This study critically examines credit evaluation practices in loan and advance disbursement, focusing on their effectiveness, challenges, and strategic implications for financial institutions. Against the backdrop of evolving regulatory norms and technological advancements, the research highlights disparities between traditional and modern credit appraisal frameworks. Institutions leveraging data-driven tools, such as AI and behavioral analytics, demonstrate superior risk assessment accuracy and lower non-performing assets (NPAs) compared to those reliant on conventional collateral-based models. Key challenges include information asymmetry, sector-specific risks, and financial exclusion due to rigid appraisal criteria. The study underscores the need for inclusive, tech-enabled evaluation systems incorporating alternative data to enhance credit access while mitigating risks. Strategic recommendations emphasize regulatory alignment, officer training, and real-time monitoring to strengthen institutional resilience. The findings offer actionable insights for lenders, policymakers, and researchers aiming to optimize credit risk management and foster equitable financial inclusion. Keywords: Credit evaluation, loan disbursement, risk assessment, non-performing assets (NPAs), financial inclusion, predictive analytics.
K et al. (Tue,) studied this question.