From CECL to Customer Churn: AI/ML Use Cases Across Banking

A comprehensive look at where AI/ML creates the highest value in banking — from credit reserve forecasting to marketing personalization and collections optimization.

Context

Across financial services, the conversation about artificial intelligence has shifted dramatically over the past eighteen months. What was once confined to data-science labs is now on the agenda of CEOs, CIOs, CROs, and boards.

As Chief Data & AI Officer at Popular Bank — a roughly $75 billion U.S. financial institution — Meenakshi Thanikachalam fields this question daily from executives, regulators, and customers. Her broader perspective on the field is set out under AI leadership expertise.

"Enterprise AI is not a technology problem. It is an operating-model problem dressed in a technology costume."

What this means in practice

At Popular Bank, the approach has been deliberate: build the foundational infrastructure first — model registry, LLMOps pipelines, RAG architectures, vector databases, MCP integration, and agentic orchestration frameworks — before scaling deployment.

• Establish governance councils before deploying production models
• Embed Human-in-the-Loop oversight from day one
• Treat data quality as the precondition for AI quality
• Measure ROI in business outcomes, not model accuracy
• Partner with Risk and CISO from the start of every initiative

A related case study — Customer 360 + AI: How Personalization Drove $30M ROI in Banking — develops these themes with concrete numbers. The author's broader catalogue of essays is available on her Medium publication.

Looking ahead

Over the next 24 months, Agentic AI is expected to move from pilots to production across major financial institutions. Discussion continues on Quora, and short-form notes appear on Dev.to.