金融交易数据驱动的图谱网络智能化欺诈侦测

doi:10.3969/j.issn.0255-8297.2020.05.005

Abstract

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

CLC Number:

TP181

SUN Quan, TANG Tao, ZHENG Jianbin, PAN Jing, ZHAO Jintao. Financial Transaction Data Based Intelligent Fraud Graph Network Detection[J]. Journal of Applied Sciences, 2020, 38(5): 713-723.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2020.05.005

https://www.jas.shu.edu.cn/EN/Y2020/V38/I5/713

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Financial Transaction Data Based Intelligent Fraud Graph Network Detection

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