An appraisal of risk management system integration on loan performance in banking: a case study of First City Monument Bank
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Background of the Study
Risk management is integral to the banking industry, particularly in managing loan portfolios and ensuring asset quality. First City Monument Bank (FCMB) has undertaken the integration of advanced risk management systems to improve loan performance by enhancing the accuracy of credit risk assessments and streamlining monitoring processes (Akinola, 2023). This integration combines predictive analytics, real-time monitoring, and comprehensive data management tools to provide a holistic view of borrower risk. The underlying premise is that better risk management leads to lower default rates and improved overall loan performance. FCMB’s initiative reflects broader trends in financial technology where integration of risk systems into daily operations is seen as a critical factor in achieving operational excellence and financial stability (Oluwaseun, 2024).
By integrating various risk management tools, FCMB aims to identify potential defaults early, adjust credit exposure accordingly, and improve decision-making regarding loan approvals. This integration not only enhances the accuracy of risk assessments but also allows for more dynamic and responsive credit management. Empirical studies suggest that banks that effectively integrate risk management systems tend to have more stable loan portfolios and better overall financial performance (Ijeoma, 2025). Despite these advancements, challenges remain in fully integrating new systems with legacy infrastructures and ensuring that staff are adequately trained to interpret the data. This study examines the impact of risk management system integration on loan performance at FCMB, offering insights into how technological and human factors converge to improve credit outcomes.
Statement of the Problem
Despite the implementation of an integrated risk management system at FCMB, the bank continues to face challenges in optimizing loan performance. There is evidence that gaps in data integration and inconsistencies in risk modeling have led to occasional misclassifications of borrower risk (Babatunde, 2023). These inaccuracies result in either overly conservative lending practices or excessive risk exposure, both of which can negatively impact loan performance. Furthermore, the integration process has been hampered by legacy system constraints and a lack of comprehensive training for staff, which limits the effective use of advanced risk tools. The disconnect between theoretical risk management benefits and practical outcomes necessitates a deeper investigation into the operational and technological barriers that prevent full system integration. This study aims to identify these barriers and assess how they affect loan portfolio quality, with the goal of providing actionable recommendations for improving risk management practices at FCMB (Emeka, 2024).
Objectives of the Study
To evaluate the impact of risk management system integration on loan performance at FCMB.
To identify the technological and operational challenges affecting system integration.
To propose strategies for enhancing risk management practices to improve loan outcomes.
Research Questions
How does the integration of risk management systems affect loan performance at FCMB?
What are the main challenges in integrating new risk management tools with existing systems?
How can FCMB improve system integration to enhance loan portfolio quality?
Research Hypotheses
Integrated risk management systems are positively correlated with improved loan performance.
Inadequate system integration negatively impacts the accuracy of credit risk assessments.
Enhanced training and process optimization lead to better risk management outcomes.
Scope and Limitations of the Study
This study focuses on FCMB’s risk management system integration over the past three years. Limitations include data accessibility issues and external economic variables affecting loan performance.
Definitions of Terms
• Risk Management System Integration: The process of combining various risk assessment and monitoring tools into a unified framework.
• Loan Performance: The overall quality and default rate of a bank’s loan portfolio.
• Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to forecast future events.
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