposed to the identified risks, and implementing control measures to prevent or reduce undesirable effects (Bouteille & CooganPushner, 2021; Levy & Zhang, 2019). This process applies to the bank’s policies, strategies and operational framework. Non-payment

Credit Risk Management

Sound credit risk management is essential to optimising commercial banks’ performance (Siriba, 2020; Molla, 2018; Witzany, 2017). Loans are banks’ prime and most apparent source of credit risk. However, other sources of credit risk exist in commercial banks’ activities. Hence, banks’ management must set up a credit supervision team to ensure that credit is properly maintained and administered. Effective credit risk management involves establishing a suitable environment, ensuring a sound credit granting process, and maintaining an appropriate credit administration to monitor the process and minimise credit risk exposures (Akomeah et al., 2017; Almekhlafi et al., 2016). Hence, the management of commercial banks needs to ensure the adoption and implementation of a sound risk management framework. The borrowers’ credit capability can be assessed using qualitative or quantitative techniques. Borrowers’ characteristics using quantitative and qualitative models by assigning numbers with the sum of the values matched up to a threshold (Werner, 2016; Echekoba et al., 2014). This method is called “credit scoring” (Tian, 2021; Levy & Zhang, 2019). Sound rating systems will minimise commercial banks’ credit risk through borrowing. Counterparty failure to fulfil borrowing commitments is a significant source of credit risks for commercial banks (Afolabi, 2021; Kinyua, 2017).

Non-Performing Loans (NPLs)

Non-payment of loans by lenders, also known as non-performing loans, increases the credit risk of commercial banks (Poyraz & Ekinci, 2019; Hamza, 2017; Otieno et al., 2016). The recovery process for non-performing loans in Nigeria is challenging. Hence, the Banks and Other Financial Institutions Act (BOFIA) 2020 introduces a credit tribunal to improve the financial system’s lending landscape and loan recovery activities in Nigeria. A non-performing loan is a loan in which the maturity date has passed, but at least part of the loan is still outstanding (Ari et al., 2019). The specific definition is dependent upon the loan’s particular terms. Sound and sustainable profitability are essential in maintaining the banking system’s stability. If solvency is high, low profitability weakens the capacity of a bank to absorb adverse shocks and improve solvency. Hence, the need for commercial banks to reduce their credit risk, including non-performing loans. Non-performing loans measure the positive and fitness of a bank’s credit risk management (Tian, 2021; Lam et al., 2018).

Financial Intermediation Theory

The financial intermediation theory of banking, propounded by Mises (1912), asserts that other people’s lending characterises the banks’ activity as negotiators of credit and loan givers. Banks fuel business activities by creating liquidity by borrowing from depositors with short maturities and lending to borrowers with longer maturities (Tian, 2021; Levy & Zhang, 2019). Banks profit by accepting customer deposits and lending the funds at a higher interest rate (Krugman, 2015; Werner, 2016). Commercial banks are increasingly prone to huge credit risks through their operations, including foreign exchange transactions, interbank transactions, bonds, trade financing, equities, and swaps (Siddique et al., 2022; Afolabi, 2021; Olson & Zoubi, 2017).

Hypothesis Development

In view of the literature review, the following hypothesis was developed for this study:

Ho: Credit risk management does not impact the performance of commercial banks.

Hi: Credit risk management impacts the performance of commercial banks.

Research and Methodology

This study uses a quasi-experimental research design approach. Fifteen (15) years of panel data (2005 to 2019), extracted from the audited financial reports of five first-tier listed banks, was used for the study. Five (5) first-tier banks were selected for this study, using a purposive sampling technique, including Access Bank, Guaranty Trust

Bank, First Bank, Zenith Bank, and United Bank for Africa. All the banks used for this study are Deposit Money Banks (DMBs) listed on the Nigerian Stock Exchange. The population of this study consists of the nineteen listed DMBs in Nigeria’s banking sector as of December 2020. The study employs a multiple regression analysis model based on the hypothesised functional relationship between credit risk management and financial performance. Non-performing loans (NPL), expected credit loss impairment provisions (ECL), and Return on Assets (ROA) are the variables used for the study. ROA (Return on assets) is the dependent variable, while non-performing loans (NPL) and expected credit loss impairment provisions (ECL) are independent variables. The model was estimated using regression techniques (fixed effects, random effects or pooled ordinary least squares (OLS). The model used to test the research hypothesis is stated below: ROA = β0+ β1 NPL + β2 ECL + ε

Where:

β0, β1, and β2 and are the regression constants,

Non-performing loans (NPL) indicates how banks manage their credit risk,

Expected Credit Loss (ECL) is the probability-weighted credit estimate, and

ε is purely a white noise phenomenon assumed to capture the influence of other exogenous factors capable of influencing the dependent variable.

The model was estimated using the Ordinary Least Squares (OLS) regression technique. Ordinary Least Squares regression (OLS) is a technique for estimating coefficients of linear regression equations which explore the relationship between independent and dependent variables.

Non-Performing Loans (NPLs)

Analysis and Findings

The data collected for this study is presented in Appendix 1. The data was analysed using descriptive statistics, unit root test, cointegration test and regression analysis. The descriptive data statistics are presented in

Table 1.

Source: Researchers’ Computation using Eview 9.0

Table 1 shows the descriptive analysis of the Return on Assets (ROA), Non-performing loans (NPL), and Expected Credit Loss impairment provisions (ECL). The Mean is the average value of the series, obtained by dividing the total value by the number of observations. Table 1 shows that the Mean value of ROA is 2.3%, NPL is 5.1%, and ECL is -122.5%. The median is the middle value of the series when the values are arranged in ascending order. Table 1 shows that the Median ROA is 2.1%, NPL is 3.7%, and ECL is -20.3%. The maximum and minimum values of the data series used for this study are maximum and minimum. The maximum and minimum values for ROA are 0.06 and -0.01, NPL is 0.25 and 0.012, and ECL is 0.47 and -40.8. The standard deviation is a measure of spread or dispersion in the series. Table 1 also shows that the standard deviation for ROA is .01, NPL is .04, and ECL is 5.16.

Skewness is a measure of the asymmetry of the distribution of the series around its Mean. Positive skewness implies that the distribution has a long right tail, and negative skewness implies that the distribution has a long left tail. The skewness of a normal distribution is zero. The data are relatively symmetrical if the skewness is between -0.5 and 0.5. The data are moderately skewed if the skewness is between -1 and -0.5 or 0.5 and 1. The data are highly skewed if the skewness is less than -1 or greater than 1. The results also indicate that the skewness of ROA (0.62) and ECL (-6.71) is moderately skewed as they are less than 1, but NPL is highly skewed at 2.59 (Table 1).

Kurtosis is a measure of the combined sizes of the two tails. It measures the amount of probability in the tails. The value is often compared to the kurtosis of the normal distribution, which is equal to 3-Mesokurtic. If the kurtosis is greater than 3, the dataset has heavier tails than a normal distribution (more in the tails-Leptokurtic). If the kurtosis is less than 3, the dataset has lighter tails than a normal distribution (less in the tails-Platykurtic). The kurtosis shows that ROA (3.79), NPL (10.9) and ECL (49.9) have a leptokurtic distribution (Kurtosis > 3). In addition, ROA and NPL are positively skewed, while ECL is negatively skewed. The positive skewness means that the degree of departure from the distribution average is positive, which reveals a consistent increase from 2005 – 2019, while the negative skewness indicates a consistent decrease. Table 2 also shows that ROA (0.04), NPL (0.00) and ECL (0.00) have a low probability, indicating that the variables are not normally distributed. This is also evident from the probability of Jarque-Bera statistics. Table 2 shows the summary of the unit root test.

Source: Researchers’ Computation using Eview 9.0

This study adopted Levin, Lin & Chu t*, ADF – Fisher Chi-square and PP – Fisher Chi-square techniques to test and verify the series’ unit root property and the model’s stationarity. The stationary test was conducted to avoid spurious regression problems usually associated with time series econometric modelling. This is necessary to establish whether the time series data is stationary and, if not, to establish the order of integration and check whether the variables are integrated in the same order. The basic idea behind cointegration is that if two or more series move closely together in the long run, even if they are trended, the difference between them is constant. All variables are examined and found stationary at their first difference. Table 2 shows that ROA, NPL and ECL are stationary in their first difference form, integrated at order one (1). At this order of integration, their p-value is less than 0.05. Hence, the co-integration of all the variables is the same in their conclusion and integrated in the same order

Discussion of Findings

This study examined the impact of credit risk management and the financial performance of commercial banks, using five first-tier banks in Nigeria as a case study.

The findings of the study suggest that:

Non-performing loans (NPL) have a negative (β1 = −0.0679788) and significant (p < 0.05) effect on Return on Asset (ROA). This indicates that the ROA is expected to decrease by 0.0679788 units for one unit increase in NPL while keeping all other variables constant. The result also indicated that, although NPL impact negatively on Nigerian banks’ financial performance, it is a significant determinant of the ROA in the Nigerian banking sector. This is consistent with previous studies’ findings that indicate that non-performing loans negatively affect banks’ liquidity and profitability (Agbamuche et al., 2022; Ajao & Oseyomon, 2019; Echobu & Okika, 2019; Serwadda, 2018; Li & Zou, 2014).
Expected Credit Loss impairment provisions (ECL) have a positive (β2 = 0.000593072) and significant (p < 0.05) effect on Return on Asset (ROA). This indicates that for one unit increase in ECL, the ROA is expected to increase by 0.000593072 units while keeping all other variables constant. The result also indicated that the ECL is a significant determinant of ROA in the Nigerian banking sector. The finding of this study is contrary to previous studies, which concluded that loan loss provisions and capital adequacy had a negative impact on the profitability of commercial banks (Serwadda, 2018; Nwude & Okeke, 2018; Alshatti, 2015; Gizaw et al., 2015; Olawale, 2014).

Conclusion

This study examined credit risk management and the financial performance of DMBs in Nigeria for a twelve (12)-year period (2010–2021) without the implementation of uniform financial reporting stage by the Central Bank of Nigeria. The selected banks for our study are the eight (8) Nigerian petrifaction money banks with international financial licenses due to their perceived trueness to international weightier practices and comparable financial reports. This study examined the impact of credit risk management and the financial performance of commercial banks, using five first-tier banks in Nigeria as a specimen study. The findings of this study indicate that credit risk management does not positively stupefy the financial performance of commercial banks in Nigeria. Commercial banks, moreover known as Deposit Money Banks (DMBs), take deposits and lend for consumption and investment purposes. Commercial banks’ lending worriedness gives rise to income, but they can incur losses due to non-payment of loans by borrowers. Commercial banks generate income through interest paid on loans by borrowers. However, commercial banks’ borrowers defaulting (default risk) in repaying their loans stupefy their financial performance. Default risk arises when borrowers default and goof to meet their obligations. Sound credit risk management and good corporate governance will reduce credit risk. Commercial banks must maintain a minimal level of ECL based on regulatory requirements to protect their depositors’ investments, thus promoting the financial system’s stability. It is, therefore, necessary for commercial banks to effectively control and monitor their non-performing loans (NPL)

Acknowledgments

All sources cited in this paper are duly acknowledged and referenced. All authors have read and agreed to the published version of the manuscript. Author Contributions: Introduction, O.S.F.; Literature review, O.S.F.; Methodology, O.S.F. and P.S.; Data analysis, O.S.F.; Discussion of findings, O.S.F. and P.S.; Conclusion and recommendations, O.S.F. and P.S. Funding: This research was funded by the Researchers Informed Consent Statement: “Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions. Conflicts of Interest: The authors declare no conflict of interest.

References

Afolabi, T. S., Obamuyi, T. M. & Egbetunde, T. (2020). Credit risk and financial performance: evidence from microfinance banks in Nigeria. IOSR Journal of Economics and Finance, 11(1), 8–15.

Bhatt, T. K., Ahmed, N., Muhammad, B. I., & Ullah, M. (2023). Examining the Determinants of Credit Risk Management and Their Relationship with the Performance of Commercial Banks in Nepal. Journal of Risk and Financial Management, 16(4), 235. https://doi.org/10.3390/jrfm16040235

Gao, L., & Xiao, J. (2021). Big Data Credit Report in Credit Risk Management of Consumer Finance. Wireless Communications & Mobile Computing (Online), 2021https://doi.org/10.1155/2021/4811086

Gao, Lu, and Jian Xiao. 2021. “Big Data Credit Report in Credit Risk Management of Consumer Finance.” Wireless Communications & Mobile Computing (Online) 2021. doi: https://doi.org/10.1155/2021/4811086.

Natufe, Oritsegbubemi K., and Esther Evbayiro-Osagie. 2023. “Credit Risk Management and the Financial Performance of Deposit Money Banks: Some New Evidence.” Journal of Risk and Financial Management 16(7):302. doi: https://doi.org/10.3390/jrfm16070302.

Olajide SF. Does credit risk management impact the financial performance of commercial banks? International Journal of Business Ecosystem & Strategy. 2023;5(2):55-66. https://www.proquest.com/scholarly-journals/does-credit-risk-management-impact-financial/docview/2824490720/se-2. doi: https://doi.org/10.36096/ijbes.v5i2.415.

Olajide, S. F. (2023). Does credit risk management impact the financial performance of commercial banks? International Journal of Business Ecosystem & Strategy, 5(2), 55-66. doi:https://doi.org/10.36096/ijbes.v5i2.415

Olajide, Solomon F. 2023. “Does Credit Risk Management Impact the Financial Performance of Commercial Banks?” International Journal of Business Ecosystem & Strategy 5(2):55-66 (https://www.proquest.com/scholarly-journals/does-credit-risk-management-impact-financial/docview/2824490720/se-2). doi: https://doi.org/10.36096/ijbes.v5i2.415.

Olajide, Solomon Fadun. 2023. Does credit risk management impact the financial performance of commercial banks? International Journal of Business Ecosystem & Strategy 5, (2): 55-66, https://www.proquest.com/scholarly-journals/does-credit-risk-management-impact-financial/docview/2824490720/se-2 (accessed October 20, 2023).

Triki, O., & Abid, F. (2022). Contingent convertible lease modeling and credit risk management. Financial Innovation, 8(1)https://doi.org/10.1186/s40854-022-00393-y

Leave a comment Cancel reply