The effect of integrated risk management systems 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 systems have become fundamental to modern banking, offering comprehensive frameworks to assess, monitor, and mitigate credit risks. First City Monument Bank (FCMB) has implemented sophisticated risk management systems that incorporate predictive analytics, real-time monitoring, and automated credit scoring models to reduce non-performing loans (NPLs). These systems enable FCMB to identify high-risk lending portfolios early and take corrective measures to prevent loan defaults (Okeke, 2023). By consolidating various risk metrics into a unified system, the bank is better equipped to manage credit risk holistically.
The evolution of these integrated systems reflects a paradigm shift in risk management from reactive approaches to proactive and data-driven strategies. Advanced risk management tools leverage big data analytics and machine learning algorithms to forecast borrower behavior and market fluctuations, thereby facilitating timely interventions. FCMB’s adoption of such technologies has not only improved loan quality but has also contributed to enhanced operational efficiency and increased investor confidence (Adebayo, 2024). Furthermore, regulatory reforms in Nigeria have underscored the importance of effective risk management, pushing banks to adopt integrated systems that ensure compliance and protect against systemic risks.
However, despite these advancements, challenges remain in fully realizing the benefits of integrated risk management. Discrepancies in data quality, integration issues with legacy systems, and the need for continuous model recalibration can undermine the effectiveness of these systems (Chukwu, 2023). This study aims to evaluate how integrated risk management systems have impacted the reduction of NPLs at FCMB, analyzing both the technical and operational dimensions that contribute to improved loan performance and overall financial stability.
Statement of the Problem
While FCMB has made significant progress in reducing non-performing loans through integrated risk management systems, persistent challenges still limit their full potential. One critical issue is the difficulty in obtaining consistent and accurate data from diverse sources, which is essential for the effective functioning of predictive models (Ibrahim, 2024). The integration of new risk management tools with existing legacy platforms often leads to data mismatches and delays in risk assessment, resulting in delayed responses to emerging credit risks.
Additionally, the rapid evolution of market conditions necessitates frequent recalibration of risk models, a process that can be both time-consuming and resource-intensive. This dynamic environment can lead to temporary lapses in risk coverage, increasing the likelihood of NPLs. Furthermore, inadequate staff training on the utilization and interpretation of advanced risk analytics further exacerbates these issues, potentially compromising the decision-making process. These challenges, combined with external economic pressures such as market volatility and fluctuating interest rates, create a complex risk landscape that FCMB must navigate.
The study seeks to identify the key factors that hinder the optimal performance of integrated risk management systems in reducing NPLs at FCMB. By analyzing both technological and human factors, the research aims to propose actionable strategies that can enhance the effectiveness of risk management practices and further lower the incidence of non-performing loans.
Objectives of the Study
- To evaluate the impact of integrated risk management systems on reducing non-performing loans at FCMB.
- To identify challenges in data integration and model calibration affecting risk assessment.
- To recommend strategies for optimizing risk management systems to further reduce NPLs.
Research Questions
- How do integrated risk management systems influence the reduction of non-performing loans at FCMB?
- What are the main challenges in integrating new risk management tools with legacy systems?
- Which strategies can enhance the effectiveness of these systems in reducing credit risk?
Research Hypotheses
- H₁: Integrated risk management systems significantly reduce non-performing loans.
- H₂: Data integration challenges negatively affect the accuracy of risk assessments.
- H₃: Continuous model recalibration and staff training improve risk management outcomes.
Scope and Limitations of the Study
This study focuses on FCMB’s risk management practices over the past three years, using internal loan performance data, system integration reports, and interviews with risk management personnel. Limitations include data quality issues and the external impact of macroeconomic factors.
Definitions of Terms
- Integrated Risk Management Systems: Comprehensive frameworks that combine various risk assessment tools and techniques.
- Non-performing Loans (NPLs): Loans in which borrowers are not making the required payments.
- Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
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