Fighting Crime with Graph Learning
Who is this presentation for?Data scientists, CTOs, Researchers, Finance, Other
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.
Prerequisite knowledgeBasic understanding of data science and graph structures; a finance audience would be great but not required
What you'll learnGraph deep learning is a powerful tool for finance and other applications
MIT-IBM Watson AI Lab
Mark Weber (@markrweber) is a research scientist at the MIT-IBM Watson AI Lab. His expertise is connecting dots across disciplines to develop emergent technologies for positive real-world impact. Mark’s current work involves the development of new graph analytics methods for anti-money laundering (see https://www.markrweber.com/graph-deep-learning/).
Mark cut his teeth at the MIT Media Lab working at the Digital Currency Initiative, where he led the development of the b_verify protocol for publicly verifiable records, focused on warehouse receipts in agricultural supply chains. Papers and open-source code here: https://www.markrweber.com/b_verify/. Mark earned an MBA in finance from MIT Sloan, where he was a Fellow at the Legatum Center for Entrepreneurship & Development.
Prior to MIT, Mark produced documentary films on political economy and development, most notably a film called Poverty, Inc., winner of over 50 film festival honors and the $100,000 Templeton Freedom Award (available on Netflix and other platforms via www.povertyinc.org).
As a public speaker, Mark enjoys opportunities to share his research and learn from others. He has delivered talks at over 100 top universities, organizations, and events around the world.
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