Machine Learning and Fraud: A Practitioner Perspective

 

Machine learning is rapidly reshaping how the payments industry approaches fraud—moving from static, rules-based systems to dynamic, predictive intelligence. But what does that shift look like in practice?

In this episode of the U.S. Payments Forum Podcast, industry experts explore how machine learning is being applied today to detect fraud, reduce false positives, and adapt to evolving threats in real time. From understanding the difference between predictive AI and generative AI to integrating machine learning into existing fraud stacks, this conversation offers a practical, real-world view into what’s working—and what’s next.

Listeners will also gain insight into implementation challenges, governance considerations, and the growing importance of cross-channel data, as well as a forward-looking perspective on emerging threats like scams and deepfakes.

In This Episode, You’ll Learn:

  • How machine learning differs from traditional rules-based fraud systems
  • The role of predictive AI vs. generative AI in fraud prevention
  • How organizations are using machine learning to improve detection and reduce false positives
  • Key implementation challenges, including data integration and model governance
  • Ethical considerations and bias in machine learning models
  • What’s next: emerging threats, deepfakes, and the future of fraud prevention

Host: Marie Jordan, Visa

Guests: Lenny Gusel, Feedzai and Jonathon Robinson

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