Sep 9–12, 2019

Fighting crime with graph learning

Mark Weber (MIT-IBM Watson AI Lab)
2:35pm3:15pm Wednesday, September 11, 2019
Location: LL21 C/D
Secondary topics:  Machine Learning
Average rating: ****.
(4.00, 3 ratings)

Who is this presentation for?

  • Data scientists, CTOs, and researchers
  • Those who work in finance, among others




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 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
Photo of Mark Weber

Mark Weber

MIT-IBM Watson AI Lab

Mark Weber is a research scientist at the MIT-IBM Watson AI Lab, where he develops new graph analytics methods for anti-money laundering. His expertise is in connecting dots across disciplines to develop emergent technologies for positive real-world impact. Previously, he was 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; he produced documentary films on political economy and development, most notably Poverty, Inc., winner of over 50 film-festival honors and the $100,000 Templeton Freedom Award (available on Netflix and other platforms. He earned his MBA in finance from the MIT Sloan School of Management, where he was a fellow at the Legatum Center for Entrepreneurship and Development. As a public speaker, Mark enjoys opportunities to share his research and learn from others. He’s delivered talks at over 100 top universities, organizations, and events around the world. He can be found on Twitter as @markrweber.

  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dataiku
  • Dell Technologies
  • Intuit
  • Gamalon
  • Hewlett Packard Enterprise
  • MapR Technologies
  • Sisu Data
  • Intuit

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