Two Experts Share Strategies for Building More Effective AML Programs
Each year, billions of dollars are transferred in and out of money mule accounts to support a variety of money laundering schemes. But banks are now using machine learning and AI more effectively to spot mule accounts. Two experts – Brett Johnson, a consultant in cybersecurity, cybercrime and identity theft, and Charmian Simmons, strategy leader at SymphonyAI NetReveal – shared the latest approaches and tools for beefing up AML programs.
Simmons said AI and machine learning AML controls are helping to identify fraudsters sooner.
“Neither of those two technologies is new, but how they’re being applied in the world of fraud, money laundering, transaction monitoring, sanctions, breaches – those types of things are starting to elevate up,” Simmons said. “And that’s coming around supervised machine learning, semi-supervised components and even down to where we’re starting to see generative AI being used to help the investigation.”
Johnson recommended a layered approach to security to defend against the cybercriminal’s toolbox, which contains both sophisticated tools and unsophisticated tactics such as social engineering and spook phone calls. “As defenders, we need to be ready for all kinds of attacks,” Johnson said.
- The connection between new accounts and mule accounts;
- Steps banks need to take to spot mule accounts early;
- How to use AI and ML the right way to spot money laundering activity.
Simmons is a financial crime and compliance expert covering the EMEA region at SymphonyAI NetReveal. She has over 20 years of experience in the financial services sector across risk management, financial crime, internal controls and IT advisory.
Johnson, a cybersecurity consultant and public speaker, was nicknamed “The Original Internet Godfather” by the U.S. Secret Service for his 20-year career in cybercrime. He built and helped lead ShadowCrew, the precursor to today’s darknet markets, and helped design, implement and refine identity theft, account takeover fraud, card-not-present fraud, IRS tax fraud and other social engineering attacks, breaches and hacking operations.