Experian: Why AI Agents Need Trust to Scale in Fintech

Few technologies have moved as quickly from experiment to expectation as AI.

In financial services, however, the question has shifted from whether firms can build an AI model to whether they can deploy one that customers, regulators and colleagues can trust.

That challenge sits at the heart of Gaurav Arora’s work at Experian.

As Head of Gen AI & Automation, Credit & Verification Services at Experian UK&I, he leads the team behind ProductMate, the company’s internal agentic AI platform.

Originally created to help generate user stories, ProductMate has since expanded across product development and service, supporting thousands of colleagues worldwide.

For Gaurav, the goal is to make AI useful beyond the people writing code. Product managers, testers, service engineers and governance teams all need access to an “AI colleague” – but only if it is built on trusted data, transparent decision-making and clear human accountability.

In this Q&A with FinTech Magazine, Gaurav discusses how Experian is scaling AI agents in a regulated environment, why governance should be designed in from the start and what fintechs can learn from moving agentic AI beyond the pilot stage.

What does it take to put AI agents into production at scale?

Getting the model right is only part of the work. What gets you to scale is a shared foundation every agent inherits.

Ours gives each agent its own identity, one controlled gateway into our systems of record, memory, full observability of every action and evaluation that runs continuously rather than once before launch.

Build it once and you stop resolving the same problems team by team.

The proof is the portfolio. On that one foundation we run a range of agents with various USPs and responsibilities, all live and each evolving towards more autonomous capability over days instead of months.

And each has a named owner and a real team whose work it changes. Agents without owners don’t survive production.