The Credit Risk Playbook: AI-Powered Loan Default Prediction

A write-up on my endevour into pairing machine learning with finance

Loan default prediction is a critical challenge for financial institutions, as missed defaults lead to financial losses while false alarms increase operational costs. This study applies machine learning techniques to loan classification, comparing Logistic Regression, K-Nearest Neighbors, Random Forest, and Gradient Boosting across multiple thresholds to evaluate trade-offs between risk mitigation and efficiency. A structured evaluation framework incorporating FNR, FPR, Recall, Precision, Accuracy, F1-Score, and AUC-ROC ensured a comprehensive assessment of model performance. Results show that Gradient Boosting minimizes financial losses by achieving the highest Recall and lowest False Negative Rate (FNR), while Random Forest optimizes operational efficiency through high Precision and Accuracy. Financial impact analysis suggests that Gradient Boosting could reduce default-related losses by 10%, while Random Forest could cut manual reviews by 40%, saving labor hours. These findings offer actionable insights for lenders, guiding threshold selection, policy adjustments, and automation strategies. However, dataset limitations and high FNR at certain thresholds highlight the need for further refinements. Future work should explore time-series data, cost-sensitive learning, and ensemble modeling to enhance predictive accuracy and real-time decision-making.

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