Explainable AI-Based Credit Default Risk Modeling on Multi-Table Data
This study addresses the prediction of credit default risk using the Home Credit Default Risk dataset. Due to the approximately eight percent imbalanced class distribution in the data structure, an intensive feature engineering pipeline that reduces multiple tables to the customer level was applied before modeling. The implementation was designed to include behavioral summaries generated from external credit history, previous applications, installment behavior, credit card transactions, and POS cash records. Missing value handling, encoding, and multicollinearity reduction steps were performed on the obtained features. LightGBM was selected as the classification model, and the hyperparameters were optimized with Optuna under cross validation. In the best configuration, the mean AUC value was observed as 0.78682 and the mean PR AUC value as 0.28015. In order for the probabilities to be used more reliably in decision making processes, Platt scaling and SHAP based explainability analysis were applied.