Class imbalance Bayesian model averaging for consumer loan default prediction: The role of soft credit information

This study investigates the predictive value of soft information for consumer loan defaults. We propose a novel framework to address class imbalance by utilizing the concept of Bayesian model averaging. Specifically, we assign unequal weights to machine learning sub-models that incorporate different combinations of variables, thereby creating an accurate and robust model for predicting consumer loan defaults. Additionally, this framework incorporates the Shapley additive explanations (SHAP) method to estimate individual contributions and employs the Bayesian information criterion to assess the variable contributions of the sub-models. We validate the effectiveness and robustness of our proposed method using authentic loan data and publicly available credit default records from a prominent consumer platform in China. Our empirical research suggests that the characteristics of user online behavior are significantly predictive of loan defaults, demonstrating asymmetry at different stages of default. • Class imbalance challenge addressed in consumer loan default prediction by leveraging the bayesian model averaging. • SHAP method is incorporated to elaborate on the predictions made by the submodels. • A novel variable contributions calculating method by integrating the variables and weights of the submodels is proposed. • This paper defines users' soft credit information through actual data and verifies their role in default prediction. • Compared to short-term predictions, soft credit information play a greater role in long-term default prediction.