Identifying Illicit Accounts in Large-Scale E-Payment Networks -- A Graph Representation Learning Approach
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AbstractRapid and massive adoption of mobile/ online payment services has brought new challenges to the service providers as well as regulators in safeguarding the proper uses such services/ systems. In this paper, we leverage recent advances in deep-neural-network-based graph representation learning to detect abnormal/ suspicious financial transactions in real-world e-payment networks. In particular, we propose an end-to-end Graph Convolution Network (GCN)-based algorithm to learn the embeddings of the nodes and edges of a large-scale time-evolving graph. In the context of e-payment transaction graphs, the resultant node and edge embeddings can effectively characterize the user-background as well as the financial transaction patterns of individual account holders. As such, we can use the graph embedding results to drive downstream graph mining tasks such as node-classification to identify illicit accounts within the payment networks. Our algorithm outperforms state-of-the-art schemes including GraphSAGE, Gradient Boosting Decision Tree and Random Forest to deliver considerably higher accuracy (94.62\% and 86.98\% respectively) in classifying user accounts within 2 practical e-payment transaction datasets. It also achieves outstanding accuracy (97.43\%) for another biomedical entity identification task while using only edge-related information.
Acceptance Date20/06/2019
All Author(s) ListDa Sun Handason Tam, Wing Cheong Lau, Bin Hu, Qiu Fang Ying, Dah Ming Chiu, Hong Liu
Name of ConferenceArtificial Intelligence for Business Security Workshop (AIBS), IJCAI-19
Start Date of Conference10/08/2019
End Date of Conference16/08/2019
Place of ConferenceMacao, China
Country/Region of ConferenceMacau
LanguagesEnglish-United States

Last updated on 2020-15-09 at 17:06