Three Moves Lenders Should Take Now To Stay Ahead Of Ai Regulation

Mortgage lenders don’t have the luxury of waiting for AI regulations to settle. While states and Washington spar over who sets the rules, lenders remain fully accountable for how artificial intelligence is used in underwriting, servicing, marketing and fraud detection. The question is no longer if AI will be regulated; it’s whether lenders are ready when scrutiny lands.

Here are three moves lenders should take now to protect themselves, scale responsibly and avoid becoming test cases for regulators.

1. Build real AI governance, not just a policy document

AI risk management cannot live in a slide deck. Lenders need a formal governance framework that inventories every AI-driven tool in use, documents how models are trained and defines who is accountable for outcomes.

That includes understanding data sources, monitoring for drift and bias and establishing escalation paths when AI outputs affect borrower eligibility, pricing or disclosures. Regulators are signaling that “we rely on a vendor” will not be an acceptable defense. If AI touches a consumer outcome, lenders will own the risk.

Just as important, governance must be operational, not theoretical. Compliance teams, legal, IT and business leaders need shared visibility into where AI is deployed, how decisions are made and how exceptions are handled in real time. When governance is disconnected from day-to-day workflows, issues surface only after harm occurs, which will be exactly the moment regulators and plaintiffs’ attorneys start paying attention.

2. Rewrite vendor oversight before regulators do it for you

Most existing vendor contracts were not written for AI scrutiny. Lenders should be tightening agreements now to address training data ownership, audit rights, bias testing, explainability and data segregation.

State laws already require lenders to explain automated decisions and document risk assessments, even when AI is supplied by third parties. If vendors cannot provide transparency or testing artifacts, lenders will be exposed. Vendor oversight is quickly becoming a core compliance function, not a procurement exercise.

This also changes how lenders should evaluate technology partners going forward. AI readiness is about governance maturity. Vendors that cannot demonstrate responsible model development, ongoing monitoring and regulator-ready documentation will slow lenders down, not speed them up. In a fragmented regulatory environment, the wrong vendor can become a compliance liability overnight.

3. Scale AI deliberately, not everywhere at once

AI does not have to be all-or-nothing. The smartest lenders are starting with lower-risk use cases, such as document classification, workflow automation and fraud detection, while maintaining human oversight in high-impact decisions.

This staged approach allows lenders to demonstrate responsible use, collect performance data and refine controls before expanding AI deeper into credit and eligibility workflows. Automation reduces effort, but it does not reduce accountability.

It also creates an evidence trail that regulators increasingly expect to see. By rolling AI out incrementally, lenders can document performance benchmarks, exception rates, override patterns and fairness testing over time. That data becomes critical when examiners ask not just what AI is doing, but why it was deployed, how it is monitored and when humans intervene. 

Lenders that treat AI adoption as a controlled program rather than a blanket rollout will be better positioned to defend outcomes when scrutiny intensifies.

Why mortgage AI carries higher stakes

AI runs on data, and in mortgage lending, that data is personal, sensitive and regulated. Compliance regimes like RESPA, TILA and TRID demand precision, explainability and strict timelines. Introducing AI into these workflows without governance doesn’t eliminate risk; it magnifies it. Small data errors can quickly become compliance violations at scale.

That reality is driving increased regulatory scrutiny of automated decisioning, particularly around fair lending, transparency and consumer impact. Opaque models are no longer acceptable, and “black box” explanations will not survive examination.

A fragmented rulebook, for now

In the absence of federal law, states moved first. California expanded its privacy regime to cover automated decision-making. Colorado enacted the nation’s first comprehensive AI law targeting “high-risk” systems, including credit eligibility tools. Other states are following suit, creating a patchwork of obligations that is difficult for national lenders to manage.

That fragmentation may not last. In December 2025, President Trump signed an executive order directing the federal government to establish a unified national AI framework and challenge state laws deemed to impede innovation. Legal battles are likely, but the direction is clear: federal standards are coming.

Compliance is becoming a trust test

AI regulation is entering a volatile phase. States are asserting authority. Washington is pushing back. Courts will decide the boundaries. Through it all, lenders remain responsible for outcomes.

In the AI era, compliance is no longer just about meeting technical requirements. It’s about trust with regulators, investors and borrowers. Lenders that act now, govern deliberately and scale responsibly won’t just keep up. They’ll help define what compliant AI in mortgage lending looks like next.

Geoffrey Litchney is managing regulatory counsel and director of compliance at Dark Matter Technologies. As an expert in federal and state lending regulations, Litchney’s work focuses on transforming legal, regulatory and privacy requirements into practical, business-ready solutions that responsibly drive innovation. He can be reached at geoffrey.litchney@dmatter.com.

This column does not necessarily reflect the opinion of HousingWire’s editorial department and its owners. To contact the editor responsible for this piece: zeb@hwmedia.com.