Abstract
Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities, and always be a fast process involving frequent and chained transactions. How can we detect ML and fraudulent activity in large scale attributed transaction data (i.e. tensors)? Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts. CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions by modeling them as two coupled tensors and applying a novel multi-attribute metric which can reveal the transfer chains accurately. Extensive experiments show CubeFlow outperforms state-of-the-art baselines in ML behavior detection in both synthetic and real data.
X. Sun, J. Zhang and Q. Zhao—Contribute equally.
The work was done when Xiaobing Sun and Qiming Zhao were visiting students at ICT CAS, who are separately from NanKai University and Chongqing University.
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Acknowledgements
This paper is partially supported by the National Science Foundation of China under Grant No.91746301, 61772498, U1911401, 61872206, 61802370. This paper is also supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA19020400 and 2020 Tencent Wechat Rhino-Bird Focused Research Program.
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Sun, X. et al. (2021). CubeFlow: Money Laundering Detection with Coupled Tensors. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_7
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