Imbalanced data credit scoring model based on Group-Lasso method

doi:10.7523/j.issn.2095-6134.2021.02.004

Abstract

Abstract: In view of the complexity of the customers' credit risk faced by commercial banks at the present, how to manage customers' credit risk is very important. Customers' credit risk modeling is a key step. We use the credit card data of a commercial bank to construct a credit scoring model and predict the default probability. We construct a credit scoring model on the basis of Logistic regression, using the group-Lasso (AUC criterion) method to select variables and using the ROSE (random over sampling examples) method to deal with the unbalanced categories. The results are compared and analyzed, and the new model constructed in this work has certain advantages in discriminating ability and predictive ability. It can play a guiding role for banks and other financial institutions in evaluating customer credit risk and can be used as an effective basis for customer credit evaluation decision. In practice, it also has good operability.

Key words: credit scoring, Logistic regression, Group-Lasso method, ROSE

CLC Number:

F832.479

WEI Yongfeng, XIANG Yibo. Imbalanced data credit scoring model based on Group-Lasso method[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(2): 181-188.

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