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An examination of integrated risk management solutions 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) solutions are essential for managing credit risk and reducing non-performing loans (NPLs) in the banking sector. First City Monument Bank (FCMB) has implemented a comprehensive IRM framework that consolidates risk assessment, monitoring, and mitigation processes into a single, unified system. By utilizing real-time data analytics and predictive modeling, FCMB can identify early warning signs of potential loan defaults and intervene proactively (Ibrahim, 2023; Oluwaseun, 2023). This integrated approach not only enhances the accuracy of risk assessments but also facilitates timely corrective measures, thereby reducing the accumulation of NPLs and improving overall asset quality.

The IRM framework at FCMB incorporates advanced technology that monitors various risk parameters, including borrower credit history, market conditions, and macroeconomic indicators. This holistic view of risk enables the bank to tailor its lending practices and adjust its portfolio to mitigate potential losses. Furthermore, integrated risk management supports regulatory compliance by providing detailed risk reports and audit trails, which are critical in maintaining stakeholder confidence and ensuring the bank’s long-term financial stability. As economic uncertainties continue to challenge the banking sector, FCMB’s IRM solutions serve as a vital tool in managing credit risk and safeguarding financial performance.

Statement of the Problem

Despite the implementation of an integrated risk management framework, First City Monument Bank continues to face challenges in significantly reducing non-performing loans. One major issue is the quality and timeliness of data inputs, which are crucial for accurate risk assessment. Delays or inaccuracies in data can lead to ineffective risk predictions and missed opportunities for early intervention (Oluwaseun, 2023). Additionally, the integration of the IRM system with legacy risk management practices has not been seamless, resulting in gaps in data synchronization and inconsistencies in reporting. Organizational resistance and insufficient training further hinder the effective adoption of new risk management protocols, exacerbating the issue. External factors, such as market volatility and economic downturns, also contribute to the persistence of NPLs. The lack of standardized performance metrics to evaluate the IRM framework further complicates efforts to measure its success, making it challenging for management to identify areas for improvement.

Objectives of the Study

1. To evaluate the effectiveness of integrated risk management solutions in reducing non-performing loans at FCMB.

2. To identify data quality and 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 solutions in reducing non-performing loans at FCMB?

2. What challenges impact the quality and integration of risk data within the IRM framework?

3. How can IRM practices be optimized to better predict and mitigate loan defaults?

Research Hypotheses

1. H₀: Integrated risk management solutions 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 practices, utilizing internal loan performance data, risk reports, and interviews with risk management personnel. Limitations include data inconsistencies and the influence of external economic factors.

Definitions of Terms

• Integrated Risk Management (IRM) Solutions: A comprehensive framework that consolidates risk management processes into a unified system.

• 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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