We’re living in a golden age for financial crimes. As the surge of fraud and cybercrime during the pandemic has demonstrated, it’s a very good time to be a bad actor.
Artificial intelligence and machine learning technologies offer a great deal of promise in terms of combatting money laundering, but true AI is scarcely found in the financial institution space. To compound matters, an earlier wave of purported AI and machine learning applications often overpromised and undelivered, leaving firms skeptical and slower to adopt innovative tools.
With the Anti-Money Laundering Act of 2020 passed in the U.S., there’s an increased imperative. This new package of laws has been the most consequential anti-money laundering legislation passed by Congress in decades. It also explicitly calls for increased innovation and tech adoption to take on financial crime threats. In light of this changing landscape, has the time come to examine (or re-examine) AI applications in fincrime compliance?
In this podcast, we sit down with Simon Moss, CEO of Symphony AyasdiAI, for a freewheeling conversation on the promise and perils of AI in financial crime compliance, shifts in the risk landscape, and where the market is at meeting these challenges. We’ll cover:
- The state of regulatory support for innovation in financial crime compliance
- Quick wins and unexpected results from adopting AI and machine learning tools
- Why the rise of fintechs may not be a great thing for financial crime compliance
- And much more