An investigation of credit risk management practices on loan portfolio quality in banking: a case study of First City Monument Bank
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Background of the Study
Credit risk management is fundamental to maintaining a healthy loan portfolio in the banking sector. First City Monument Bank (FCMB) has implemented innovative credit risk management practices to minimize defaults and improve the quality of its loan portfolio (Adediran, 2023). These practices include rigorous credit assessment procedures, advanced predictive modeling, and continuous monitoring of borrower performance (Olawale, 2024). Effective credit risk management ensures that loans are granted to creditworthy customers, thereby reducing the likelihood of non-performing assets and enhancing overall financial stability.
FCMB’s approach integrates traditional financial analysis with modern analytical tools, enabling the bank to assess risk more accurately and make informed lending decisions. The adoption of data-driven strategies has allowed FCMB to adjust its credit policies dynamically in response to market changes and borrower behavior (Babatunde, 2023). However, despite these advancements, the challenge remains in maintaining a consistently high-quality loan portfolio in a volatile economic environment. External factors such as market fluctuations, regulatory changes, and shifts in consumer behavior can significantly impact credit risk, necessitating continuous adaptation of risk management practices (Eze, 2024).
This study aims to investigate how FCMB’s credit risk management practices influence the quality of its loan portfolio. By analyzing historical loan performance data, borrower profiles, and risk assessment methodologies, the research will identify the key factors that contribute to credit risk mitigation. The findings are expected to provide insights into best practices for credit risk management, which can be applied by banks to enhance loan portfolio quality and safeguard financial stability (Adediran, 2023; Olawale, 2024).
Statement of the Problem
FCMB faces ongoing challenges in managing credit risk, as external economic volatility and evolving borrower profiles continue to affect loan portfolio quality. Despite implementing advanced risk management practices, the bank experiences fluctuations in non-performing loans, which can undermine overall profitability (Babatunde, 2023). One major issue is the difficulty in accurately predicting borrower behavior over extended periods, particularly in times of economic uncertainty. The limitations of predictive models and data quality issues further complicate risk assessments, leading to occasional lapses in credit evaluation (Eze, 2024).
Additionally, the complexity of integrating diverse data sources and updating risk models in real time poses significant operational challenges. The gap between theoretical risk management frameworks and their practical application in a dynamic market environment remains a critical concern (Adediran, 2023). This study seeks to address these issues by examining the effectiveness of current credit risk management practices at FCMB and their impact on loan portfolio quality. By identifying shortcomings in existing approaches, the research will offer recommendations to improve risk assessment accuracy and loan performance. This investigation is crucial for enabling banks to mitigate credit risk more effectively, thereby ensuring long-term financial sustainability and stability (Olawale, 2024).
Objectives of the Study:
1. To examine the impact of credit risk management practices on loan portfolio quality at FCMB.
2. To identify key factors that influence borrower risk profiles and loan performance.
3. To propose improvements for enhancing credit risk assessment accuracy.
Research Questions:
1. How do current credit risk management practices affect loan portfolio quality at FCMB?
2. What are the key factors influencing credit risk in the bank’s loan portfolio?
3. What measures can be implemented to improve risk assessment and loan performance?
Research Hypotheses:
1. Effective credit risk management practices significantly improve loan portfolio quality.
2. Advanced predictive models enhance the accuracy of credit risk assessments.
3. Continuous improvement in risk management processes leads to reduced non-performing loans.
Scope and Limitations of the Study:
This study focuses on FCMB’s loan portfolio, utilizing historical performance data and risk assessment methodologies. Limitations include potential data quality issues and the influence of external economic variables.
Definitions of Terms:
• Credit Risk Management: Strategies used to assess and mitigate the risk of borrower default.
• Loan Portfolio Quality: The overall performance and risk profile of a bank’s loan assets.
• Non-Performing Loans: Loans on which the borrower is not making interest or principal payments.
• Predictive Modeling: The use of statistical techniques to forecast future outcomes.
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