应用科学学报 ›› 2020, Vol. 38 ›› Issue (5): 713-723.doi: 10.3969/j.issn.0255-8297.2020.05.005

• 智能计算新技术 • 上一篇    

金融交易数据驱动的图谱网络智能化欺诈侦测

孙权1,2, 汤韬1,2, 郑建宾1, 潘婧1,2, 赵金涛2   

  1. 1. 复旦大学 计算机科学技术学院, 上海 200433;
    2. 中国银联电子支付研究院, 上海 201201
  • 收稿日期:2020-05-11 发布日期:2020-10-14
  • 通信作者: 汤韬,博士,研究员,研究方向为数据挖掘风险防控.E-mail:tangtao2@unionpay.com E-mail:tangtao2@unionpay.com
  • 基金资助:
    国家发改委项目(No.2018AAA0100700,No.2016YFB1001003);上海市学科带头人项目(No.19XD1433700)资助

Financial Transaction Data Based Intelligent Fraud Graph Network Detection

SUN Quan1,2, TANG Tao1,2, ZHENG Jianbin1, PAN Jing1,2, ZHAO Jintao2   

  1. 1. School of Computer Science, Fudan University, Shanghai 200433, China;
    2. China UnionPay Research Institute of Electronic Payment, Shanghai 201201, China
  • Received:2020-05-11 Published:2020-10-14

摘要: 针对当前金融领域营销场景中商户与持卡人团伙化的交易欺诈行为难以侦测、挖掘的不足的问题,该文基于交易流水数据,构建了持卡人-商户的智能化交易图谱网络,建立了图拓扑特征体系框架和机器学习的异常检测算法,对当前营销欺诈团伙化网络进行了智能化侦测.基于相关样本数据提出的模型效果比传统模型具有较大提升,对欺诈团伙证据链挖掘和画像分析提供了有效方法.

关键词: 图计算, 可解释性, 人工智能, 数据融合

Abstract: Nowadays, fraud risk has been changing from individual fraud to group fraud, leading to jump rise of financial payment. How to identify and detect the group fraud is becoming a challenge in risk management. To deal with the group fraud transaction, this article builds a transaction graph network based on financial transaction data, and founds a topological graph feature extraction framework and anomaly detection model. Experiment on sample data shows that the proposed model obtains better results comparing with the previous individual feature analysis models, and gives reasonable explanation and evidence for the fraud detection.

Key words: graph computing, explainable, artificial intelligence, data aggregation

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