Credit Risk Assessment of Internet Financial Platforms Based on BP Neural Network

Abstract

Aiming at the problem of credit risk, this paper selects key data indicators to establish an index system combining with the factors affecting the credit risk of the platform. Python crawler software was used to obtain relevant data of net lending platforms, and the crawled data of more than 1000 platforms were preprocessed. Ninety-five platforms with complete data were selected to build a BP neural network risk assessment model. The BP neural network model is used to make an empirical analysis of the risks of online lending platforms by using the data obtained, and the evaluation method of this paper is compared with the rating method of online lending sky eye. The empirical results show that the error of BP neural network can be stable at about 0.5, and the accuracy rate of evaluation is as high as 95.45%, which is much higher than the accuracy rate of 44.21% of net loan platform. This paper provides decision support for the credit risk early warning of net loan platform.