Application and optimization of deep learning in the credit score of auto finance

Abstract

Abstract In this paper, a credit scoring integration model incorporating BRNN is used to study the credit scoring problem in automobile finance. Aiming at the problems of existing credit scoring models constructed with shallow architecture and the unidirectional limitation of RNN itself, this paper introduces a BRNN model that superimposes RNN models in two directions. The potential relationship between each credit feature is mined through logistic regression, extreme gradient boosting tree, and bidirectional recurrent neural network algorithms, and the final prediction output is linked to the customer’s overall credit to improve the prediction accuracy. In this paper, we study the application of a credit scoring model based on the improved BRNN model for an auto finance company. Data preprocessing techniques and feature screening methods are used to improve the BRNN model and construct the credit scoring model for auto finance at Company A. The BRNN model is the basis for Company A’s credit scoring model. Based on the comparison with other models, it is concluded that the automobile finance credit scoring IBRNN model constructed based on the improved BRNN model in this paper has an accuracy of 89.6% in classifying the user finance data of Company A on different datasets, which is a significant improvement compared with the other five models.