An assessment of integrated risk management practices on reducing non-performing loans in banking: a case study of First City Monument Bank
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Background of the Study
Integrated risk management (IRM) practices have emerged as a crucial tool for banks to manage credit risk and minimize non-performing loans (NPLs). First City Monument Bank (FCMB) has implemented a comprehensive IRM framework that consolidates various risk assessment and monitoring processes into a unified system. By employing real-time data analytics and predictive modeling, FCMB can identify early warning signs of credit deterioration and intervene proactively (Ibrahim, 2023; Oluwaseun, 2023). The integrated approach enables the bank to align its lending practices with market conditions and borrower profiles, thereby reducing the incidence of loan defaults. This proactive risk management strategy not only safeguards the bank’s assets but also enhances overall portfolio quality and financial stability.
The IRM framework incorporates continuous risk monitoring, stress testing, and regular internal audits to ensure that all potential risks are managed effectively. Enhanced risk reporting and communication channels facilitate better decision-making at the senior management level, allowing for timely adjustments in lending strategies. As regulatory bodies increasingly emphasize the need for robust risk management practices, FCMB’s integrated system also ensures compliance with evolving standards. This holistic approach to risk management is critical in an environment characterized by economic volatility and uncertain market conditions, where proactive risk mitigation can significantly reduce the burden of NPLs.
Statement of the Problem
Despite the adoption of integrated risk management practices, FCMB still faces challenges in reducing its non-performing loans. One major issue is the timeliness and quality of risk data, which are critical for accurate predictive modeling. Incomplete or delayed information can lead to ineffective risk assessments, resulting in missed opportunities for early intervention (Oluwaseun, 2023). Moreover, the integration of modern IRM tools with legacy systems has proven difficult, often resulting in data discrepancies and communication gaps between departments. Organizational resistance to change and inadequate staff training further undermine the full potential of the IRM framework. Additionally, external economic shocks and market volatility continue to exert pressure on the bank’s loan portfolio, complicating efforts to achieve a significant reduction in NPLs. The absence of standardized metrics to evaluate the success of the integrated system further hinders efforts to identify and address areas requiring improvement.
Objectives of the Study
1. To evaluate the effectiveness of integrated risk management practices in reducing non-performing loans at FCMB.
2. To identify data quality and system integration challenges within the IRM framework.
3. To recommend strategies for optimizing risk management practices to lower NPLs.
Research Questions
1. How effective are integrated risk management practices in reducing non-performing loans at FCMB?
2. What challenges affect the quality and integration of risk data in the IRM framework?
3. How can risk management practices be optimized to better predict and mitigate loan defaults?
Research Hypotheses
1. H₀: Integrated risk management practices do not significantly reduce non-performing loans at FCMB.
2. H₀: Data quality and integration challenges do not significantly affect the predictive accuracy of the IRM system.
3. H₀: Optimization strategies do not significantly enhance the effectiveness of IRM practices in reducing NPLs.
Scope and Limitations of the Study
This study focuses on FCMB’s risk management operations, using internal loan performance data, risk assessment reports, and interviews with risk managers. Limitations include potential inconsistencies in data collection and the influence of external economic conditions.
Definitions of Terms
• Integrated Risk Management (IRM) Practices: A unified approach to identifying, assessing, and mitigating risks across the organization.
• Non-Performing Loans (NPLs): Loans on which the borrower is not making scheduled payments.
• Predictive Modeling: The use of statistical techniques to forecast future outcomes based on historical data.
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