Artificial Intelligence and Machine Learning for Credit Risk Assessment: Improving Loan Default Prediction and Lending Efficiency
0 viewsAbstract
In this study, I investigate whether machine learning models can outperform traditional credit-scoring methods in predicting loan default and improving lending efficiency for retail banks. Conventional credit scoring relies on a small set of static variables, fixed scorecards, and periodic manual review, which tends to under-price risk for borrowers with thin credit files and over-reject creditworthy applicants. To test whether a machine learning approach can address these limitations, I ran two parallel loan-underwriting pipelines over a six-month period at a mid-sized retail lender: a traditional logistic-regression scorecard and a gradient-boosted machine learning model trained on transactional, behavioral, and bureau data. I evaluated five performance indicators (Nasr et al., 2022) — default prediction accuracy, average approval time, non-performing loan rate, false positive rate, and cost per loan underwritten — across approximately 14,000 loan applications. The results show a substantial improvement under the AI-driven model: prediction accuracy rose by 18 percentage points, approval time fell by 83%, the non-performing loan rate dropped by 52%, the false positive rate fell by 59%, and underwriting cost per loan declined by 47%. These findings suggest that machine learning can materially improve both risk management and financial inclusion by approving more creditworthy borrowers at lower cost. However, model explainability, fair-lending compliance, and data-quality dependence remain significant barriers to widespread adoption.
Keywords
Artificial Intelligence (AI), Credit Risk, Financial Inclusion, Loan Default Prediction, Machine Learning, Retail Banking