A Two-Stage Interpretable Model to Explain Classifier in Credit Risk Prediction

In the financial sector, credit risk represents a critical issue, and accurate prediction is essential for mitigating financial risk and ensuring economic stability. Although artificial intelligence methods can achieve satisfactory accuracy, explaining their predictive results poses a significant challenge, thereby prompting research on interpretability. Current research primarily focuses on individual interpretability methods and seldom investigates the combined application of multiple approaches. To address the limitations of existing research, this study proposes a two-stage interpretability model that integrates SHAP and counterfactual explanations. In the first stage, SHAP is employed to analyze feature importance, categorizing features into subsets according to their positive or negative impact on predicted outcomes. In the second stage, a genetic algorithm generates counterfactual explanations by considering feature importance and applying perturbations in various directions based on predefined subsets, thereby accurately identifying counterfactual samples that can modify predicted outcomes. We conducted experiments on the German credit datasets, HMEQ datasets, and the Taiwan Default of Credit Card Clients dataset using SVM, XGB, MLP, and LSTM as base classifiers, respectively. The experimental results indicate that the frequency of feature changes in the counterfactual explanations generated closely aligns with the feature importance derived from the SHAP method. Under the evaluation metrics of effectiveness and sparsity, the performance demonstrates improvements over both basic counterfactual explanation methods and prototype-based counterfactuals. Furthermore, this study offers recommendations based on features derived from SHAP analysis results and counterfactual explanations to reduce the risk of classification as a default.