AI in Lending: Why Consumer-Facing Should Be the Last Stop, Not the First
The mortgage industry has no shortage of hype around artificial intelligence. Every week, a new vendor promises borrower-facing chatbots, “AI loan advisors,” and conversational assistants that will supposedly transform the consumer experience. But as the CEO of a lender and the founder of a technology company, I can tell you: starting with consumer-facing AI is the wrong approach.
The stakes in mortgage are simply too high. We operate in one of the most regulated industries in the country, where compliance, data security, and consumer trust aren’t marketing buzzwords — they’re survival. Borrowers are making the single largest financial transaction of their lives. If AI gives them the wrong answer, or even creates the impression that advice is automated and unverified, the result is more than a bad experience — it’s a compliance risk and a reputational disaster.
So where should AI start in lending? Behind the scenes.
Rule-Based AI: The Responsible Entry Point
The least risky and most valuable place to begin is with rule-based automation in the back office. Automating underwriting checks, workflow routing, and document recognition doesn’t make headlines, but it delivers measurable ROI. It cuts cycle times, reduces errors, and allows staff to focus on judgment-based work where humans excel. Most importantly, it can be deployed safely without ever touching the borrower.
This is the “back-office first” philosophy we’ve embraced at Mortgage Automation Technologies (MAT) with The BIG Point of Sale. It’s not about chasing hype — it’s about building a foundation where efficiency and compliance go hand-in-hand.
Generative AI for Staff: Intelligence Without Risk
Once you have the basics in place, the next logical layer is generative AI — but still not consumer-facing. Loan officers, processors, and executives benefit enormously from AI that can interpret data, generate insights, and answer operational questions in plain English.
Imagine asking your system: “What’s our pipeline fallout this month?” or “Who’s my top producer today?” and getting an instant, accurate answer. That’s the value of large language models in the back office: they empower decision-makers, speed up reporting, and remove manual drudgery — all under human oversight.
By keeping these tools internal, you de-risk the technology while proving its value. Borrowers never see the AI, but they feel the impact through faster service and better-informed loan officers.
Moving Carefully Toward the Consumer
Only after AI is proven internally should it begin to reach borrowers. Even then, the first step should be structured and rule-based: document collection, secure data validation, and transactional notifications. These tools improve the borrower experience without risking “hallucinations” or compliance missteps.
The final frontier — consumer-facing generative AI like chatbots and auto-attendants — should come last, not first. Yes, it has potential. But deployed too early, it risks confusing borrowers or replacing the trust they have in their loan officer with a script that sounds clever but isn’t accountable.
Responsible AI Is the Only Real AI
Here’s the truth: there is no shortcut to responsible AI adoption in mortgage. You cannot skip the layers. You cannot push untested AI directly at consumers and hope for the best. If lenders want to protect trust, avoid regulatory pitfalls, and actually deliver value, they need a roadmap:
Rule-based automation in the back office.
Generative AI for staff.
Structured, rule-based consumer tools.
Only then, consumer-facing generative AI.
That’s the sequence. That’s the playbook.
The industry doesn’t need AI theater — it needs AI that works. The companies that understand this will not only protect their borrowers but also position themselves as leaders in efficiency, compliance, and trust. And in an industry like ours, that’s the real disruption.
By Matthew VanFossen, CMB
CEO of Mortgage Automation Technologies & Absolute Home Mortgage Corporation.