基于神经网络模型的网络借贷高危企业信用风险的识别研究

王茂光; 朱子君

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

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基于神经网络模型的网络借贷高危企业信用风险的识别研究

学术论文 | 更新时间：2024-06-03

- 基于神经网络模型的网络借贷高危企业信用风险的识别研究
- Credit risk identification of high-risk online lending enterprises based on neural network model
- 网络与信息安全学报 2017年3卷第12期页码：15-21
- 作者机构：
- 作者简介：
  
  [ "王茂光（1974-），男，山东招远人，中央财经大学教授，主要研究方向为互联网金融风控和征信、软件工程、分布式智能系统。" ]
  [ "朱子君（1993-），女，吉林长春人，中央财经大学硕士生，主要研究方向为互联网金融风控和征信。" ]
- 基金信息：
  
  网金中心合作基金资助项目;Cooperation Project with Network Finance Center(020676116004);北京大学合作基金资助项目;Cooperation Project with Peking University(020676114004)
- DOI：10.11959/j.issn.2096-109x.2017.00222
  中图分类号： F49
- 网络首发：2017-12，
  
  纸质出版：2017-12-15
- 稿件说明：
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王茂光, 朱子君. 基于神经网络模型的网络借贷高危企业信用风险的识别研究[J]. 网络与信息安全学报, 2017,3(12):15-21.

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.
王茂光, 朱子君. 基于神经网络模型的网络借贷高危企业信用风险的识别研究[J]. 网络与信息安全学报, 2017,3(12):15-21. DOI： 10.11959/j.issn.2096-109x.2017.00222.

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.

关键词

Keywords

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