5 Ways to Evaluate AI in Your Mortgage LOS (sponsored by MeridianLink)

Mitch Swanson is Senior Product Manager with MeridianLink

The conversation about AI for mortgage loan origination systems has fixated on the wrong question. Lenders are debating whether a platform was “built on AI” or “added AI later”—arguing over codebase age, proprietary models, and architecture. But architecture is not what predicts whether AI will help your team close loans faster.

I’ve seen this from both sides. I started in the mortgage industry in 2012 as a loan officer assistant, then a processor—pulling conditions at 5 p.m. on a Friday and chasing borrowers for the missing page of a bank statement. Today I build AI products for mortgage document processing with MeridianLink^®. That transition taught me that the marketing conversation around AI has almost nothing to do with what determines whether the technology works.

Here are five questions that do.

1. Does the AI make your staff faster, or just create different work?

Vendors lead with accuracy metrics. “The model identified the document correctly 94% of the time, precision was 96%.” Those numbers are meaningful, but they tell you little about whether processors will move faster.

A highly accurate AI can still create more work.

A processor needs a clear signal: accept or reject, specific reasons, and a fast path to the next action. If AI generates a multi-paragraph analysis that the processor has to read to find the answer, it hasn’t eliminated the work. It’s simply moved it somewhere else.

Ask the vendor to show you the workflow. Watch a processor use the system. Ask how long it takes to act on an AI finding. That will tell you whether the technology was built for a pitch deck or for the people who will use it.

• What happens when AI is wrong, and can you prove it?

Regulators have raised the stakes here. Fannie Mae’s Lender Letter LL-2026-04 requires lenders to hold their technology vendors to the same AI governance standards lenders themselves must meet. That means your vendor’s governance posture is now your compliance exposure. You need to prove what AI did, why it did it, and when a human overrode it. That audit trail is no longer optional.

When evaluating AI solutions, look for evidence, not just assurances:

• Does the vendor have a formal AI governance body?

Not a marketing page. An actual committee with a charter, cross-functional membership, and regular meetings.

• Is the human-in-the-loop?

In lending, AI should suggest. Staff should decide. If AI acts autonomously on anything that touches a loan file, you need to know exactly where that autonomy boundary is.

• Is every AI action logged?

Can those logs be compiled into an audit-ready report per loan? Do you know how the system reviewed the document, evaluated it, wrote data to the URLA, or when a user overrides the output?

• Is AI scoped away from credit decisioning?

There’s a meaningful difference between AI that evaluates document quality and AI that influences loan approval. The boundary should be explicit, not implied. Look up Massachusetts vs Earnest Operations LLC if you want to understand why this matters.

• Has the framework been reviewed against LL-2026-04 specifically?

The policies and procedures for development, implementation, use, and maintenance of any AI/ML must have risks mitigated.

• If they claim proprietary AI, what data trained it?

“We have proprietary AI trained specifically for lending” is a common claim. But building and running your own AI infrastructure is a significant undertaking. Whether you buy the hardware or rent it in the cloud, you need computing power, storage, networking, and software to train and run the models.

For a vertical SaaS company serving one industry, the math for those requirements rarely holds.

Then there’s the data problem. Training a lending-specific model requires a large corpus, but consumer-consent requirements make it difficult to use production loan data for training without explicit borrower consent, which most institutions never collected on past loans. That’s a regulatory wall, not a technical hurdle.

Next time someone tells you they have proprietary AI trained on lending data, ask two questions. What data was it trained on? And do you have consumer consent for that use? The answers will tell you whether you’re hearing a technical claim or a marketing claim.

• How deep is the integration ecosystem?

A lender doesn’t just buy an LOS; they buy into an ecosystem—credit bureaus, AUS and pricing engines, doc prep, title, AMC, fraud detection, CRM, POS, core banking, etc. Each integration represents years of edge-case handling and compliance validation that never appears in a demo but absolutely shows up when something breaks in production.

AI functionality may get the attention, but the ecosystem underneath it is critical. Integration depth, compliance infrastructure, and platform coverage all determine how well the technology can support a real lending operation.

That depth takes time to build. When a newer vendor announces a single integration, it’s headline news. For an established platform, it’s part of a normal week. That gap tells you something important about the maturity of the platform and its ability to support your business.

So don’t just evaluate what AI can do. Evaluate the foundation underneath it. Ask how many production-certified integrations the platform has, how long the most recent one took to build, and whether it can support your full lending footprint.

• Does AI cover your full lending operation, or just one channel?

This cost is routinely underestimated.

When you choose an AI solution that covers only one lending channel, you’re not just choosing which vendor AI to use. You’re choosing to maintain separate systems of record. Separate compliance frameworks. Separate vendor relationships.

If your institution does mortgage and consumer lending, a mortgage-only platform—however impressive its AI—creates a new fragmentation problem. 

The platform decision isn’t just about what AI can do. It’s about whether the platform can support your full lending footprint as one connected operation.

Map what you need today and where you expect your lending business to be three to five years from now. Then ask: Can this platform support that vision as one operation, or will it leave you managing separate systems?

The real platform decision

An LOS isn’t just the technology you use. It’s the foundation everything else sits on.

The AI layer is modular. It can be upgraded, replaced, and expanded. But the system of record, the compliance infrastructure, the integration ecosystem? Those can’t be easily replaced. Those decisions lock you in for years.

That’s the real platform decision. Not which vendor has the newest code. But which one has the foundation your AI will run on.

The five criteria in this article are designed to help you move past the architecture debate and focus on what actually matters. The answers won’t be obvious from a demo or a feature comparison slide.

See how MeridianLink puts AI to work the way lenders need with a stronger platform foundation and embedded intelligence.

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The materials available in this article are for informational purposes only and not for the purpose of providing legal advice. You should contact your own advisors with questions regarding the AI in lending content herein. The opinions expressed in this article are the opinions of the individual authors and may not reflect the opinions of MeridianLink, Inc.

(Sponsored content includes material submitted independently of the Mortgage Bankers Association and MBA NewsLink and does not connote an MBA endorsement of a specific company, product or service. For more information about sponsored content opportunities, contact Bill Farmakis at bill@jlfarmakis.com or 203/834-8832.)