Enabling the AI-Driven Enterprise

Fighting Financial Crimes with AutoML

Fighting Financial Crimes with AutoML

Automated Machine Learning (AutoML) is dramatically improving the effectiveness and the efficiency of anti-money laundering (AML) and fraud detection.  DataRobot invites you to partake in a peer to peer dialogue discussing the impact autoML can have across the entire financial crimes detection and prevention process.
Hear from practitioners how autoML is being used in KYC, risk scoring, suspicious activity monitoring, and is improving the efficiency of financial investigations units by prioritizing alerts and safely reducing time spent chasing false positives.  Ways of using both supervised and unsupervised learning separately and in combination will be discussed.

Key Takeaways

  • An overview of successful financial crime detection applications leveraging DataRobot's autoML platform
  • How to apply both supervised and unsupervised learning techniques in financial crime detection
  • Opportunity to hear how other financial institutions are leveraging machine learning in financial crime prevention

Date & Location:


06:00 – Arrival

06:15 – Introduction

06:25 – ML trends within compliance

06:45 – Open discussion / Q&A

08:45 – Event concludes


Jessica Li Guild  Ph.D.
Jessica Li Guild Ph.D.
Director, Head of Digital Transformation Office Compliance Americas, Societe Générale
Ray Mi
Ray Mi
Customer Facing Data Scientist, DataRobot