Research on Credit Scoring Model Based on Transfer Learning
Bin Zhu · 2015
Customer credit scoring is an important part of daily business activities for financial companies such as banks. Default customers usually makae up the minority of the population while customers of timely repayment make up the majority,which is called a class imbalance problem in the study of customer credit scoring. Existing methods in credit scoring cannot effectively solve the issue of class imbalance caused by absolute scarcity of the minority class. In our study,we introduce the technique of transfer learning to integrate the external information and try to solve the issue of class imbalance caused by absolute scarcity of the minority class. In order to exploit the minority sample outside the system more effectively,a transfer learning model is proposed,which is based on the ensemble transfer learning technology transfer bagging. A two-stage sampling method and the technique of group method of data handling are used in the new model to improve the generation and integration strategy of base models. The empirical results on the credit card dataset from a commercial bank show that the new model can deal with the issue of class imbalance caused by absolute scarcity better in comparison with other commonly used methods in credit scoring and provide a better prediction of the credit status of default customers.