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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- NGN 5000
Background of the Study
Effective risk management is essential in minimizing non-performing loans (NPLs) and ensuring the financial stability of banks. First City Monument Bank (FCMB) has recently enhanced its risk management processes by integrating advanced analytical tools, real-time monitoring systems, and comprehensive credit evaluation frameworks. These process enhancements enable the bank to identify high-risk lending portfolios earlier and implement proactive measures to mitigate potential defaults (Okeke, 2023). By leveraging big data and predictive analytics, FCMB can monitor borrower performance, assess economic trends, and adjust lending strategies accordingly, leading to improved asset quality and reduced NPLs.
The improved risk management framework includes robust internal controls and regular stress testing, which are critical for maintaining regulatory compliance and investor confidence. The bank’s focus on continuous improvement in risk management practices has allowed it to align with international best practices, ensuring that lending decisions are based on accurate, real-time data (Adebayo, 2024). Enhanced risk processes also foster a culture of accountability and transparency, which further reduces the likelihood of credit losses. This strategic initiative is instrumental in maintaining a healthy loan portfolio and supporting sustainable growth in a competitive market.
Despite these significant improvements, challenges remain in fully realizing the benefits of risk management process enhancements. Integration issues with legacy systems, data quality concerns, and the need for ongoing staff training can limit the effectiveness of these measures. This study appraises the impact of enhanced risk management processes on reducing non-performing loans at FCMB, analyzing both the operational benefits and the barriers that need to be overcome for optimal credit risk management.
Statement of the Problem
Although FCMB has enhanced its risk management processes to reduce non-performing loans, persistent challenges continue to undermine their effectiveness. One major issue is the difficulty in integrating new analytical tools with existing legacy systems, which often results in data discrepancies and delays in risk assessment (Ibrahim, 2024). These integration challenges can lead to inadequate identification of high-risk loans, causing delays in intervention measures that are critical for preventing defaults.
Moreover, the quality of data collected from various sources may not always be consistent, leading to inaccuracies in predictive models. The dynamic nature of the credit market further complicates risk assessment, as rapid economic changes may outpace the bank’s risk management updates. In addition, insufficient training and expertise among staff in using advanced risk management tools can result in suboptimal decision-making, thereby affecting the overall effectiveness of these enhancements.
This study aims to identify the key factors that limit the performance of enhanced risk management processes at FCMB and assess their impact on non-performing loans. By examining technological, operational, and human resource challenges, the research seeks to propose actionable strategies to further optimize credit risk management and reduce the incidence of NPLs.
Objectives of the Study
- To evaluate the impact of risk management process enhancements on reducing non-performing loans at FCMB.
- To identify integration and data quality challenges affecting risk management.
- To recommend strategies for improving risk assessment and credit portfolio quality.
Research Questions
- How do enhanced risk management processes affect non-performing loans at FCMB?
- What challenges impede effective integration and data utilization in risk management?
- Which strategies can optimize risk management and reduce credit defaults?
Research Hypotheses
- H₁: Enhanced risk management processes significantly reduce non-performing loans.
- H₂: Integration issues with legacy systems negatively impact risk assessment accuracy.
- H₃: Improved staff training and data quality management enhance credit portfolio performance.
Scope and Limitations of the Study
This study focuses on FCMB’s risk management practices over the past three years, using credit performance data, risk reports, and interviews with risk management professionals. Limitations include economic fluctuations and proprietary data constraints.
Definitions of Terms
- Risk Management Process Enhancements: Improvements made to the systems and procedures for evaluating and mitigating credit risk.
- Non-performing Loans (NPLs): Loans on which borrowers are not making interest or principal repayments.
- Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to forecast future outcomes.
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