An appraisal of risk management process enhancements on reducing non-performing loans in banking: a case study of First City Monument Bank
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Background of the Study
Effective risk management is essential for maintaining asset quality in the banking sector. First City Monument Bank (FCMB) has recently enhanced its risk management processes by incorporating advanced analytics, real-time monitoring, and proactive intervention strategies to reduce non-performing loans (NPLs). The bank’s new approach integrates predictive modeling with traditional credit assessment methods to provide a more dynamic and accurate evaluation of borrower risk (Afolabi, 2023). This integrated process enables FCMB to identify potential loan defaults at an early stage and implement targeted measures such as loan restructuring, enhanced collateral requirements, and revised interest rate policies.
These process enhancements are supported by a robust IT infrastructure that collects and analyzes vast amounts of financial data, enabling continuous monitoring of loan performance. Regular training programs ensure that credit officers are well-equipped to interpret risk analytics and adjust lending practices accordingly (Okeke, 2024). The adoption of these advanced risk management techniques not only improves the overall quality of the loan portfolio but also enhances the bank’s profitability and stability, even in times of economic uncertainty (Chinwe, 2025).
However, despite these improvements, FCMB still encounters challenges in fully mitigating the incidence of non-performing loans. The dynamic nature of credit risk and external economic fluctuations necessitate ongoing adjustments to risk models and continuous staff training. This study examines the impact of enhanced risk management processes on reducing NPLs at FCMB, aiming to provide insights into best practices and propose strategies for further optimization.
Statement of the Problem
Although FCMB has implemented advanced risk management processes, the bank continues to experience a significant level of non-performing loans that affect financial performance. One critical issue is the challenge of continuously updating risk models to capture emerging risk factors in a rapidly changing economic environment (Ibrahim, 2023). Integration of new analytics with existing credit evaluation procedures sometimes leads to discrepancies that delay effective risk mitigation.
Moreover, insufficient training on the updated risk management tools and variability in staff interpretation of risk indicators contribute to inconsistent application of these processes (Nwankwo, 2024). External economic shocks and market volatility further complicate risk predictions, resulting in higher default rates than anticipated. The misalignment between forecasted risks and actual loan performance undermines the effectiveness of the enhanced processes and increases the burden of non-performing loans on the bank’s balance sheet. This study seeks to analyze these challenges and provide strategic recommendations to optimize risk management processes, thereby reducing NPLs and improving overall asset quality.
Objectives of the Study
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To assess the impact of enhanced risk management processes on reducing non-performing loans at FCMB.
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To identify challenges in model integration and staff training that affect risk management outcomes.
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To recommend strategies for further optimizing risk management processes.
Research Questions
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How do risk management process enhancements affect the incidence of non-performing loans at FCMB?
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What integration and training challenges hinder the effective implementation of advanced risk management?
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What measures can improve the predictive accuracy of risk models and reduce NPLs?
Research Hypotheses
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H1: Enhanced risk management processes significantly reduce non-performing loans at FCMB.
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H2: Integration challenges negatively affect the predictive accuracy of risk models.
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H3: Continuous staff training and system upgrades are positively correlated with reduced NPLs.
Scope and Limitations of the Study
This study focuses on FCMB’s risk management framework and its impact on non-performing loans, using internal loan performance data, risk model assessments, and employee surveys. Limitations include external economic variability and challenges in isolating risk management effects from broader credit policies.
Definitions of Terms
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Risk Management Process Enhancements: Improvements made to the methods and systems used to assess and mitigate credit risk.
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Non-performing Loans (NPLs): Loans for which the borrowers are not meeting repayment obligations.
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Predictive Modeling: The use of statistical techniques to forecast future loan performance.
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Credit Risk: The potential for loss resulting from a borrower's failure to repay a loan.
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