A shapelet-based behavioral pattern extraction method for credit risk classification with behavior sparsity

Abstract

In this paper, a shapelet-based behavioral pattern extraction method (Behavior2Shapelets) is proposed to address the behavior-sparsity issue in credit risk classification. In the proposed method, three steps are involved. First, by introducing the shapelets module, the proposed model can learn the behavioral patterns from the sparse behavioral features. Then, these extracted patterns are combined with a logistic regression model to construct an interpretable classifier. Finally, to enhance the robustness of the model, a dynamic strategy for threshold determination is used, which is based on the Kolmogorov-Smirnov statistic. The performance of the Behavior2Shapelets model is validated by using the Taiwan credit dataset and its seven derivative datasets. The empirical results demonstrate that the Behavior2Shapelets model outperforms the baseline methods in terms of classification accuracy and robustness. This highlights the feasibility of the proposed model as a solution to the behavior-sparsity problem in credit risk classification.