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

Mao-guang WANG; Zi-jun ZHU

doi:10.11959/j.issn.2096-109x.2017.00222

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

Papers | 更新时间：2024-06-03

- Credit risk identification of high-risk online lending enterprises based on neural network model
- Chinese Journal of Network and Information Security Vol. 3, Issue 12, Pages: 15-21(2017)
- 作者机构：
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- DOI：10.11959/j.issn.2096-109x.2017.00222
  CLC： F49
- Online First：2017-12，
  
  Published：15 December 2017
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Mao-guang WANG, Zi-jun ZHU. Credit risk identification of high-risk online lending enterprises based on neural network model[J]. Chinese Journal of Network and Information Security, 2017, 3(12): 15-21.
DOI：

Mao-guang WANG, Zi-jun ZHU. Credit risk identification of high-risk online lending enterprises based on neural network model[J]. Chinese Journal of Network and Information Security, 2017, 3(12): 15-21. DOI： 10.11959/j.issn.2096-109x.2017.00222.

摘要

网络借贷的飞速发展在一定程度上缓解了小微型企业融资难的问题，但也暴露出网络借贷平台信用风险的识别问题。为充分识别高危网贷企业的特征，以中小型网贷企业为样本，通过指标筛选，挑选出与风险识别相关度较高的指标作为指标变量。并利用BP神经网络算法模型得出高危网贷企业在不同条件下的信用风险识别率和信用风险分类正确率。实验结果表明，高危网贷企业的信用风险具有高度识别性，高召回率、高正确率的特点。

Abstract

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.

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references

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