3 papers
cs.SI2025
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Bruno Deprez, Bart Baesens, Tim Verdonck +1
Purpose: We introduce GARG-AML, a fast and transparent graph-based method to catch `smurfing', a common money-laundering tactic. It assigns a single, easy-to-understand risk score…
cs.LG2025
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Bruno Deprez, Wei Wei, Wouter Verbeke +3
Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts o…
cs.LG2024
Uplift modeling with continuous treatments: A predict-then-optimize approach
Simon De Vos, Christopher Bockel-Rickermann, Stefan Lessmann +1
The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment. One common approach involves two steps:…