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NVIDIA AI Podcast

How Visa Is Making Payments Safer and Smarter with AI - Ep. 256

22 min episode · 2 min read
·
Sarah Laszlo

Episode

22 min

Read time

2 min

Topics

Fundraising & VC, Design & UX, Artificial Intelligence

AI-Generated Summary

Key Takeaways

  • Virtual GPU optimization: Visa isolates memory and compute into separate virtual GPU instances on single cards, giving users one-tenth of a GPU instead of full allocation, dramatically improving cluster utilization while maintaining user experience for enterprises with limited cloud access.
  • Code modernization with GenAI: One engineer used GPT-4 to convert 50 legacy jobs from an unsupported programming language to Python in a single quarter, saving Visa $5 million by automating code translation that no internal staff could perform manually.
  • Ray Everywhere strategy: Visa adopted AnyScale's Ray ecosystem for the entire AI pipeline from data conditioning through model training to serving, creating a unified factory approach that accelerates model refresh cycles critical for staying ahead of evolving fraud tactics.
  • Privacy-preserving personalization: Visa creates high-quality consumer embeddings from trillions of transaction observations without exposing raw cardholder data, developing abstract representations that enable better product recommendations than competitors while maintaining strict privacy standards and regulatory compliance.

What It Covers

Sarah Laszlo, Senior Director of Visa's machine learning platform, explains how Visa leverages AI for fraud prevention, personalized cardholder experiences, and agentic commerce while managing petabyte-scale data in proprietary data centers.

Key Questions Answered

  • Virtual GPU optimization: Visa isolates memory and compute into separate virtual GPU instances on single cards, giving users one-tenth of a GPU instead of full allocation, dramatically improving cluster utilization while maintaining user experience for enterprises with limited cloud access.
  • Code modernization with GenAI: One engineer used GPT-4 to convert 50 legacy jobs from an unsupported programming language to Python in a single quarter, saving Visa $5 million by automating code translation that no internal staff could perform manually.
  • Ray Everywhere strategy: Visa adopted AnyScale's Ray ecosystem for the entire AI pipeline from data conditioning through model training to serving, creating a unified factory approach that accelerates model refresh cycles critical for staying ahead of evolving fraud tactics.
  • Privacy-preserving personalization: Visa creates high-quality consumer embeddings from trillions of transaction observations without exposing raw cardholder data, developing abstract representations that enable better product recommendations than competitors while maintaining strict privacy standards and regulatory compliance.

Notable Moment

Laszlo reveals Visa possesses a dataset rivaling Google's scale, with petabytes of transaction data providing consumer insights that even major tech companies cannot access, enabling uniquely powerful personalization models based on actual purchasing behavior rather than browsing history.

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Episode Transcript

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. AI's impact on the financial services industry has already been quite significant. From improving customer satisfaction and loyalty to streamlining operations and reducing costs, artificial intelligence is transforming an industry that's already been at the forefront of technology innovation for quite some time now. Visa is leading the way in leveraging AI to transform payment experiences. And with us to talk about how they're doing it is Sarah Laszlo. Sarah is senior director of Visa's machine learning platform, where she's architecting the infrastructure that will power Visa's future. Before joining Visa, Sarah well, her resume is really impressive and too long to get into right now, but I have to mention she was on NPR Science Friday, which is pretty cool. Sarah, welcome. Thank you so much for taking the time to join the AI podcast. Yes. Thank you so much. So before we get into all the stuff you're doing at Visa, all the stuff Visa's doing, would you tell us a little bit about your own journey, maybe how you got started in AI, and how you wound up in the role you're in now? Yeah. So, one thing that I always like to remind people of these days is that, of course, everybody thinks of artificial intelligence as something maybe that computer scientists do, but we need to remember that the back propagation paper that opened the field back up again after sort of the AI winter was, yes, Geoff Hinton was on there, but it was also coauthored by three psychologists, David Rumelhart. And I came up through that tradition of psychology. Oh, no kidding. Yeah. So my PhD is in psychology, and I became interested in computational neuroscience. There were many steps along the way Sure. Sure. Dot dot. Yeah. But now I still do it. Very cool. Did you practice as a psychologist? I was not ever a clinical psychologist or therapist. I was a cognitive neuroscientist. Right. So, you know, I worked with human research participants, but never in a therapeutic context. Got you. And so was computational neuroscience kind of your pathway leading to where you are now? Yeah. So, my postdoctoral adviser, David Plaut, was appointed in both psychology and computer science at Carnegie Mellon, and my academic grandfather in that line is Jeff Hinton. So Amazing. Most of the people who who were in that line and now do this. Right. Because that was sort of there were not that many people in 2009 that were doing deep learning. And so if you are one of those, you're one of the only, you know, 20 people that have fifteen years of deep learning experience. Totally. Totally. Incredible. I kind of hinted at it, and we don't want we wanna talk about what you're doing. How did you wind up with this role? Yeah. So I was working in responsible AI at Google, and I did that for …

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Tools

  • by OpenAI

    One engineer used GPT-4 to convert 50 legacy jobs from an unsupported programming language to Python in a single quarter, saving Visa $5 million by automating code translation that no internal staff could perform manually.
  • RayRecommended

    by Anyscale

    Visa adopted AnyScale's Ray ecosystem for the entire AI pipeline from data conditioning through model training to serving, creating a unified factory approach that accelerates model refresh cycles critical for staying ahead of evolving fraud tactics.

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