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
Effective credit risk management is fundamental to maintaining a healthy loan portfolio and ensuring the long-term financial stability of banks. First City Monument Bank (FCMB) has been at the forefront of implementing innovative credit risk management practices designed to enhance loan portfolio quality. The bank employs a combination of advanced analytical tools, comprehensive risk assessment models, and proactive monitoring systems to evaluate borrower creditworthiness and mitigate potential defaults (Akinola, 2023). These practices are rooted in the theoretical framework of risk-adjusted lending, which emphasizes the need to balance profitability with prudent risk management.
FCMB’s approach integrates quantitative models, such as predictive analytics and credit scoring, with qualitative assessments of borrower behavior and market conditions. This dual approach enables the bank to identify emerging risks and take corrective actions before these risks materialize into significant financial losses (Oluwaseun, 2024). Moreover, ongoing staff training and technological upgrades further enhance the effectiveness of the risk management framework, contributing to improved loan portfolio performance and overall asset quality. Despite these efforts, fluctuations in economic conditions and unforeseen borrower behavior continue to pose challenges in maintaining an optimal risk-return balance. This study aims to investigate how credit risk management practices at FCMB influence loan portfolio quality and to identify the key factors that contribute to effective risk mitigation.
Statement of the Problem
Despite FCMB’s comprehensive credit risk management practices, the bank still experiences challenges in maintaining a high-quality loan portfolio. Economic volatility and unpredictable borrower behavior have occasionally resulted in higher-than-expected default rates, which erode asset quality (Babatunde, 2023). Inconsistencies in data integration and limitations in predictive modeling further complicate the accurate assessment of credit risk. Additionally, the reliance on historical data may not fully capture emerging risks, leading to gaps in the bank’s risk management framework (Chinonso, 2024). Such shortcomings not only threaten financial stability but also impact the bank’s ability to sustain profitable lending practices. The operational challenge of aligning risk management practices with rapid market changes necessitates an in-depth analysis to understand where the current system may be falling short. This study will explore these issues to determine how existing credit risk management practices affect the overall quality of the loan portfolio at FCMB and to suggest improvements that can enhance risk mitigation (Emeka, 2025).
Objectives of the Study
To examine the impact of credit risk management practices on loan portfolio quality at FCMB.
To identify gaps in current risk assessment and predictive modeling techniques.
To propose strategies for improving credit risk evaluation and portfolio performance.
Research Questions
How do credit risk management practices influence loan portfolio quality at FCMB?
What are the key limitations of current risk assessment models?
How can risk management practices be enhanced to improve loan portfolio quality?
Research Hypotheses
Effective credit risk management practices are positively correlated with improved loan portfolio quality.
Gaps in predictive modeling negatively impact risk assessment accuracy.
Enhanced risk evaluation techniques lead to lower default rates and better asset quality.
Scope and Limitations of the Study
This study examines credit risk management practices at FCMB over the past three years. Limitations include potential inconsistencies in data reporting and external economic factors influencing loan performance.
Definitions of Terms
• Credit Risk Management Practices: Methods and models used to assess and mitigate the risk of borrower default.
• Loan Portfolio Quality: The overall performance and risk profile of a bank’s lending assets.
• Predictive Analytics: The use of statistical methods and machine learning to forecast future risk events.
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