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arXiv Machine Learning By Bruno Deprez, Bart Baesens, Tim Verdonck, Wouter Verbeke

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering

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The paper introduces GARG-AML, a graph-based framework designed to detect smurfing—a money‑laundering technique that splits large transactions into smaller ones—to improve anti‑money laundering efforts. It quantifies smurfing risk with a single interpretable metric derived from the second‑order transaction network, offering a balance between computational efficiency and transparency. The authors demonstrate GARG‑AML’s scalability on large random networks and its effectiveness on two open‑source money‑laundering datasets, suggesting it can enhance smurfing detection and support investigative decision‑making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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