文章摘要
Sun Quan (孙权)* **,Tang Tao * **,Zheng Jianbin**,Lin Jiale***,Zhao Jintao**,Liu Hongbao**.[J].高技术通讯(英文),2020,(3):253~261
Fraud detection on payment transaction networks via graph computing and visualization
  
DOI:doi:10.3772/j.issn.1006-6748.2020.03.003
中文关键词: 
英文关键词: payment fraud detection, graph computing, graph embedding, machine learning
基金项目:
Author NameAffiliation
Sun Quan (孙权)* ** (*School of Computer Science, Fudan University, Shanghai 200433, P.R.China) (**China UnionPay Research Institute of Electronic Payment, Shanghai 201201, P.R.China) 
Tang Tao * ** (*School of Computer Science, Fudan University, Shanghai 200433, P.R.China) (**China UnionPay Research Institute of Electronic Payment, Shanghai 201201, P.R.China) 
Zheng Jianbin** (**China UnionPay Research Institute of Electronic Payment, Shanghai 201201, P.R.China) 
Lin Jiale*** (***School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R.China) 
Zhao Jintao** (**China UnionPay Research Institute of Electronic Payment, Shanghai 201201, P.R.China) 
Liu Hongbao** (**China UnionPay Research Institute of Electronic Payment, Shanghai 201201, P.R.China) 
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中文摘要:
      
英文摘要:
      With the fast development of Internet technology, more and more payments are fulfilled by mobile Apps in an electrical way which significantly saves time and efforts for payment. Such a change has benefited a large number of individual users as well as merchants, and a few major players for payment service have emerged in China. As a result, the payment service competition becomes even fierce, and various promotion activities have been launched for attracting more users by the payment service providers. In this paper, the problem focused on is fraud payment detection, which in fact has been a major concern for the providers who spend a significant amount of money to popularize their payment tools. This paper tries the graph computing-based visualization to the behavior of transactions occuring between the individual users and merchants. Specifically, a network analysis-based pipeline has been built. It consists of the following key components: transaction network building based on daily records aggregation; transaction network filtering based on edge and node removal; transaction network decomposition by community detection; detected transaction community visualization. The proposed approach is verified on the real-world dataset collected from the major player in the payment market in Asia and the qualitative results show the efficiency of the method.
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