Machine learning-based variable selection for clustered credit risk modeling

Abstract Several studies have demonstrated the high prediction accuracy of clustered credit risk modeling. In clustered modeling, borrowers are segmented based on their similarities through cluster analysis, and a separate predictive model is developed for each cluster, resulting in increased predictive accuracy. Unambiguously, its effectiveness depends on the quality of the segmentation, which in turn depends primarily on the choice of variables used in the cluster analysis. However, appropriate variable selection for clustering is a major challenge, particularly for high-dimensional data. In the present study, we propose a machine learning-based variable selection method based on theoretical and regulatory considerations. Formally, the most influential risk drivers from a best-in-class machine learning model are identified using Shapley values and employed as clustering variables. Thus, the information of the explanatory variables crucial for the prediction of the dependent variable is already processed during data segmentation, making each individual predictive model more effective. Through a comparative analysis using two real-world credit default datasets, we show that our proposed approach to clustered modeling leads to the highest prediction accuracy among various clustering models.