ML Anthology

Smurf-Based Anti-Money Laundering in Time-Evolving Transaction Networks

Michele Starnini, Charalampos E. Tsourakakis, Maryam Zamanipour, André Panisson, Walter Allasia, Marco Fornasiero, Laura Li Puma, Valeria Ricci, Silvia Ronchiadin, Angela Ugrinoska, Marco Varetto, Dario Moncalvo
ECML-PKDD 2021 pp. 171-186
doi:10.1007/978-3-030-86514-6_11 /ecmlpkdd/2021/starnini2021ecmlpkdd-smurfbased/

Abstract

Money laundering refers to the criminal attempt of concealing the origins of illegally obtained money, usually by passing it through a complex sequence of seemingly legitimate financial transactions through several financial institutions. Given a large time-evolving graph of financial transactions, how can we spot money laundering activities? In this work, we focus on detecting smurfing, a money-laundering technique that involves breaking up large amounts of money into multiple small transactions. Our key contribution is a method that efficiently finds suspicious smurf-like subgraphs. Specifically, we find that the velocity characteristics of smurfing allow us to find smurfs by using a standard database join, thus bypassing the computational complexity of the subgraph isomorphism problem. We apply our method on a real-world transaction graph spanning a period of six months, with more than 180M transactions involving more than 31M bank accounts, and we verify its efficiency. Finally, by a careful analysis of the suspicious motifs found, we provide a classification of smurf-like motifs into categories that shed light on how money launderers exploit geography, among other things, in their illicit transactions.

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Cite

Text

Starnini et al. "Smurf-Based Anti-Money Laundering in Time-Evolving Transaction Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021. doi:10.1007/978-3-030-86514-6_11

Markdown

[Starnini et al. "Smurf-Based Anti-Money Laundering in Time-Evolving Transaction Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021.](https://mlanthology.org/ecmlpkdd/2021/starnini2021ecmlpkdd-smurfbased/) doi:10.1007/978-3-030-86514-6_11

BibTeX

@inproceedings{starnini2021ecmlpkdd-smurfbased,
  title     = {{Smurf-Based Anti-Money Laundering in Time-Evolving Transaction Networks}},
  author    = {Starnini, Michele and Tsourakakis, Charalampos E. and Zamanipour, Maryam and Panisson, André and Allasia, Walter and Fornasiero, Marco and Puma, Laura Li and Ricci, Valeria and Ronchiadin, Silvia and Ugrinoska, Angela and Varetto, Marco and Moncalvo, Dario},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2021},
  pages     = {171-186},
  doi       = {10.1007/978-3-030-86514-6_11},
  url       = {https://mlanthology.org/ecmlpkdd/2021/starnini2021ecmlpkdd-smurfbased/}
}