基于知识图谱的小微企业贷款申请反欺诈方案

金磐石; 万光明; 沈丽忠

doi:10.11959/j.issn.2096-0271.2019035

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基于知识图谱的小微企业贷款申请反欺诈方案

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- 基于知识图谱的小微企业贷款申请反欺诈方案
- Knowledge graph-based fraud detection for small and micro enterprise loans
- 大数据 2019年5卷第4期页码：100-112
- 作者机构：
  
  1. 中国建设银行股份有限公司，北京 100033
  2. 中国建设银行金融科技部，北京 100032；3.建信金融科技有限责任公司厦门事业群，福建厦门 361008
  3. 建信金融科技有限责任公司厦门事业群，福建厦门 361008
- 作者简介：
  
  [ "金磐石（1965- ），男，中国建设银行股份有限公司信息总监，主要研究方向为数据处理领域技术研究与应用、人工智能领域技术研究与应用等。" ]
  [ "万光明（1974- ），男，中国建设银行金融科技部高级工程师，主要研究方向为应用架构管理、人工智能方向架构管理等。" ]
  [ "沈丽忠（1978- ），男，建信金融科技有限责任公司厦门事业群大数据平台架构师，主要研究方向为数据挖掘、分布式存储、分布式计算、流计算、数据分析等。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019035
  中图分类号： TP393
- 网络首发：2019-07，
  
  纸质出版：2019-07-15
- 稿件说明：
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金磐石, 万光明, 沈丽忠. 基于知识图谱的小微企业贷款申请反欺诈方案[J]. 大数据, 2019,5(4):100-112.

Panshi JIN, Guangming WAN, Lizhong SHEN. Knowledge graph-based fraud detection for small and micro enterprise loans[J]. Big Data Research, 2019, 5(4): 100-112.
金磐石, 万光明, 沈丽忠. 基于知识图谱的小微企业贷款申请反欺诈方案[J]. 大数据, 2019,5(4):100-112. DOI： 10.11959/j.issn.2096-0271.2019035.

Panshi JIN, Guangming WAN, Lizhong SHEN. Knowledge graph-based fraud detection for small and micro enterprise loans[J]. Big Data Research, 2019, 5(4): 100-112. DOI： 10.11959/j.issn.2096-0271.2019035.

摘要

近年来，在各大商业银行竞相开展小微企业信贷业务的同时，贷款欺诈风险也随之产生。针对小微企业信贷业务的特点，提出了一种基于全方位企业画像与企业关联图谱的贷前反欺诈模型。通过整合多源信息，形成完整的企业属性特征，并结合从图谱中提取的关系网络结构特征，把特征共同输入模型，以定量评估小微企业客户的欺诈风险。实验表明，使用隐含在关系图谱中的信息比单纯使用企业自身特征建模在测试集上的AUC提高了5%，有助于银行机构准确地对企业申贷欺诈行为进行评估。

Abstract

While major commercial banks have been providing various business loans

the risk of loan fraud has arisen at the same time.In order to overcome this challenge

an anti-fraud model was proposed based on the full-scale enterprise portrait and enterprise relation graph.By integrating multi-source information to form a concrete enterprise profile

and quantifying the interactions among enterprise entities

the fraud risk of new SMEs’loan applications could be quantitatively evaluated.Experiments show that compared with purely considering enterprise’s attributes

the additional use of relational information contributes a 5% increase in the AUC of the test set

which is more helpful for banking institutions to accurately assess the corporate fraud risk.

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references

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