Enhancing Credit Risk Prediction through an Ensemble of Explainable Model

Machine learning has been widely used in the field of credit scoring due to their excellent predictive performance, but opacity hinders the further application of more accurate complex machine learning models. We propose a credit score explainable framework that integrates multiple models, uses the XGBoost algorithm to predict credit scores, and then uses multiple explanation algorithms and K-means to enhance the accuracy and explainability of credit scores. The paper uses data sets from public websites to test model performance. The results show that the credit scoring model can simultaneously achieve the two goals of accurate prediction and stable explanation, making the credit scoring process easy to understand.