Background of the Study
Credit underwriting policies are critical in managing risk and ensuring profitability in corporate banking. Sterling Bank in Kano has developed stringent credit underwriting policies that govern the evaluation and approval of loan applications. These policies incorporate both quantitative models and qualitative assessments to determine the creditworthiness of corporate borrowers (Okechukwu, 2023). Effective underwriting not only minimizes the risk of non-performing loans but also enhances the bank’s competitive positioning by enabling more accurate pricing of credit risk. By continuously refining its underwriting criteria in response to market conditions and regulatory changes, Sterling Bank aims to balance risk and reward, thereby driving improved financial performance (Adenola, 2024). However, challenges such as inconsistent application of policies, integration issues with digital systems, and evolving borrower profiles can impede the effectiveness of credit underwriting, ultimately impacting corporate banking performance (Chukwu, 2025).
Statement of the Problem
Sterling Bank faces significant challenges in implementing consistent and effective credit underwriting policies. The complexity of evaluating corporate credit risk, coupled with data integration issues between traditional methods and new digital tools, often leads to discrepancies in loan approvals. Inadequate training for credit officers and rapid changes in borrower risk profiles further complicate the underwriting process, increasing the incidence of non-performing loans. These challenges negatively affect the bank’s profitability and market competitiveness. This study seeks to explore these issues and propose strategies to enhance the effectiveness of credit underwriting policies in corporate banking.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
The study focuses on Sterling Bank’s corporate banking division in Kano, analyzing credit underwriting practices over recent fiscal periods. Limitations include potential data access issues and market volatility.
Definitions of Terms
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