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B. Baesens

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

This paper introduces GARG-AML, a novel graph-based method that quantifies smurfing risk through a single interpretable metric derived from the structure of the second-order transaction network, and demonstrates the scalability of GARG-AML on large random networks, and its performance on two open-source money launderin...

Bruno Deprez, B. Baesens, Tim Verdonck et al. · 2 citations · ⚡1

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