Explainable Machine Learning Framework for Predicting Auto Loan Defaults

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection methods, including SHapley Additive exPlanations (SHAP) values and Mutual Information (MI), and resampling techniques such as Synthetic Minority Over-sampling TEchnique (SMOTE), SMOTE-Tomek, and SMOTE Edited Nearest Neighbor (SMOTE-ENN), are evaluated. The results show that combining SHAP-based feature selection with SMOTE-Tomek resampling and a Stacked Classifier consistently achieves superior predictive performance. These findings highlight the value of explainable AI in enhancing credit risk assessment for auto lending. This research also offers valuable insights for addressing other financial modeling challenges involving imbalanced datasets, supporting more informed and reliable decision-making.