What Credit Union Leaders Need to Know Before Using AI in Lending Decisions

There’s a tendency to frame AI in lending as a future decision, like something that institutions will adopt once they’ve fully evaluated the risks, selected the right tools, and put governance in place.

That’s not how it’s playing out at all, though. In most credit unions, AI is already showing up inside the lending process in smaller, less formal ways: It might be used to summarize borrower information, compare applications, or flag potential risks. None of that replaces underwriting, and none of it typically shows up as a formal “AI initiative,” but it still changes how decisions get made.

The structure of the decision is shifting

At a surface level, actual lending decisions haven’t changed. An application still gets approved, declined, or escalated, but there is a difference in how the information leading up to that decision is being processed.

Historically, a loan officer would review inputs directly, interpret them, and build a mental model of the borrower. That process wasn’t perfectly consistent, but it was relatively transparent, i.e., you could trace how someone got from the application to the outcome.

AI introduces a layer between the raw information and that interpretation. It organizes inputs, highlights certain factors, and, in some cases, frames how a borrower is perceived before a human fully evaluates the file. Judgement and discernment are still involved, but this changes where they start.

Influence without clear boundaries

One of the more practical issues here is that AI doesn’t need to make a decision to influence it. If a system emphasizes certain risks, pulls forward particular data points, or draws comparisons to past cases, that shapes how a loan officer approaches the application. Even if the final decision is human, the framing is not neutral.

This creates a situation where decisions are still owned by people, but increasingly shaped by systems.

That’s manageable, but only if it’s acknowledged. If it isn’t, you end up with decisions that feel human on the surface but are partially driven by inputs that no one has fully accounted for. That’s where things get harder to explain, and eventually harder to defend.

Explainability becomes operational

“Explainability” tends to get treated like a technical feature. In lending, it’s a practical requirement. If a decision is questioned—by a regulator, internally, or by a member—the institution needs to be able to explain how it was made in a way that is clear and consistent.

Once AI is involved, that explanation has to account for more than just policy and criteria. It has to account for how that information was surfaced, what influenced the interpretation, and how the final judgment was reached. That doesn’t mean every AI-assisted process needs to be dissected in detail. But it does mean leadership needs to be confident that decisions can be traced in a way that holds up under scrutiny.

Where leadership teams get tripped up

Most of the issues here come from using it AI without defining how it fits into decision-making.

That usually shows up in a few ways.

• First, there’s ambiguity around how much weight to give AI-generated insights. If a system flags a risk or suggests a comparison, is that advisory, or is it expected to carry real influence?
  
• Second, decision ownership can get blurry. If a loan officer follows a system’s recommendation and the outcome creates risk, it’s not always clear how responsibility is interpreted after the fact.
  
• Third, different teams may use AI differently. One group may rely on it heavily, while another avoids it entirely. That introduces inconsistency into a process that is supposed to be controlled and explainable.

What needs to be clear before AI goes further

This makes it even more important to be deliberate about how AI fits into a process that already carries regulatory and operational weight.

At a minimum, leadership teams should be clear on a few things.

• Where AI is being used in the lending process: Not in theory, but in practice. What tools are actually being used, and at what stage?
  
• How AI inputs are meant to be treated: Are they suggestions? Supporting analysis? Something closer to a recommendation?
  
• Who is accountable for decisions: This sounds obvious, but it matters more once multiple inputs are involved. The presence of AI doesn’t change accountability, but it can complicate how it’s perceived.
  
• What a defensible explanation looks like: If a decision needs to be explained, what level of detail is expected, and what needs to be included?

AI doesn’t remove human judgment—it does make it more visible

There’s a common assumption that AI will reduce the role of human judgment in lending, but what’s more likely is that it actually increases its role. 

As systems standardize how information is presented and analyzed, the remaining points of discretion become easier to see. When a decision deviates from what a system suggests, that deviation stands out. That raises the bar, a lot, not in a punitive way, but in a practical one.

A more grounded way to think about this

The question for credit union leaders isn’t whether to use AI in lending decisions. That line has already been crossed in most institutions, whether formally or not. The more useful question is how those systems are shaping decisions, and whether that influence is understood well enough to manage.

Lending has always required a balance between structure and discretion. AI just shifts that balance a bit. And…if that shift isn’t addressed directly, it doesn’t stay contained to the tool—it shows up in the decisions themselves.

If this feels familiar, this is exactly the kind of work we’ve been doing with leadership teams through AI BASECAMP—helping them get oriented before decisions start compounding.