Prem Natarajan

Prem Natarajan, Executive Vice President and Chief Scientist at Capital One, dives into the transformative effects of AI on financial services. He discusses how generative AI enhances customer experiences and internal operations, particularly through innovations like chat concierges for seamless car buying. Prem highlights the company’s 'AI factory' model, leveraging public cloud technology and proprietary AI applications for tailored solutions. Furthermore, he emphasizes collaboration with diverse talents to tackle the unique challenges of AI in finance.

Prem Natarajan, Capital One’s Chief Scientist leading enterprise AI, explains why the bank builds its own AI stack and custom weights. He discusses cloud-native architectures that speed experimentation. He covers using proprietary data with open models, cautious AI use in credit, multi-agent workflows like MACA, and AI as a capacity multiplier for engineers and customer support.

Prem Natarajan, Chief scientist at Capital One, discusses applying AI in fraud detection, the transformative power of large language models and generative AI, evolving composition of AI teams, and the potential applications of generative AI in summarizing papers and comparing documents.

In this discussion, Prem Natarajan, Chief Scientist and Head of Enterprise AI at Capital One, tackles AI access and inclusivity as critical challenges in banking. He highlights the importance of diversity in data sets to combat biases and improve fraud detection. Prem shares insights on the use of foundation models and federated learning, emphasizing data quality and privacy preservation. He also stresses the need for collaboration between academia and industry to enhance AI impact, ultimately advocating for mission-inspired research that benefits customers and the broader community.

Most companies are racing to adopt generative and agentic AI — but many are skipping the most important steps. In this episode of Today in Tech, host Keith Shaw is joined by Prem Natarajan, EVP, Chief Scientist, and Head of Enterprise AI at Capital One, for a candid discussion about what true AI readiness looks like in the enterprise. Learn why flashy demos aren’t enough — and what organizations must invest in to move beyond experimentation: Why data quality, cloud architecture, and talent are non-negotiables How Capital One built its own AI agent platform (and why most companies can’t yet) The difference between outputs vs. outcomes in agentic AI What “human in the loop” really means in 2025 Why trust must be earned — by the technology The overlooked role of governance, risk, and strategic patience AI success starts long before the first model is deployed.