Predictive Analytics for Loan Default Risk Assessment in Commercial Banks
TL;DR
It is concluded that investing in predictive credit risk analytics is highly viable, providing commercial banks with enhanced asset quality, lower provisioning overhead, and improved capital adequacy.
Abstract
This study, titled "Predictive Analytics for Loan Default Risk Assessment in Commercial Banks," evaluates the predictive accuracy, loan portfolio risk composition, Non-Performing Asset (NPA) reduction trends, and financial feasibility of advanced machine learning risk scoring engines in commercial banking. Commercial banks face significant credit risk exposure, with unsecured personal loans representing 40% and MSME business loans accounting for 30% of portfolio volume. A five-year project lifecycle (2021-2025) of an ensemble predictive analytics platform is evaluated using standard capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that deploying ensemble neural network classifiers raises default risk ROC-AUC scores to 0.96 compared to 0.72 under traditional logistic regression models. Higher predictive precision improves default detection accuracy from 68.5% to 96.2%, reducing annual credit loss costs from 480 Crores to 65 Crores and lowering Gross NPAs to 320 Crores while achieving an 89.5% Provision Coverage Ratio (PCR) by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in predictive credit risk analytics is highly viable, providing commercial banks with enhanced asset quality, lower provisioning overhead, and improved capital adequacy.