What Happens When Examiners Start Using AI Too?

Posted August 6, 2026 · Regulatory / Examination Trends
Jeff L. Bassill, CU Risk Advisors

Nearly everything written about AI and financial institution examinations runs in one direction. NCUA named AI oversight as an examiner focus area in its January 2026 Supervisory Priorities. The OCC and the Federal Reserve have made AI governance a standing topic in every routine exam. All of it asks the same question: how well does your institution govern its own AI use?

Almost nobody is asking the reverse question yet. What happens when the examiner is the one using AI?

A Staffing Reality Worth Naming

In 2025, NCUA ran a Voluntary Separation Program under Executive Order 14210, the federal Department of Government Efficiency workforce optimization initiative. Roughly 250 employees left the agency through a combination of deferred resignation and, for retirement-eligible staff, a separation incentive payment. By the 2026 budget, NCUA's authorized headcount had fallen to around 967, its lowest level since 2003.

That last detail matters more than the raw number. The incentive payment option was aimed specifically at employees old enough to retire, so the departures likely skewed toward the examiner corps with the most institutional experience, not a random cross-section of staff. Whatever replaces that experience over the next few years will, by definition, have less of it.

This is not a critique of the policy's merits. It is simply the operating environment compliance officers are now working within: fewer senior examiners, more staff still building judgment, and an unusually capable set of AI tools arriving at the same moment.

Where the Risk Actually Sits

I have not seen or heard any evidence that examiners are currently using AI to draft findings or documents of resolution. The one recent public comment I could find from an examiner on this topic, from late 2024, described their own generative AI use as still largely theoretical. This is a forward-looking risk, not a documented practice.

But the incentive is easy to see. An examiner, tasked with demonstrating thoroughness but perhaps still building their pattern recognition, has a natural reason to lean on a tool that can produce exhaustive, well-organized analysis in minutes. AI tools are very good at exactly that. Ask one to review a policy or risk assessment and build a supported case for a finding, and it will not stop at the obvious gaps. It will find the third-order ones too, the technically defensible but practically immaterial points a busy, experienced examiner might reasonably let go.

Test it yourself. Take a policy or risk assessment you're comfortable sharing with a commercial AI tool, something with no confidential member data or institution-identifying detail, and ask it to build and support a document of resolution against it. Read what comes back. Most compliance officers who try this are surprised by how deep it goes, and how reasonable each individual point sounds in isolation.

The Standard Already Exists, and It Should Apply Both Ways

Here is the part that's genuinely underdiscussed. Regulators already have a clear standard for AI used in a decision that matters. SR 11-7's model risk framework and the NIST AI Risk Management Framework both require documented validation, human review, and what SR 11-7 calls effective challenge, meaning someone independent has to be able to explain and stand behind the output, not simply pass it along.

That is exactly the standard NCUA and the other banking agencies are now holding credit unions and banks to for their own AI use. The same standard should apply to the exam process itself. Not a ban on examiners using AI tools. A requirement that if one does, a human examiner can independently explain and support every finding that results, the same effective challenge the industry itself is being asked to demonstrate.

What to Actually Do About It

Match rigor with rigor. If a finding arrives unusually exhaustive or deeply documented, the right response isn't a paragraph of disagreement. It's an equally structured rebuttal, your own policy citations, your controls evidence, your risk-based rationale, addressed point by point. AI tools are just as capable of helping you build that response as they are of helping generate the original finding. This ensures a balanced dialogue, regardless of how the initial finding was produced.

Don't let depth substitute for materiality. A long, dense finding feels harder to argue with than a short one, but volume was never the test. Whether something is documented and supported and whether it's actually material to the institution's risk profile are two different questions. Keep asking the second one.

Ask for the same transparency the industry is being asked to provide. If AI tools start shaping how findings get written, NCUA, state leagues, or America's Credit Unions could get ahead of it now with disclosure norms, examiner training standards, or public guidance on if and how it's being used. Better to ask the question before it becomes a surprise than after.

This is still early. But the staffing trend is real, the tools are already capable of this, and the industry is already being told what good AI governance looks like. It's worth thinking through what happens when that same technology sits on the other side of the exam table, before it does.

The views expressed in this post are my own and do not represent the official position of Kings Federal Credit Union. This post reflects publicly available information as of the date above and does not represent a claim that any specific examiner or agency is currently using AI tools in the manner described. It is offered as a risk worth preparing for, not a report of current practice.