How Top Banks Are Engineering AI At Scale

Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I started by managing large-scale data platforms for consumer banking, which set the base for building a cloud-native AI/ML environment. That platform now supports a wide range of internal models across risk, lending, and customer engagement.

Q2. What, in your view, truly makes an enterprise “AI-ready” today—maturity in data infrastructure, a solid cloud strategy, the right org design, or something else?

Being “AI-ready” means three things working together:

• Treating data as products with quality/lineage/SLA,
• Having a flexible cloud/hybrid setup
• Having controls and compliance built into the workflow.
  Because of that, we can turn an idea into a governed service very quickly.

Q3.How do you see leading banks navigating the shift toward in-house GenAI platforms or fine-tuned LLMs?

Leading banks are taking an “adopt then tailor” approach to GenAI: start from a strong foundation model, add retrieval over internal knowledge, fine-tune on enterprise-safe data, and run it inside the bank’s own secure environment. That way, you get speed while still keeping data, guardrails, and audit in your own perimeter.

Q4.What do you think are thebiggest lessons…