利用网络数据预测企业失信行为

doi:10.11959/j.issn.2096-0271.2018049

Abstract

Abstract:

Previous enterprise credit level analysis mainly focused on the features including enterprise size,place of operation,industry category,registration and paid-in capital,and lacked in-depth analysis based on massive data.A directed investment network consisted of more than 4 million enterprises was built up,among which nearly 260 000 enterprises have various discredited behaviors.The results show that there is an obvious "network effect" in the discredited behaviors of enterprises.If the target enterprise's shareholders or its invested enterprises have discredited behaviors,the risk of having discredited behaviors of the target enterprise is far greater than the average.Based on this,a simple generalized linear regression algorithm was proposed to predict the discredited behaviors of enterprises,which is far more accurate than the regression method without considering the network effect.

Key words: discredited behavior prediction, network effect, enterprise credit

CLC Number:

TN399

Tao ZHOU, Yanli LI, Qian LI, Duanbing CHEN, Wenbo XIE, Tong WU, Tu ZENG. Predicting the discredited behavior of enterprises via large-scale investment network[J]. Big Data Research, 2018, 4(5): 41-49.

Figures/Tables 5

References 24

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特征		说明
一阶邻居特征	失信被投资对象数	对这里的数量进行了处理，当值大于或等于4时，值取4
	失信被投资对象比例	失信被投资对象数/被投资对象的总数
	失信投资者数	对这里的数量进行了处理，当值大于或等于4时，值取4
	失信投资者比例	失信投资者数/投资者的总数
	失信一阶邻居数	对这里的数量进行了处理，当值大于或等于4时，值取4
	失信一阶邻居比例	失信一阶邻居数/一阶邻居的总数
二阶邻居特征	二阶邻居中失信被投资对象数	对这里的数量进行了处理，当值大于或等于4时，值取4
	二阶邻居中失信被投资对象比例	二阶邻居中失信被投资对象/二阶邻居中被投资对象总数
	二阶邻居中失信投资者数	对这里的数量进行了处理，当值大于或等于4时，值取4
	二阶邻居中失信投资者比例	二阶邻居中失信投资者数/二阶邻居中投资者总数
	失信二阶邻居数	对这里的数量进行了处理，当值大于或等于4时，值取4
	失信二阶邻居比例	失信二阶邻居数/二阶邻居总数
基本特征	企业类型	个体户、有限公司等类型，离散型
	企业资本	公司注册资本
	企业规模	根据工业和信息化部联企业[2011]300号文件《关于印发中小企业划型标准规定的通知》的标准，针对不同行业计算得到的企业规模被分为大型、中型、小型、微型4类，数值型
	地区	公司所在地理位置，离散型
	行业	公司所处行业细分领域（见表1），离散型

Predicting the discredited behavior of enterprises via large-scale investment network

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