| Summary: | "Despite tremendous resources dedicated to anti-money laundering (AML), only a tiny fraction of illicit activity is prevented. The research community can help. Mark Weber (MIT-IBM Watson AI Lab) explores how to map the structural and behavioral dynamics driving the technical challenge, and he reviews AML methods both current and emergent. You'll get a first look at scalable graph convolutional neural networks for forensic analysis of financial data, which is massive, dense, and dynamic. Mark outlines preliminary experimental results using a large synthetic graph (1M nodes, 9M edges) generated by a data simulator called AMLSim, and he considers opportunities for high performance efficiency, in terms of computation and memory, and shares results from a simple graph compression experiment, all of which supports the working hypothesis that graph deep learning for AML bears great promise in the fight against criminal financial activity. This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA."--Resource description page
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