A hybrid interpretable credit card users default prediction model based on RIPPER

摘要

With the vigorous development of the financial sector, financial risks are showing a tendencytoward diversification, particularly regarding the customer credit risk of commercial banks.Therefore, the customer's credit risk is being considered by financial institutions, and a creditevaluatingmodel has emerged as a result. Currently, research has concentrated on enhancing theprecision of the model, ignoring the interpretability,which makes it difficult to apply in the industry.Compared to precision, studies related to the interpretable model are limited. In our previouswork, we did not consider model operation time and stability. Therefore, this study proposes ahybrid model based on the RIPPER algorithm. First, according to the characteristics of credit carddata sets, targeted special data pretreatment methods are proposed. Next, the RELIEF methodfor feature selection removes the redundant features and further improves the interpretability ofthemodel. Then, to address the problem of the imbalanced distribution of credit card data sets, asynthetic minority class sampling algorithm is used to equalize the samples. Finally, default creditcard users are predicted by taking advantage of the rules generated by the RIPPER algorithm.To test the performance of the model, we used Taiwanese credit card customer data for empiricalresearch.We considered model accuracy and interpretability when comparing the proposedSPR-RIPPERmodel with the existingmainstreammodels. The results of the experiments indicatethat the proposed model achieves acceptable results. This study demonstrates that the proposedcredit card user default predictionmodel, SPR-RIPPER, has practical application value.