An Explainable Hybrid Learning Framework for Cost-Sensitive Credit-Risk Modeling

Credit-risk prediction must balance the detection of adverse outcomes with the need to inspect how model predictions are formed. This study presents an AutoML-driven hybrid framework combining a supervised xDNN-inspired representation, traceable class-conditional prototypes, a cost-sensitive Random Forest, and out-of-fold stacking. The framework was evaluated on the Taiwan Default of Credit Card Clients and Give Me Some Credit datasets using three randomly seeded train–validation–test partitions per dataset. Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, a multilayer perceptron, and FT-Transformer served as comparative models. Across the three data splits, the proposed hybrid achieved a mean ROC–AUC of 0.7839, a mean average precision of 0.5549, and a mean Brier score of 0.1623 on Taiwan. On Give Me Some Credit, the corresponding mean values were 0.8625, 0.4032, and 0.0491, respectively. At validation-selected F2 thresholds, mean recall was 86.81% and 63.39%, respectively, with corresponding mean F2-scores of 0.6366 and 0.5168. Performance remained close to strong baselines, with substantial false-positive burdens at the selected operating points. TreeSHAP described how the final forest used original and xDNN-derived inputs, while stored prototypes supplied traceable development-data examples. The framework integrates sensitivity-oriented prediction and complementary feature- and example-based evidence within a leakage-controlled workflow.