The Future of Bank Compliance? There’s an AI Agent for That

Against a backdrop of rising financial complexity, ballooning regulatory risk and exploding customer expectations, trimming compliance bloat has become a new battleground for innovation.

“The last 20 years of compliance has been broadly building systems that help you identify risks,” Greenlite AI CEO Will Lawrence said during a conversation with PYMNTS CEO Karen Webster. “What happens when it goes wrong? That’s when a human gets involved. It’s a very labor-intensive and operationally intensive process.”

A study cited by Lawrence alleged that up to 85% of what compliance investigators do is non-analytical work, such as document processing, form-filling and internal follow-ups.

“If you told me my sales team was only going to spend 15% of their time selling, I’d be very concerned,” he said. “But we’ve tolerated that in compliance.”

Lawrence said Greenlite AI, fresh off a $15 million Series A funding round, believes it has cracked the code on the future of compliance. The solution lies not in throwing more software at the problem of financial crime and compliance but in letting smarter artificial intelligence agents automate away repetitive tasks.

Building for One of the Most Regulated Industries on Earth

Greenlite’s ambition isn’t to serve just FinTech disruptors. Its true target is the highly regulated financial institutions that move markets and set industry standards.

“You’re a brand-new company,” Webster said. “And you’re dealing with highly regulated financial institutions. How do you build trust when you’re talking about agents that people may not quite understand?”

“As part of our Series A, we announced a really exciting system we built internally,” Lawrence said. “We call it our trust infrastructure. It’s this proprietary system that embeds U.S. federal banking regulatory guidance into the heart of every agent’s foundation.”

That infrastructure includes model governance, validation and human-in-the-loop reviews — the kinds of safeguards regulators demand before signing off on AI-driven decision making.

Greenlite’s agentic AI-driven solution isn’t meant to replace existing compliance infrastructure, but to layer AI agents on top, relieving operational teams from labor-intensive reviews. These AI agents are built to handle “enhanced due diligence” — the kind of high-touch review that regulators demand for high-risk clients, Lawrence said.

The promise is an increase in investigative capacity, turning a compliance bottleneck into a competitive advantage.

“These high-risk customers might be some of your most profitable,” Lawrence said. “But they require a lot of work to maintain. And if you can’t keep up, you might turn them away.”

Still, what defines a “high-risk” customer, anyway? That, it turns out, depends.

“Some common risk factors include geography — say, countries flagged by the Financial Action Task Force — industry, organization size and customer behavior,” Lawrence said. “But it’s not just about who raises red flags. Often, it’s about how much operational effort is needed to manage that customer.”

The Future of Financial Crime Detection

In one case, a client of Greenlite AI discovered that their systems were misflagging a large portion of Pakistani users due to name matches with the Office of Foreign Assets Control (OFAC) sanctions list.

“There’s a lot of bias baked into these systems,” Lawrence said. “The cost to serve some people is just higher than it needs to be.”

Smarter systems should reduce friction, not just shift it, he said.

Webster illustrated the notion with a common frustration.

“You’re in a different location, trying to log on, and the system flags it,” she said. “But you should know this because I’m here often. These kinds of things should be better in terms of detection and mitigation.”

This is where AI agents can shine, Lawrence said. By intelligently navigating unstructured data to make more context-aware decisions, they can work to reduce unnecessary friction for legitimate users and speed up reviews for compliance teams.

To explain Greenlite’s moment, Lawrence invoked a timeline. The 2000s were defined by rule-based systems. The 2010s ushered in machine learning. But the 2020s?

“Call it the agentic era of compliance,” he said.

The shift has varied implications. Trust is important for regulated financial institutions. Mistakes lead not only to declined transactions; they risk regulatory exposure.

“Right now, banks are getting more risk signals than they can investigate,” Lawrence said. “Digital accounts are growing. Backlogs are growing. Detection isn’t the problem anymore — it’s what to do next.”

“AI is only scary until you understand how it works,” Lawrence added. “Then it’s just a tool — like a calculator. We’re helping banks understand how to use it safely.”

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.

The post The Future of Bank Compliance? There’s an AI Agent for That appeared first on PYMNTS.com.