Developing Consumer Bank Loan Delinquency Prediction Model Using XAI

[Purpose] This study aims to create a loan default prediction model using machine learning, a core concept of AI technology, and explainable AI (XAI) techniques, and to compare and evaluate it against highly interpretable models such as logistic regression. [Methodology] This study utilizes the data of household loan customers of Local Bank A in South Korea from December 2020 to June 2022 to generate and compare the logistic regression model and the XGBoost model. We used SHAP, one of the XAI techniques, to explain the XGBoost model. [Findings] The XGBoost model shows better prediction performance than the logistic regression model. By applying SHAP to the XGBoost model, we identify key predictors： credit rating, average bank deposit balance over the past six months, card loan risk rating, K rating agency’s multiple debt risk index, average core deposit balance over the past six months, and the current interest rate of the loan account. [Implications] The machine learning model (XGBoost) combined with XAI shows better prediction performance than the logistic regression model, and the key predictors identified by the two models are similar. Therefore, the combined model could serve as an alternative to the logistic regression model commonly used in business research.