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 ensuring the quality of a bank’s loan portfolio and maintaining financial stability. First City Monument Bank (FCMB) has implemented advanced credit risk management practices to minimize non-performing loans and optimize asset quality (Okechukwu, 2023). The bank employs a combination of predictive analytics, rigorous credit evaluation processes, and continuous monitoring systems to assess the creditworthiness of borrowers. These practices are designed to identify potential risks early and implement corrective measures, thereby safeguarding the bank’s financial health. Recent innovations in data analytics and machine learning have further enhanced FCMB’s ability to manage credit risk effectively (Adeniyi, 2024).
The evolving economic environment, coupled with rapid changes in borrower behavior, necessitates dynamic risk management frameworks that can adapt to new challenges. FCMB’s approach integrates traditional credit analysis with modern statistical tools to provide a comprehensive view of risk exposure. Academic studies indicate that effective credit risk management practices lead to improved loan portfolio quality, reduced default rates, and higher investor confidence (Chinwe, 2023). However, challenges remain in accurately predicting default risk due to external factors such as economic downturns and market volatility. This study aims to examine the impact of FCMB’s credit risk management practices on the quality of its loan portfolio, drawing on both quantitative performance data and qualitative insights from risk management professionals.
Statement of the Problem
FCMB faces persistent challenges in managing credit risk despite the adoption of advanced risk management practices. One of the key problems is the inherent unpredictability of borrower behavior, particularly during periods of economic stress, which can undermine even the most sophisticated risk assessment models (Ifeoma, 2023). While predictive analytics and continuous monitoring have improved risk detection, discrepancies remain between projected and actual loan performance. Additionally, the integration of new risk management technologies with existing evaluation processes has encountered obstacles, leading to occasional delays in risk identification and mitigation.
Furthermore, external economic factors and regulatory changes contribute to uncertainty in credit risk management. The lack of standardized metrics for evaluating credit risk also complicates the assessment of loan portfolio quality. These issues create a gap between risk management strategies and their effectiveness in reducing non-performing loans. This study seeks to determine whether the credit risk management practices at FCMB are sufficiently robust to maintain high loan portfolio quality, or if further innovations and adjustments are needed to mitigate risk in a volatile economic environment.
Objectives of the Study
• To assess the impact of credit risk management practices on the quality of FCMB’s loan portfolio.
• To identify gaps between risk assessment models and actual loan performance.
• To propose improvements to enhance the effectiveness of credit risk management.
Research Questions
• How do FCMB’s credit risk management practices influence loan portfolio quality?
• What are the primary factors contributing to discrepancies in risk predictions?
• How can risk management frameworks be enhanced to improve loan performance?
Research Hypotheses
• H1: Advanced credit risk management practices significantly improve loan portfolio quality.
• H2: Integration challenges negatively impact the accuracy of risk predictions.
• H3: Regular updates to risk models enhance the effectiveness of credit risk management.
Scope and Limitations of the Study
This study focuses on FCMB’s credit risk management practices over the past three years, using loan performance data and risk assessment reports. Limitations include external economic influences and potential model biases.
Definitions of Terms
• Credit Risk Management Practices: Methods and processes used to evaluate 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 data, statistical algorithms, and machine learning to forecast future outcomes.
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