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Today we’re joined by Prem Natarajan, chief scientist and head of enterprise AI at Capital One. In our conversation, we discuss AI access and inclusivity as technical challenges and explore some of Prem and his team’s multidisciplinary approaches to tackling these complexities. We dive into the issues of bias, dealing with class imbalances, and the integration of various research initiatives to achieve additive results. Prem also shares his team’s work on foundation models for financial data curation, highlighting the importance of data quality and the use of federated learning, and emphasizing the impact these factors have on the model performance and reliability in critical applications like fraud detection. Lastly, Prem shares his overall approach to tackling AI research in the context of a banking enterprise, including prioritizing mission-inspired research aiming to deliver tangible benefits to customers and the broader community, investing in diverse talent and the best infrastructure, and forging strategic partnerships with a variety of academic labs.
I’d like to send a huge thanks to Capital One for their support of the podcast and their sponsorship of today’s show. Capital One's AI and machine learning capabilities are central to how it builds products and services — and they're now at the forefront of what’s possible in banking. Driving these efforts are Capital One's Applied AI Researchers, who work with top universities and research institutions to research, experiment, and accelerate the adoption of state-of-the-art academic advancements into the business.
To learn more about Applied Research job positions at Capital One, visit twimlai.com/capitalone.