Title: Scalable Graph Learning for Anti-Money Laundering: A First Look,
Abstract: Organized crime inflicts human suffering on a genocidal scale: the Mexican
drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people
per year are "exported" in a human trafficking industry enslaving an estimated
40 million people. These nefarious industries rely on sophisticated money
laundering schemes to operate. Despite tremendous resources dedicated to
anti-money laundering (AML) only a tiny fraction of illicit activity is
prevented. The research community can help. In this brief paper, we map the
structural and behavioral dynamics driving the technical challenge. We review
AML methods, current and emergent. We provide a first look at scalable graph
convolutional neural networks for forensic analysis of financial data, which is
massive, dense, and dynamic. We report preliminary experimental results using a
large synthetic graph (1M nodes, 9M edges) generated by a data simulator we
created called AMLSim. We consider opportunities for high performance
efficiency, in terms of computation and memory, and we share results from a
simple graph compression experiment. Our results support our working hypothesis
that graph deep learning for AML bears great promise in the fight against
criminal financial activity.