An investigation of risk assessment models in business banking: A case study of Sterling Bank, Kano
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Background of the Study
Risk assessment models are fundamental to ensuring the stability and profitability of business banking operations. Sterling Bank in Kano has adopted various quantitative and qualitative risk assessment models to evaluate creditworthiness, market risks, and operational vulnerabilities. These models incorporate advanced statistical techniques, predictive analytics, and scenario analysis to provide a comprehensive view of potential risks (Afolabi, 2023). The application of these models facilitates proactive risk management, enabling the bank to identify early warning signs and implement corrective measures before risks materialize into significant losses. By integrating risk assessment models into its decision-making processes, Sterling Bank enhances its ability to manage non-performing loans and adjust credit policies in response to dynamic market conditions (Oluwaseun, 2024).
The bank’s risk management framework is supported by continuous monitoring and regular updating of its assessment models to reflect current economic trends and regulatory requirements. This adaptive approach ensures that risk evaluations remain relevant and accurate, which is critical in the volatile business banking environment. However, challenges such as data quality issues, model complexity, and the need for skilled personnel to interpret the results can impede the effectiveness of these models. Furthermore, integrating risk assessment tools with legacy systems poses additional challenges that may affect the timeliness and accuracy of risk predictions (Chin, 2025). This study investigates the risk assessment models employed by Sterling Bank, examining their strengths, limitations, and overall impact on risk management practices in business banking.
Statement of the Problem
Despite the implementation of advanced risk assessment models, Sterling Bank faces several challenges in effectively managing business banking risks. One significant issue is the reliance on historical data that may not accurately capture emerging risks in a rapidly changing market environment (Afolabi, 2023). In addition, the complexity of risk models and their integration with existing legacy systems often result in data inconsistencies and delayed risk identification. The shortage of skilled personnel capable of effectively operating and interpreting sophisticated risk models further hampers the bank’s risk management efforts (Oluwaseun, 2024). Moreover, frequent changes in regulatory requirements necessitate continuous modifications to risk models, which can lead to instability and uncertainty in risk assessments (Chin, 2025). These challenges ultimately affect the bank’s ability to make timely and informed decisions, increasing the likelihood of non-performing loans and financial losses. This study aims to explore these issues comprehensively and suggest improvements to enhance the effectiveness of risk assessment models in business banking.
Objectives of the Study
- To evaluate the current risk assessment models used by Sterling Bank.
- To identify challenges related to data quality, model complexity, and integration with legacy systems.
- To propose recommendations for improving the effectiveness of risk assessment practices.
Research Questions
- What risk assessment models are currently used by Sterling Bank?
- What challenges affect the accuracy and timeliness of risk evaluations?
- How can the risk assessment process be improved to enhance risk management outcomes?
Research Hypotheses
- H₁: Advanced risk assessment models significantly reduce non-performing loans.
- H₂: Integration issues with legacy systems negatively impact the effectiveness of risk models.
- H₃: Continuous staff training and model updates improve risk management efficiency.
Scope and Limitations of the Study
This study focuses on Sterling Bank’s business banking division in Kano, reviewing risk assessment practices over recent fiscal periods. Limitations include data access constraints and the dynamic nature of financial risks.
Definitions of Terms
- Risk Assessment Models: Quantitative and qualitative tools used to evaluate potential risks.
- Business Banking: Banking services provided to commercial and corporate clients.
- Non-performing Loans: Loans that are in default or close to default.
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