An Evaluation of Risk Management Practices on Non-Performing Loan Ratios in Banking: A Case Study of First City Monument Bank
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- NGN 5000
Background of the Study
Effective risk management is paramount in maintaining a healthy loan portfolio and reducing the incidence of non-performing loans (NPLs). First City Monument Bank (FCMB) has implemented comprehensive risk management practices that combine traditional credit assessment methods with modern predictive analytics to monitor borrower performance and mitigate credit risk (Adediran, 2023). These practices involve regular portfolio reviews, dynamic credit scoring, and the integration of big data to forecast potential defaults. By proactively identifying high-risk loans, FCMB aims to minimize losses and maintain a strong asset quality. Enhanced risk management not only improves profitability but also bolsters the bank’s reputation among investors and regulators (Olawale, 2024). However, despite these efforts, FCMB continues to grapple with elevated NPL ratios, which have a detrimental impact on financial stability. This may be due to integration challenges between legacy systems and new analytical tools, as well as inconsistencies in applying risk management frameworks across different loan segments (Chinwe, 2023). This study seeks to evaluate the effectiveness of FCMB’s risk management practices by analyzing historical loan performance data, credit scoring models, and borrower profiles. The objective is to identify key factors contributing to non-performing loans and propose measures to enhance risk management strategies, thereby reducing NPL ratios and improving overall financial performance.
Statement of the Problem
Despite robust risk management systems, First City Monument Bank continues to experience high non-performing loan ratios that adversely affect its profitability and asset quality. The bank faces challenges in accurately predicting borrower defaults, partly due to data integration issues between traditional credit models and modern analytics (Adediran, 2023). Inconsistencies in the application of risk management practices across various lending segments have also been identified, leading to pockets of elevated risk. Furthermore, external factors such as economic downturns and changes in borrower behavior further complicate the effectiveness of existing risk assessment methods (Olawale, 2024). The lack of a unified evaluation framework makes it difficult to pinpoint weaknesses within the risk management process, thereby impeding the bank’s ability to take corrective measures promptly. This study aims to investigate these issues by evaluating the correlation between risk management practices and NPL ratios, with the goal of identifying operational gaps and proposing strategic improvements to reduce non-performing loans (Chinwe, 2023).
Objectives of the Study:
1. To evaluate the impact of current risk management practices on NPL ratios at FCMB.
2. To identify factors contributing to high non-performing loans.
3. To recommend strategies for improving credit risk assessment.
Research Questions:
1. How do risk management practices affect NPL ratios at FCMB?
2. What are the primary factors contributing to non-performing loans?
3. What improvements can reduce NPL ratios?
Research Hypotheses:
1. Effective risk management significantly reduces NPL ratios.
2. Data integration issues negatively impact risk assessment accuracy.
3. Enhanced monitoring and dynamic models lower non-performing loans.
Scope and Limitations of the Study:
This study focuses on FCMB’s loan portfolio and risk management systems using historical data and internal reports. Limitations include external economic fluctuations and data integration complexities.
Definitions of Terms:
• Risk Management Practices: Methods used to assess and mitigate credit risk.
• Non-Performing Loans (NPLs): Loans in default or close to default.
• Credit Scoring Models: Tools used to evaluate borrower creditworthiness.
• Predictive Analytics: Techniques for forecasting loan performance.
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