Chinese Journal of Network and Information Security ›› 2017, Vol. 3 ›› Issue (12): 15-21.doi: 10.11959/j.issn.2096-109x.2017.00222

• Papers •     Next Articles

Credit risk identification of high-risk online lending enterprises based on neural network model

Mao-guang WANG,Zi-jun ZHU()   

  1. School of Information,Central University of Finance and Economics,Beijing100081,China
  • Revised:2017-12-01 Online:2017-12-01 Published:2018-01-12
  • Supported by:
    Cooperation Project with Network Finance Center(020676116004);Cooperation Project with Peking University(020676114004)


The rapid development of online lending alleviates the difficulty of financing for small and micro enterprises to a certain extent,but it also exposes the credit risk identification problem of online lending platform.In order to fully identify the characteristics of high-risk network lending enterprises,small and medium-sized network lending companies were selected as samples,and indicators that were highly correlated with risk identification were chosen as indicators variables.And by using the BP neural network algorithm model,the credit risk identification rate and credit risk classification accuracy rate of high risk network lending enterprises,under different conditions,were obtained.The results show that the credit risks of high-risk network lending enterprises are highly recognized,and have the characteristics of high recall rate and high accuracy.

Key words: high risk online lending enterprise risk identification, index screening, neural network, recall rate

CLC Number: 

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