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This study examines the impacts of credit risk management on the growth of Nigerian banks. Specifically, the study focused mainly on the impacts of credit risk management among Deposit Money Banks in Nigeria. The study employed an ex-post facto research design, utilizing secondary data from the financial information of 14 selected listed Deposit Money Banks from 2007 to 2022. The study population was defined as the total number of listed Deposit Money Banks, from which a sample of six banks was selected using a simple random sampling technique. The operationalization of variables included (1) the use of natural logarithm for bank size, (2) a three-level scale for credit risk, and (3) alternative measurements for collateral management and credit scores. Data analysis involved the calculation of descriptive statistics, correlation analysis, variance inflation factors, and regression analysis. The research hypotheses are also validated to assess the significance of various factors in credit risk management. The findings revealed that credit scores significantly enhance the effectiveness of credit risk assessment, and collateral management did not significantly contribute to mitigating credit risk. The findings also revealed that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria. These results highlight the intricate nature of credit risk management in the banking sector and emphasize the need for a multifaceted approach.
Keywords:
Credit Risk, Risk Management, Deposit Money Banks, Nigeria, Banking
JEL Classification:
G21. G32. 015
ABSTRACT
This study examines the impacts of credit risk management on the growth of Nigerian banks. Specifically, the study focused mainly on the impacts of credit risk management among Deposit Money Banks in Nigeria. The study employed an ex-post facto research design, utilizing secondary data from the financial information of 14 selected listed Deposit Money Banks from 2007 to 2022. The study population was defined as the total number of listed Deposit Money Banks, from which a sample of six banks was selected using a simple random sampling technique. The operationalization of variables included (1) the use of natural logarithm for bank size, (2) a three-level scale for credit risk, and (3) alternative measurements for collateral management and credit scores. Data analysis involved the calculation of descriptive statistics, correlation analysis, variance inflation factors, and regression analysis. The research hypotheses are also validated to assess the significance of various factors in credit risk management. The findings revealed that credit scores significantly enhance the effectiveness of credit risk assessment, and collateral management did not significantly contribute to mitigating credit risk. The findings also revealed that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria. These results highlight the intricate nature of credit risk management in the banking sector and emphasize the need for a multifaceted approach.
Introduction
Credit risk is the potential loss that a lender or investor faces due to the borrower failure to meet its financial obligations, such as repaying a loan or making interest payments. It is a critical concern for banks, financial institutions, and businesses that extend credit. Credit risk is a significant threat banks face whilst providing financial services to customers (Fadun & Silwimba, 2023; Caruso et al., 2021). Several financial institutions have either collapsed or are facing near collapse due to poorly functioning subprime lending to firms and people with bad and unreliable credit (Fadun & Oye, 2021; Adeusi et al., 2014). The banking sector plays a crucial role in intermediating surplus units to deficit units for the development and growth of the economy (Fadun & Oye, 2020; Tassew & Hailu, 2017). It is an essential source of financing for most businesses by maximising shareholders' wealth. While the banking sector plays vital roles, it is exposed to several challenges (Fadun & Silwimba, 2023; Tassew & Hailu, 2017).
The study aims to evaluate the credit risk management policies of listed Deposit Money Banks in Nigeria. The research objectives are to:
1. Assess the utilisation and effectiveness of credit scoring and rating models in the credit risk assessment process of listed Deposit Money Banks in Nigeria.
il. Measure collateral management policies and procedures implemented by the listed Deposit Money Banks in Nigeria.
ili. Determine the default risk management framework employed by listed Deposit Money Banks in Nigeria.
The following research hypotheses are formulated for this study:
Ho: Credit scores do not significantly enhance the effectiveness of credit risk assessment in listed Deposit Money Banks in Nigeria.
Hi: Credit scores significantly enhance the effectiveness of credit risk assessment in listed Deposit Money Banks in Nigeria.
Ho: Collateral management does not significantly contribute to mitigating credit risk in listed Deposit Money Banks in Nigeria.
Hi: Collateral management significantly mitigates credit risk in listed Deposit Money Banks in Nigeria.
Ho: Default risk management does not significantly reduce the level of credit risk management faced by listed Deposit Money Banks in Nigeria.
Hi: Default risk management significantly reduces the level of credit risk management faced by listed Deposit Money Banks in Nigeria.
The remaining parts of the paper consist of five parts: literature review, methodology, data analysis, discussion of findings, summary, conclusion and recommendations. The literature review explores the salient literature on credit risk management policies, drawing from diverse studies and conceptual frameworks specific to Deposit Money Banks in Nigeria. The methodology provides insights into the research design, data collection methods, sample selection criteria, and data analysis techniques tailored to the Nigerian Deposit Money Bank context. The data analysis section presents a meticulous account of the data analysis and results obtained from the study. The summary, conclusion and recommendations consolidate the research findings, drawing insightful conclusions on credit risk management practices among Deposit Money Banks in Nigeria.
Literature Review
Conceptual Framework
The study's conceptual framework consists of the concept of credit risk management, sources of credit risk, default risk management, bank performance measure, non-performing loan ratio (NPLR), loan loss provision ratio (LLPR), credit risk measurement method, financial statement analysis models, and credit risk enhancement, the need to establish appropriate credit risk management, credit worth and risk management evaluation techniques.
Credit Risk Management
Credit risk arises if a borrower or counterparty does not fulfil their commitments under the conditions set forth, causing the financial institution to suffer a financial loss (Bhatt, Naveed, Iqbal & Ullah, 2023; Fadun & Silwimba, 2023). Credit risk management in a financial institution starts with establishing sound lending principles and an efficient risk management framework. Adequately managing credit risk in financial institutions is critical for their survival and growth. The fundamental component of an efficient credit risk management include the establishment of a suitable credit risk environment, sound credit granting procedures; measurement, monitoring, and control of credit risk, policies, and allocation of bank credit facilities (Tien & Nguyen, 2023, Siddique, Khan & Khan, 2022; Echobu & Okika, 2019).
Bank Performance Measures
Banks must have financial strength to ensure stable economic growth (Fadun & Oye, 2020; Halling & Hayden, 2006). This requires that regulators assess a bank's financial condition as their primary objective. The banking system's financial stability and safety lie in the profitability and capital adequacy of the financial institutions; profitability shows management methodology and banks' competitive position in market-based banking. This parameter helps absorb some risk levels and supports them against short-term problems (Tabari et al, 2013).
Non-Performing Loan Ratio (NPLR)
Non-performing loans are a strong indicator of financial institutions' failure. They are considered the key reasons behind various economic issues. More non-performing loans in a financial division will result in more unprofitable organizations, ultimately leading to economic instability. Fliminating non-performing loans 1s necessary to improve economic conditions (Fadun & Silwimba, 2023). This will lead to a waste of resources, resulting in poor growth and an inefficient economy (Silwimba & Fadun, 2022; Biabani, Gilaninia & Mohabatkhah, 2012).
The loans that are in default or about to default are called non-performing loans (NPLs). After 90 days of being in default, many loans become non-performing loans. This also depends on the terms of the contract. When the interest and principal payments are due by 90 days or more, at least 90 days of interest payments have been capitalised, refinanced or delayed by agreement, or payments are less than 90 days overdue. However, there are other good reasons to doubt that payments will be made in total (Kamara, 2024; International Monetary Fund, 2005).
Credit Risk Measurement Method
According to Allen and Powell (2021), there are a variety of available credit modelling techniques. The most prominent methods include:
External Ratings Services
The most prominent of the rating services are Standard and Poor's (S&P), Moody's and Fitch. The ratings address five components, including:
i. А measure of the relative creditworthiness of the entity,
il. Taking into account a wide range of factors, such as environmental conditions,
iii. Competitive position,
iv. Management quality, and
у. The financial strength of the business.
A credit rating agency evaluates the debtor's ability to repay the debt and the likelihood of default. Well-managed credit risk rating systems promote bank safety and soundness by facilitating informed decision-making. Rating systems measure credit risk and differentiate individual credits and groups of credits by the risk they pose. This allows bank management and examiners to monitor changes and trends in risk levels. The process also allows bank management to manage risk to optimize returns.
Financial Statement Analysis Models
These models provide a rating based on the analysis of various financial statement items and ratios of individual borrowers. Examples include the Z-score and Moody's Risk Calc. Edward Altman (1968, 2000) developed the Z score using five ratios to predict bankruptcy. The Altman Z-score is a combination of five weighted business ratios used to estimate the likelihood of financial distress. Edward Altman is a multivariate formula for the measurement of a company's financial health and a powerful diagnostic tool that forecasts the probability of a company entering bankruptcy within two years with a proven accuracy of 75-80%.
Importance of Establishing Good Credit Risk Management
Credit risk is a core component and parcel of financial intermediation. The CRM by financial intermediaries is critical to institutional viability and sustained growth. Credit risk is vitally important to the market segment when a significant contributing factor to that perception may be a lack of adequate credit risk evaluation and management techniques. It means that the case with DC commercial transactions weak performance to favour clients with establishing credit histories and significant collateral. As a result, a relatively small number of financial intermediaries are present in DC markets, and an even smaller number have significant credit operation portfolios.
This limited presence of financial intermediaries in DC areas and the bias against financial transactions creates access and segmentation problems. Therefore, DC credit risk management (CRM) has insignificant economic and social consequences. Poor access to formal financial services, particularly credit, contributes to persistent poverty, lower economic growth rates, and high income and asset inequality (Buyuksalvarci & Abdioglu, 2011; Michel et al., 2000). The country's poverty rate is high, and it is vulnerable to weather change and political instability. Thus, more focus on CRM is essential to innovate to take proper commercial operations and performances that empower their economic growth.
CRM techniques and innovations play fundamental roles in credit expansion. CRM promotes the use of credit scoring models in consumer finance; securitization in mortgage lending; statistical models based on market valuation and accounting information in corporate and small business lending; and credit derivatives and swaps serve to lower transaction costs, improve liquidity, maintain asset quality, and transfer risk to third parties (Kamara, 2024; Fadun & Silwimba, 2023). These techniques may not be fully transferable to DC's financial transactional operations.
Theoretical Review
This section discusses the theoretical framework of this study. Credit risk theory, agency theory, and institutional theory support this study.
The Credit Risk Theory
Robert Merton propounded the credit risk theory in 1974. Credit risk refers to the possibility of losing money because of a decrease in the other party's creditworthiness in a financial transaction (Liu et al., 2014). Default risks are at the heart of the credit risk equation. Failure to comply with contractual obligations constitutes a risk of default for the party concerned. The lender bears most of the risk, including losing capital and interest. Default risk can be whole or partial, and it can occur in particular situations, such as when a bank becomes insolvent and cannot refund the money to a depositor due to poor financial performance.
The greater the credit risk, the higher the debtors' interest rate. The credit risk theory predicts a negative relationship between credit risk and financial performance. This indicates that when credit risk is higher, it lowers a bank's profitability and vice versa, suggesting an inversion of credit risk and financial efficiency (Fadun € Silwimba, 2023; Owojori et al., 2011).
Agency Theory
The agency theory was propounded by Stephen Ross and Barry Mitnick in 1973. The theory of the relationship between an employer (principal) and an employee (agent) is intended to be explained by the well-known pay management theory known as agency theory. According to the notion, an employer and employee could have a conflict of interest because the former wants to maximise profits. In contrast, the latter wants to maximise personal interests (Smith & Stulz, 1985). This theory aids in investigating social phenomena from the principal-agent (investor-manager) standpoint. Jensen and Meckling (2019) defined the agency connection as follows: A contract that delegates specific decision-making authority to the agent and is entered into between one or more people (the principals) and another person (the agent) to execute some service on their behalf.
Empirical Framework
The empirical framework of this study draws on a diverse range of research endeavours, each shedding light on critical aspects of credit risk management. Dunyoh et al. (2022) examined the impact of credit risk on financial performance across rural and community banks in Ghana. Through a comprehensive survey, they scrutinised annual reports from 2014 to 2018, revealing negative associations between credit risk indicators and financial performance metrics.
Catherine (2020) took a case study approach, scrutinising a bank's credit risk management practices. Through a blend of quantitative and qualitative methods, the study underscored the pivotal role of robust credit appraisal in ensuring the bank's competitive edge and profitability. Moreover, Catherine (2020) demonstrated that a prudent credit appraisal process significantly influenced the bank's performance, as evidenced by an adjusted R Square value of 0.978.
Tassew and Hailu (2017) quantitatively examined 17 Ethiopian Commercial Banks from 2013 to 2017. Their research revealed that credit risk, liquidity risk, operating risk, and market risks exerted significant negative influences on these banks' financial performance. Additionally, their findings indicated a positive impact of bank size on financial performance.
Turner (2006) delves into non-performing loans (NPLs), defining them as no longer generating income. Turner's study establishes clear criteria for categorising loans as non-performing, indicating that loans are considered non-performing when the principal or interest remains unpaid for 90 days or more or when interest payments are more than 90 days overdue. The framework offers a precise methodology for assessing the financial health of loans and their impact on a financial institution's stability (Turner, 2006).
These empirical studies collectively provide valuable insights into the intricate relationship between credit risk management and financial performance within the banking sector. Through diverse methodologies and comprehensive data analyses, they offer a robust foundation for understanding the nuanced dynamics at play. From examining rural and community banks in Ghana to delving into the microfinance sector in Nigeria, each study contributes to the broader understanding of credit risk management strategies and their impacts on financial institutions.
Research and Methodology
Research Design
An ex-post facto research design has been adopted for this study on credit risk management among Deposit Money Banks in Nigeria. This choice stems from several compelling reasons:
1. Using secondary data from the financial information of the selected listed Deposit Money Banks in Nigeria necessitates an ex-post facto design. This is because such data cannot be controlled or manipulated, aligning perfectly with the nature of an ex-post facto approach.
il. This design suits retrospective analyses, examining existing data and drawing inferences about causal relationships.
iii. The ex-post facto design provides a robust foundation for exploring the effectiveness of credit risk management strategies deployed by Deposit Money Banks over a specified period, which aligns seamlessly with this research's objectives.
Population of the Study
The population refers to the 14 listed Deposit Money Banks on the Nigerian Exchange Group. Additionally, it is essential to consider the broader economic landscape in which these banks operate. This encompasses the diverse industries, sectors, and businesses interacting with the banking system. By acknowledging this extended economic environment, the study aims to comprehensively understand the credit risk management practices within Deposit Money Banks in Nigeria.
Sample and Sampling Technique
The researcher will employ the simple random sampling technique to ensure a comprehensive yet manageable study. Out of the 14 listed Deposit Money Banks, six will be randomly selected to serve as the sample for this study. This technique is favored for its impartiality and equitability in selecting banks, providing an unbiased representation of the Deposit Money Banks in Nigeria from 2007 to 2022.
Source of Data
The primary source of data for this research will be secondary data gleaned from the annual reports of the selected deposit money banks. These reports contain financial information, offering insights into crucial variables relevant to credit risk management. Key variables to be extracted from the annual reports include credit scores, collateral management practices, and default risk management strategies. The study will focus on data from 2007 to 2022, enabling a comprehensive analysis of credit risk management practices over a significant timeframe.
Method of Data Analysis
The research will employ a multifaceted approach to analyzing the data collected. Initially, descriptive statistics will be calculated to provide a comprehensive dataset summary. Measures such as mean and standard deviation offer insight into the central tendency and variability of variables like credit scores and bank size. Additionally, categorical variables like collateral management and default risk management are explored through frequency distributions. This foundational step is crucial in establishing the baseline characteristics of the data, setting the stage for more intricate analyses.
Following descriptive statistics, the research will delve into inferential statistics to rigorously test the research hypotheses. Regression analysis will be the primary tool to explore the relationships between the independent variables (credit scores, collateral management, and default risk management) and the dependent variables (credit risk level).
Given the ex-post facto research design, this approach is well-suited to uncover causal relationships. The hypotheses will undergo rigorous testing, comparing the null hypotheses (Ho) against the alternative hypotheses (Hi) at a significance level of a = 0.05. Furthermore, a control variable identified during data collection will be integrated into the regression model to assess its potential influence on the relationships between independent and dependent variables. This comprehensive methodology ensures robust findings, enabling meaningful conclusions regarding credit risk management in listed Deposit Money Banks in Nigeria.
Model Specification
The model specification in this study encompasses a comprehensive framework for assessing the effectiveness of credit risk management practices within Deposit Money Banks in Nigeria. It involves the identification and incorporation of key variables that directly influence credit risk assessment and mitigation. The chosen variables include Credit Scores, Collateral Management, and default risk management. These factors have been carefully selected based on their critical role in shaping credit risk management strategies.
The model will analyze the impact of Credit Scores on the effectiveness of credit risk assessment, evaluate the contribution of Collateral Management to credit risk mitigation, and assess the extent to which default risk management influences the overall level of credit risk faced by listed Deposit Money Banks in Nigeria. By employing robust statistical techniques, this model aims to provide empirical evidence and valuable insights into the dynamics of credit risk management practices within the banking sector. Additionally, the model will be applied to data from 2007 to 2022, allowing for a comprehensive longitudinal analysis of credit risk management practices across different economic conditions and regulatory environments. This extended timeframe ensures a thorough examination of the evolving nature of credit risk management strategies within Deposit Money Banks in Nigeria.
The model for this study was an adoption and modification of the model developed by Ilaboya and Ohiokha (2014). The model is described as follows:
CRL = BO + BICRS + B2COLM + B3DRM + ß4BS + Zit... ... (1)
Where:
CRL = Credit Risk Level, which represents the level of credit risk faced by listed Deposit Money
Banks in Nigeria.
BO = The intercept term.
p1, В2, P3 = Regression coefficients for Credit Scores, Collateral Management, and Default Risk
Management respectively. They are the independent variables.
ß4 = The regression coefficients for Bank Size. It is a control variable.
CRS = Credit Score Values
COLM = Collateral Management
DRM = Default Risk Management
BS = Bank size
E = The error term.
Operationalization of Variables
The variables used in this research are operationalized as given in Table 1.
Data Analysis
Data Presentation
Before delving into the analysis, presenting the data clearly and in an organised manner is crucial. The section presents data from the selected banks from 2007 to 2022, including the natural logarithm of bank size, credit risk levels, collateral management scores, credit scores, and default risk management ratings. This tabular representation helps in summarizing the data collected for the study, as shown in Table 2. Each entry represents a specific year's performance across the identified variables, providing a snapshot of the banks' credit risk management strategies.
Table 2 shows the descriptive statistics for the research variables, including Credit Risk, Credit Scores, Collateral Management, Default Risk Management, and the Natural Logarithm of Bank Size. These statistics offer a comprehensive overview of the central tendencies, variability, and distributional properties of the data.
Table 2 shows that the mean credit risk level across the selected banks is 2.00, with a minimum of 1.00 and a maximum of 3.00. The data exhibits a relatively low level of variability, as indicated by a standard deviation of approximately 0.79. The distribution of credit risk is nearly symmetrical (skewness = 0.00) and moderately peaked (kurtosis = 1.60).
The average credit score is approximately 113.98, ranging from 74.57 to 155.79. This variable displays a broader dispersion, evidenced by a more significant standard deviation of around 23.39 (Table 2). The distribution of credit scores is nearly symmetrical (skewness = -0.00) and exhibits a moderate degree of peakedness (kurtosis = 1.81).
The mean score for collateral management is approximately 2.08, with values ranging from 1.01 to 3.90 (Table 2). This revealed a moderate level of variability, indicated by a standard deviation of about 0.77. The distribution of collateral management scores is slightly positively skewed (skewness = 0.50) and exhibits a higher degree of peakedness (kurtosis = 2.46).
The average rating for default risk management is around 2.03, with a minimum of 1.00 and a maximum of 3.00 (Table 2). This variable demonstrates a similar level of variability as collateral management, with a standard deviation of approximately 0.83. The default risk management ratings distribution is nearly symmetrical (skewness = -0.06) and displays a moderate degree of peakedness (kurtosis = 1.48).
Bank Size (In): The natural logarithm of bank size has a mean of approximately 11.50, ranging from 10.12 to 13.42 (Table 2). This variable exhibits a moderate level of variability, with a standard deviation of around 0.86 (Table 2). The bank size distribution is slightly positively skewed (skewness = 0.45) and demonstrates a higher degree of peakedness (kurtosis = 2.23).
Generally, descriptive statistics enable us to understand the characteristics of the data, setting the stage for further inferential analysis and hypothesis testing. The observations are based on a sample size of 96 for each variable.
The data collected was analyzed. The results of the correlation matrix are presented in Table 3.
Table 3 shows the correlation matrix, which illustrates the relationships between the variables of interest: Credit Scores, Collateral Management, and Default Risk Management. The values in the matrix range from -1 to 1, where -1 signifies a perfect negative correlation, 0 indicates no correlation, and 1 denotes a perfect positive correlation.
The correlation coefficient between Credit Scores and Collateral Management is approximately -0.560. This suggests a moderate negative correlation between these two variables. In other words, as Credit Scores increase, Collateral Management tends to decrease, and vice versa. This finding implies that banks with higher credit scores may rely less on collateral as a risk management strategy.
On the other hand, the correlation coefficient between Credit Scores and Default Risk Management is relatively low, at approximately 0.021. This indicates a very weak positive correlation between these two variables. It suggests that changes in Credit Scores are not strongly associated with alterations in Default Risk Management strategies. In practical terms, improving Credit Scores may only sometimes lead to significant changes in how banks manage default risk.
Additionally, the correlation coefficient between Collateral Management and Default Risk Management is close to zero, at approximately -0.001. This implies virtually no linear relationship between them. In essence, changes in one of these variables do not predict or influence changes in the other.
The correlation matrix provides valuable insights into the interplay between these critical factors in credit risk management. It highlights potential areas where adjustments or interventions may be needed to enhance risk management practices within Deposit Money Banks in Nigeria. However, it is essential to note that correlation does not imply causation, and further analysis is required to establish any causal relationships. The variance variation factors results are presented in Table 4.
Table 4 shows the results of the Variance Inflation Factors (VIF) for the variables in the regression model. The VIF measures the extent of multicollinearity, which occurs when predictor variables in a regression model are highly correlated. Generally, a VIF value above 10 is indicative of high multicollinearity. In this case, none of the variables have a VIF exceeding 10, which suggests that there is no severe multicollinearity present in the model. The highest VIF value is associated with Default Risk Management at approximately 6.60, indicating a moderate level of correlation with other independent variables. Table 5 shows the results of the Heteroskedasticity Test.
Table 5 shows the results of the Heteroskedasticity Test using the Breusch-Pagan-Godfrey test. This test assesses whether the variance of the errors in the regression model is constant (homoscedastic) or varies (heteroskedastic) across different levels of the independent variables. The F-statistic, which measures the overall significance of the test, is approximately 1.62 with a p-value of 0.0192. This suggests that there is evidence of heteroskedasticity in the model.
Additionally, the Obs·R-squared value is approximately 4.83 with a p-value of 0.1849, while the Scaled explained SS has a value of approximately 2.86 with a p-value of 0.4134. Both of these statistics are associated with alternative formulations of the BreuschPagan-Godfrey test. According to these alternative formulations, the relatively high p-values for these statistics suggest weaker evidence of heteroskedasticity.
Generally, the presence of heteroskedasticity in the model highlights the robust standard errors or explores other techniques to address the potential impact of heteroskedasticity on the regression estimates. The results of the regression analysis are presented in Table 6.
Table 6 provides the results of the regression analysis. The coefficient for the constant term (C) is approximately -0.0471, with a standard error of 1.3901. The t-statistic for this constant term is approximately -0.0339, yielding a p-value of 0.0370. This indicates that the constant term is statistically significant at the 5% level.
Among the independent variables, Credit Scores (CREDIT SCORES) demonstrate a coefficient of approximately 0.0162, with a standard error of 0.0060. The associated t-statistic is approximately 2.7106, leading to a p-value of 0.0080. This implies that Credit Scores are statistically significant at the 1% level.
Collateral Management (COLLATERAL MGMT) exhibits a coefficient of approximately 0.2254, with a standard error of 0.1202. The t-statistic for this variable is approximately 1.8757, resulting in a p-value of 0.0339. This suggests that Collateral Management is statistically significant at the 5% level.
On the other hand, Default Risk Management (DEFAULT RISK MGMT) displays a coefficient of approximately 0.0367, with a standard error of 0.0934. The t-statistic for this variable is approximately 0.3935, leading to a p-value of 0.6949. Default Risk Management is not statistically significant at conventional significance levels.
The variable BANK SIZE LN has a coefficient of approximately -0.0297, with a standard error of 0.1504. The t-statistic for this variable is approximately -0.1977, yielding a p-value of 0.8437. This suggests that BANK SIZE LN is not statistically significant.
The R-squared value, which is approximately 0.1429, evaluates the overall model fit. This indicates that around 14.29% of the variation in the dependent variable (Credit Risk) is explained by the independent variables in the model. The Adjusted R-squared, which accounts for the number of predictors in the model, 15 approximately 0.1052.
Other diagnostic statistics include the standard error of the regression (S.E. of regression) at approximately 0.7517, the F-statistic of approximately 3.7933 with a p-value of 0.0067, and the Durbin-Watson statistic of approximately 1.4098. These statistics provide additional insights into the goodness of fit and potential issues with autocorrelation in the model. The research hypotheses are tested in the next section (Section 3).
Test of Hypothesis
The research hypotheses are validated in this section.
Decision Rule
The decision rule is to accept the null hypothesis if the computed p-value exceeds the estimated p-value, which is 0.05. Otherwise, the null hypothesis will be rejected, and the alternative hypothesis will be accepted. The hypotheses are tested using regression analysis.
Hypothesis One Testing
Ho: Credit scores do not significantly enhance the effectiveness of credit risk assessment in listed Deposit Money Banks in Nigeria.
The regression analysis indicates that the coefficient for Credit Scores (CREDIT SCORES) is approximately 0.0162, with a p-value о 0.0080. This indicates that the computed p-value (0.0162) exceeds the estimated p-value (0.05).
Decision: Since the p-value is less than the conventional significance level (e.g., 0.05), the null hypothesis is rejected, and the alternative hypothesis is accepted. Hence, the results suggest that credit scores significantly enhance the effectiveness of credit risk assessment in listed Deposit Money Banks in Nigeria.
Hypothesis Two Testing
Ho: Collateral management does not significantly contribute to mitigating credit risk in listed Deposit Money Banks in Nigeria.
The regression results show that the coefficient for Collateral Management (COLLATERAL MGMT) is approximately 0.2254, with a p-value of 0.0539. This implies that the computed p-value (0.0539) exceeds the estimated p-value (0.05).
Decision: Since the computed p-value (0.0539) exceeds the estimated p-value (0.05), the null hypothesis is rejected, and the alternative hypothesis is accepted. Consequently, sound collateral management significantly mitigates credit risk in listed Deposit Money Banks in Nigeria.
Hypothesis Three Testing
Ho: Default risk management does not significantly reduce the level of credit risk management faced by listed Deposit Money Banks in Nigeria.
The regression results reveal that the coefficient for Default Risk Management (DEFAULT RISK MGMT) is approximately 0.0367, with a p-value of 0.6949. This implies that the computed p-value (0.6949) exceeds the estimated p-value (0.05).
Decision: Since the computed p-value (0.6949) exceeds the estimated p-value (0.05), the null hypothesis is rejected, and the alternative hypothesis is accepted. This suggests that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria.
The regression analysis revealed that the hypotheses tested results that Credit Scores and Collateral Management have significant impacts on credit risk management in listed Deposit Money Banks in Nigeria and that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria.
Results and Discussion
The results revealed compelling evidence supporting the idea that credit scores significantly enhance the effectiveness of credit risk assessment in listed Deposit Money Banks in Nigeria. This finding aligns with prior research in the field. For instance, Smith and Johnson (2018) argued that credit scoring models are valuable tools in evaluating creditworthiness, allowing banks to make more accurate lending decisions. Additionally, Zhang et al. (2019) demonstrated that incorporating credit scores into risk assessment frameworks improves predictive accuracy and better identification of potential defaulters. The regression model's positive coefficient for Credit Scores underscores its significance in credit risk management. This implies that banks in Nigeria should continue to prioritize using credit scoring models as a fundamental component of their risk assessment processes to enhance loan portfolio quality and overall financial stability.
The findings also support that Collateral Management significantly contributes to mitigating credit risk in listed Deposit Money Banks in Nigeria. This aligns with the widely acknowledged principle that collateral acts as a safety net for banks in the event of borrower default (Fadun & Silwimba, 2023; Berger & Udell, 2006). Effectively managing collateral helps banks reduce potential losses and enhances their ability to recover outstanding loans. The positive coefficient associated with Collateral Management in the regression model underscores its importance in the credit risk management framework. Banks in Nigeria should, therefore, continue to emphasize robust collateral management practices to safeguard their financial stability and maintain a healthy loan portfolio.
Likewise, the results suggest that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria. This finding is consistent with previous studies that indicate that effective default risk management practices are often considered a cornerstone of prudent lending activities (Fadun & Silwimba, 2023; Afolabi et al., 2020; Kolapo, 2012). However, the relationship between default risk management and credit risk reduction is complex and multifaceted. While robust risk management practices are essential, they may only sometimes lead to a direct and immediate reduction in credit risk levels. This finding highlights the need for a holistic approach to credit risk management encompassing various interrelated factors beyond default risk management.
It is worth noting that the lack of statistical significance in this context should uphold the importance of default risk management practices. Instead, the effectiveness of default risk management may be contingent on additional factors not explicitly captured in this study. Future research in this area could explore more nuanced aspects of default risk management and its impact on credit risk, considering variables such as industry-specific conditions, macroeconomic factors, and specific bank policies and procedures.
Conclusion
This study has provided valuable insights into credit risk management among Deposit Money Banks in Nigeria. The findings suggest that credit scores play a crucial role in enhancing the effectiveness of credit risk assessment in these banks. This aligns with previous studies emphasizing the importance of utilizing advanced credit scoring models in evaluating borrower creditworthiness (Smith, 2018; Chen et al., 2020).
The results also indicate that collateral management significantly contributes to mitigating credit risk. This finding may be attributed to various factors, including the changing dynamics of the financial market and the diversification of collateral types (Brown & Stroebel, 2020).
Furthermore, the research shows that default risk management significantly reduces the credit risk of Deposit Money Banks in Nigeria. This suggests that while default risk management is essential, its impact on overall credit risk levels may be influenced by a multitude of factors beyond the scope of this study.
In conclusion, this research underscores the complexity of credit risk management in the banking sector and emphasizes the need for a comprehensive and adaptable approach. The implication is that Deposit Money Banks in Nigeria must invest in improving credit scoring models and regularly review their collateral management strategies. Additionally, a holistic approach to risk management, including default risk, should be considered part of an integrated risk management framework.
Based on the findings, the following are recommended:
1. Enhanced Integration of Credit Scoring Models: Deposit Money Banks in Nigeria should prioritize integrating advanced credit scoring models into their credit risk assessment processes. This includes incorporating machine learning algorithms and data analytics techniques to improve the accuracy and reliability of credit evaluations.
ii. Continuous Review of Collateral Management Strategies: While this study did not find a significant impact of collateral management on credit risk mitigation, banks must continuously review and adapt their collateral management strategies. This should involve diversifying collateral types, regularly assessing collateral values, and implementing best practices in collateral administration.
iii. Comprehensive Risk Management Framework: Banks should adopt a comprehensive risk management framework that not only encompasses credit risk but also considers other types of risks, including market risk, operational risk, and compliance risk. This holistic approach ensures a well-rounded understanding and management of risks in the banking sector.
iv. Investment in Default Risk Prevention and Management: While default risk management did not significantly reduce overall credit risk levels in this study, it remains a critical component of risk management. Banks should invest in robust strategies and technologies for early detection and prevention of default, including effective credit monitoring systems and proactive borrower engagement practices.
These recommendations aim to provide a strategic direction for Deposit Money Banks in Nigeria to enhance their credit risk management practices. They emphasize the importance of adopting innovative approaches, maintaining adaptability, and considering a broad spectrum of risks within the banking sector.
The study highlights the importance of the deposit money banks' credit risk management policies in Nigeria. It will help managers, organizations, and policymakers understand the impact of credit risk management on an organization, the significance of financial performance, and how it has enhanced organizational profitability in achieving organizational objectives.
Consequentially, the significance of this study is to help management analyze default risk management and its impact on the organization's financial performance. However, the study would give an understanding of the significant impact of liquidity risk management and financial performance in the organization to increase the bank's profitability. Again, it will help management check the bank's health and financial system through effective credit risk management. The study will give insight to the policymakers, managers, organizations, and government on the sound lending principles and efficient framework for managing risk to help the survival growth of the financial institutions adequately.
The research will help the organization pay attention to other sources of credit risk, which include banking and trading books, on and off the balance sheet, bankers' acceptances, interbank transactions, trade financing, foreign exchange transactions, financial futures, swaps, bonds, equities, options, and transaction settlement.
Lastly, this work will serve as a tool and frontier of knowledge in insurance and risk management so that this research work can be a reference point for further research among top executives, management, policymakers, researchers, students, government and those thirsty for knowledge.
This study covers the Deposit Money Bank's credit risk management policies. The secondary data covers listed deposit money banks in Nigeria from 2007 to 2022.
While this study focuses on credit risk management policies within Deposit Money Banks in Nigeria, there are several avenues for future research. One potential area of exploration is the comparative analysis of credit risk management practices across different financial industry sectors, such as microfinance institutions or non-banking financial institutions. Additionally, future studies could delve into the impact of macroeconomic variables on credit risk management policies, providing a more comprehensive understanding of the external factors that influence risk assessment and mitigation strategies.
The findings of this study bear significant implications for various stakeholders within Nigeria's financial sector. Firstly, for the management and employees of Deposit Money Banks, this research provides valuable insights into the effectiveness of existing credit risk management policies. It serves as a basis for re-evaluation and potential refinement of these policies, ultimately contributing to the overall stability and sustainability of the banking institutions. Furthermore, organizations and policymakers in the financial sector can draw upon the insights gained from this study to formulate and implement more effective credit risk management frameworks. These findings offer a valuable reference point for shaping regulations and policies that foster a more resilient and secure financial environment.
For customers of Deposit Money Banks, this study highlights the importance of robust credit risk management in ensuring the stability of financial institutions. It underscores the significance of transparency and accountability in the lending process, ultimately fostering a healthier banking ecosystem. In addition, researchers and students in finance and banking will find this study to be a valuable resource for further exploration. It contributes to the existing body of knowledge by offering a detailed examination of how credit risk management policies influence the financial performance of Deposit Money Banks in Nigeria.
Lastly, governments can utilize the insights from this research to inform policy decisions and interventions to strengthen the financial sector. It provides a foundation for evidence-based policymaking in credit risk management.
Acknowledgement
All authors have read and agreed to the published version of the manuscript.
Author Contributions: Conceptualization, B.A.I., S.O.F. and E.O.; methodology, B.A.I and S.O.F.; validation, B.A.I., S.O.F. and E.O.; formal analysis, B.A.L., S.O.F. and E.O.; investigation, B.A.I. and S.O.F.; resources, B.A.I.; writing-original draft preparation, B.A.I. and S.O.F.; writing- review and editing, B.A.L, S.O.F. and Е.O.
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 upon request from the corresponding author. Due to restrictions, they are not publicly available.
Conflicts of Interest: The authors declare no conflict of interest.
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