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NVIDIA AI Podcast

Capital One’s Prem Natarajan Shares How AI Can Enhance Financial Services and Customer Experiences - Ep. 253

31 min episode · 2 min read
·
Prem Natarajan

Episode

31 min

Read time

2 min

Topics

Investing, Fundraising & VC, Leadership

AI-Generated Summary

Key Takeaways

  • Proprietary AI Advantage: Capital One uses its unique customer data to deeply customize open-weight models for financial tasks, creating differentiated AI capabilities competitors cannot replicate. This data advantage translates directly into superior customer experiences and fraud protection systems.
  • Agentic Workflow Architecture: Multi-agentic systems combine custom specialized models with company-specific business processes, enabling AI to complete tasks like scheduling test drives through the auto finance chat concierge, not just answer questions. Human-in-the-loop oversight creates reinforcement learning flywheels.
  • Responsibility Through Design: Financial services AI requires regulatory compliance and risk management built into the design phase, not added afterward. Capital One prioritizes use cases with high confidence in risk mitigation while exploring benefit potential, using formal evaluation processes before deployment.
  • Infrastructure Foundation: Six to seven years of data infrastructure investment preceded current AI capabilities. Organizations need clean, curated, reliable data platforms and world-class AI talent to handle the fragility of model training and last-mile customization challenges before achieving production results.

What It Covers

Prem Natarajan, Capital One's Chief Scientist and Head of AI, explains how the bank leverages proprietary data, open-source models, and agentic workflows to deliver AI-powered financial services to over 100 million customers.

Key Questions Answered

  • Proprietary AI Advantage: Capital One uses its unique customer data to deeply customize open-weight models for financial tasks, creating differentiated AI capabilities competitors cannot replicate. This data advantage translates directly into superior customer experiences and fraud protection systems.
  • Agentic Workflow Architecture: Multi-agentic systems combine custom specialized models with company-specific business processes, enabling AI to complete tasks like scheduling test drives through the auto finance chat concierge, not just answer questions. Human-in-the-loop oversight creates reinforcement learning flywheels.
  • Responsibility Through Design: Financial services AI requires regulatory compliance and risk management built into the design phase, not added afterward. Capital One prioritizes use cases with high confidence in risk mitigation while exploring benefit potential, using formal evaluation processes before deployment.
  • Infrastructure Foundation: Six to seven years of data infrastructure investment preceded current AI capabilities. Organizations need clean, curated, reliable data platforms and world-class AI talent to handle the fragility of model training and last-mile customization challenges before achieving production results.

Notable Moment

Natarajan reframes AI's purpose as transferring cognitive burden from humans to systems, allowing customers to experience magic rather than frustration. This philosophy drives Capital One's focus on reducing latency and meeting customers when, where, and how they want service.

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Episode Transcript

Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. The financial services industry has long been on the forefront of technological innovation. Today, generative AI is redefining financial services from customer service agents to AI factories enabling the next wave of industry innovation. As organizations move beyond testing and experimentation to successful AI implementation, these new technologies and ways of working are driving business results. Our guest is working on the leading edge of bringing the power of AI to financial services. Prem Natarajan is executive vice president, chief scientist, and head of AI at Capital One, where he leads the technology strategy, architecture, research, and development for all AI initiatives, as well as all data technologies across the company. Prem, welcome, and thank you so much for joining the AI podcast. Thank you for having me on the podcast, Noah. Can we start with a little bit, kind of an overview of your role, and then maybe you can talk about your approach, Capital One's approach to using AI? Yeah. Delighted to. You You know, I started my life, my professional journey, after finishing graduate school in a DARPA sponsored world. It was a company called BBN Technologies, which was, one of the pioneers in speech and language technologies at the time Okay. Which was really the big application of AI and machine learning if you think about it back. Like, you know, it was all tied with speech, language, and computer vision. Right. These are the three modalities that humans interact in. And it was also big on Internet working, but my my area was always in AI and machine learning. Right. So after about, like, two decades in this DARPA sponsored world, and back in the nineties and, the first decade of the February for much of it, DARPA was probably the biggest sponsor of, research and development in AI and machine learning. And we're standing on the shoulders of the outcomes of many of those programs Mhmm. That they sponsored. I then went to, USC where I was for several years, University of Southern California. Mhmm. Then I took a leave and I went to Amazon to be part of the Alexa organization. And at some point, I led the Alexa AI organization for a while Okay. And then came to Capital One. So throughout my professional career, though, I've been very close to the intersection of research and transitioning or translating those research advances into products and capabilities that benefit users in their everyday life because DARPA itself is very mission focused research. Sure. You're making fundamental advances, but you're also making advances that take that technology towards solving a set of needs Right. Right. In real life. The real world use. Yeah. The real world use. And so I've always been inspired by being at that intersection. Right. Right? Like, advancing the state of technology, but then advancing it in a way that then humans and other users can benefit …

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