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 fundamental to the stability and profitability of banks, particularly in managing non-performing loans (NPLs). First City Monument Bank (FCMB) has enhanced its risk management processes by incorporating advanced analytics, real-time monitoring, and automated credit scoring systems to identify and mitigate credit risks early in the loan lifecycle (Akinola, 2023). These enhancements allow FCMB to assess borrower creditworthiness more accurately, monitor loan performance continuously, and implement timely interventions to reduce defaults.
By integrating modern risk management techniques with traditional lending practices, FCMB has been able to create a more dynamic and responsive risk management framework. The use of predictive analytics and scenario analysis enables the bank to forecast potential default risks and take corrective actions before loans become non-performing (Chukwu, 2024). This proactive approach not only helps in reducing NPLs but also improves overall asset quality and financial performance. The system is further reinforced by regular internal audits and compliance reviews, ensuring that risk management practices remain robust and aligned with regulatory requirements (Ibrahim, 2025).
Despite these advancements, challenges persist in fully realizing the benefits of risk management process enhancements. Issues related to data quality, system integration, and the dynamic nature of market conditions can limit the accuracy of risk assessments. Moreover, the rapid evolution of economic factors and borrower behavior may outpace the predictive capabilities of current models, leading to discrepancies between forecasted and actual loan performance. This study aims to evaluate the impact of risk management process enhancements on reducing NPLs at FCMB, analyzing both the successes and limitations of the current system and offering recommendations for further improvement.
Statement of the Problem
Although FCMB has significantly enhanced its risk management processes to reduce non-performing loans, challenges remain in achieving the desired reduction in defaults. One major problem is the reliability of predictive models, which is often compromised by data quality issues and integration challenges with legacy systems (Akinola, 2023). Inaccurate or incomplete data can lead to misestimation of credit risk, resulting in delayed or inappropriate interventions. Additionally, the complexity of integrating advanced analytics with traditional credit evaluation processes may cause inconsistencies in risk assessment, thereby contributing to a higher incidence of NPLs (Chukwu, 2024).
Furthermore, external economic volatility and sudden market shifts can render predictive models less effective, creating a gap between expected and actual loan performance. Insufficient continuous training for risk management staff further diminishes the efficacy of the system, as employees may struggle to interpret complex data outputs accurately (Ibrahim, 2025). These challenges underscore the need for a more refined approach to risk management that not only leverages advanced technology but also addresses issues related to data integrity and human resource capability.
This study seeks to identify the specific factors that limit the effectiveness of FCMB’s risk management enhancements and to propose strategies to improve data quality, system integration, and staff proficiency, ultimately reducing non-performing loans.
Objectives of the Study
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To evaluate the impact of risk management process enhancements on reducing non-performing loans at FCMB.
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To identify challenges in data quality and system integration affecting risk assessments.
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To recommend strategies for improving predictive accuracy and staff training.
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 challenges hinder accurate risk assessment in the current framework?
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What measures can improve data quality and employee proficiency in risk management?
Research Hypotheses
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H₁: Risk management process enhancements significantly reduce non-performing loans at FCMB.
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H₂: Data quality and integration challenges negatively affect risk assessment accuracy.
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H₃: Continuous training and system improvements enhance the effectiveness of risk management.
Scope and Limitations of the Study
This study examines FCMB’s risk management practices over the past three years, using internal loan performance data, risk assessment reports, and interviews with risk management personnel. Limitations include external economic variability and challenges in obtaining complete data.
Definitions of Terms
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Risk Management Process Enhancements: Improvements in methodologies and technologies used to assess and manage credit risk.
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Non-Performing Loans (NPLs): Loans that are in default or close to being in default.
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Predictive Models: Statistical models used to forecast borrower behavior and credit risk.
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