February 2020
Intermediate to advanced
50m
English
Closed Captioning available in German, English, Spanish, French, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional)
Overview
Organized crime inflicts human suffering on a massive scale: the Mexican drug cartels have murdered 150,000 people since 2006; upward 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. 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.
Prerequisite knowledge
- A basic understanding of data science and graph structures
- Experience with finance (useful but not required)
What you'll learn
- See why graph deep learning is a powerful tool for finance and other applications
This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA.
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