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Titan’s Founder and CEO, Arjun Sirrah, recently sat down with The Bank Slate’s Paul Davis to discuss the future of AI in banking, why most large language models fall short in financial services, and how Titan is building purpose-built banking AI designed for real operational environments.
Bringing a rare mix of fintech and banking experience to the conversation, Arjun discusses his time at Laurel Road, helping scale the business from inside a community bank before its eventual acquisition by KeyCorp. He later became Head of Digital and Fintech at Key, earning recognition as American Banker’s Digital Banker of the Year and winning the CIO 100 Award along the way.
In the interview, Arjun explains why Titan believes banking requires specialized AI models trained specifically for the industry, not generalized systems retrofitted for financial services.
“The three biggest obstacles banks face in adopting AI are security, explainability, and domain expertise,” Sirrah said. “Large language models are impressive generalists, but that’s very different from having real depth in banking.”
The discussion explores:
- Why Titan built smaller banking-specific AI models instead of relying entirely on general-purpose LLMs
- How banks can use AI to improve operational efficiency ratios
- The growing importance of explainability and portability in regulated environments
- Where community banks are adopting AI fastest
- Why banking workflows ultimately come down to “search, retrieve, stare, and compare”
- How fintechs and banks continue to learn from each other
Arjun also shares his perspective as an investor in multiple unicorn fintech companies, discussing the importance of domain expertise, distribution strategy, and founder-market fit.
Read the full Q&A with Arjun Sirrah and Bank Slate here, or click the link to listen to the full podcast conversation.
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