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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Mining Knowl. Discov. 29(3), 626–688 (2014). https://doi.org/10.1007/s10618-014-0365-y
Balkema, A.A., De Haan, L.: Residual life time at great age. Annals of Probability (1974)
Feng, W., Liu, S., Danai, K., Shen, H., Cheng, X.: Specgreedy: unified dense subgraph detection. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) (2020)
Hooi, B., Shin, K., Lamba, H., Faloutsos, C.: Telltail: fast scoring and detection of dense subgraphs. In: AAAI (2020)
Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: Fraudar: bounding graph fraud in the face of camouflage. In: SIGKDD. ACM (2016)
Jiang, M., Beutel, A., Cui, P., Hooi, B., Yang, S., Faloutsos, C.: A general suspiciousness metric for dense blocks in multimodal data. In: ICDM (2015)
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Catchsync: catching synchronized behavior in large directed graphs. In: SIGKDD. ACM (2014)
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Inferring strange behavior from connectivity pattern in social networks. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8443, pp. 126–138. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06608-0_11
Khan, N.S., Larik, A.S., Rajput, Q., Haider, S.: A Bayesian approach for suspicious financial activity reporting. Int. J. Comput. Appl. 35, 181–187 (2013)
Khanuja, H.K., Adane, D.S.: Forensic analysis for monitoring database transactions. In: Mauri, J.L., Thampi, S.M., Rawat, D.B., Jin, D. (eds.) SSCC 2014. CCIS, vol. 467, pp. 201–210. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44966-0_19
Kolda, T., Bader, B.: Tensor decompositions and applications. SIAM Review (2009)
Li, X., et al.: Flowscope: spotting money laundering based on graphs. In: AAAI (2020)
Liu, S., Hooi, B., Faloutsos, C.: A contrast metric for fraud detection in rich graphs. IEEE Trans. Knowl. Data Eng. 31, 2235–2248 (2019)
Liu, S., Hooi, B., Faloutsos, C.: Holoscope: topology-and-spike aware fraud detection. In: CIKM. ACM (2017)
Lv, L.T., Ji, N., Zhang, J.L.: A RBF neural network model for anti-money laundering. In: ICWAPR. IEEE (2008)
Lütkebohle, I.: Bworld robot control software. https://data.world/lpetrocelli/czech-financial-dataset-real-anonymized-transactions/. Accessed 2 Nov 2018
Prakash, B.A., Sridharan, A., Seshadri, M., Machiraju, S., Faloutsos, C.: EigenSpokes: surprising patterns and scalable community chipping in large graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6119, pp. 435–448. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13672-6_42
Shin, K., Hooi, B., Faloutsos, C.: M-Zoom: fast dense-block detection in tensors with quality guarantees. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 264–280. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46128-1_17
Shin, K., Hooi, B., Kim, J., Faloutsos, C.: D-cube: dense-block detection in terabyte-scale tensors. In: WSDM. ACM (2017)
Stavarache, L.L., Narbutis, D., Suzumura, T., Harishankar, R., Žaltauskas, A.: Exploring multi-banking customer-to-customer relations in aml context with poincar\(\backslash \)’e embeddings. arXiv preprint arXiv:1912.07701 (2019)
Tang, J., Yin, J.: Developing an intelligent data discriminating system of anti-money laundering based on SVM. In: ICMLC. IEEE (2005)
Wang, S.N., Yang, J.G.: A money laundering risk evaluation method based on decision tree. In: ICMLC. IEEE (2007)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-75762-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75761-8
Online ISBN: 978-3-030-75762-5
eBook Packages: Computer ScienceComputer Science (R0)