Machine Learning-Based Loan Approval Prediction with SHAP Interpretability Analysis

Abstract

Loan approval prediction is central to financial risk management, where lenders need models that are both accurate and interpretable. We compared five machine learning classifiers on a loan approval dataset: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine. The original 45,000-sample dataset was reduced to 20,000 for training due to computational constraints. We applied SHAP TreeExplainer to interpret the best-performing model. XGBoost achieved the highest AUC (0.9747) and accuracy (0.931). SHAP identified previous loan status, personal income, loan percentage, and loan interest rate as the top four features by importance. The analysis also traces how each feature shifts individual predictions toward approval or rejection. These findings give practitioners evidence for model selection in loan approval settings and produce explanations that meet regulatory transparency requirements.