Why Your AI Needs a Feedback Loop (And Why No One Wants to Give It One)…Hey AI…DO BETTER!!!
February 28, 2025
AI is supposed to be the future of mortgage lending, right? It promises to streamline underwriting, reduce inefficiencies, and make loan officers and underwriters more productive. Yet, there’s one massive problem: AI doesn’t know when it’s wrong. And the only way it can know is if humans bother to correct it. Spoiler alert—most of them won’t.
The mortgage industry loves talking about automation, but when it comes to actually integrating AI into workflows, there’s a crucial step that gets ignored: feedback. Without a structured, intuitive way for AI to learn from human input, it becomes just another overhyped tool that produces results no one trusts. AI needs feedback loops. But getting people to actually engage with them? That’s an entirely different battle.
The Big Problem: Humans Are Lazy (And Also a Little Paranoid)
Let’s be honest: No one wakes up in the morning excited to train their AI overlord. Mortgage professionals—loan officers, underwriters, compliance specialists—already have enough to do. The idea that they should take extra time to teach an AI system how to do its job better is, frankly, laughable.
It’s not just laziness, though. There’s also fear. AI is often framed as a job-stealing machine that’s coming for everyone’s paychecks. Why would an underwriter invest time refining an AI model when they’re worried that same model will replace them next year? If people see AI as competition rather than a tool, they’re not going to help it get better.
And even if they want to engage, most feedback systems are so clunky and tedious that users give up before they even start. If AI wants to learn, it has to collect feedback effortlessly.
The Myth of the Perfect AI (And the Reality of Edge Cases)
Mortgage lending isn’t a one-size-fits-all industry. Sure, AI can handle the basics—income verification, debt-to-income calculations, fraud checks—but the second it hits an edge case, it stumbles. And in mortgage lending, edge cases aren’t the exception; they’re the rule.
Take underwriting, for example. No AI system will ever be 100% right, 100% of the time. There are too many variables—borrower history, property details, regulatory quirks—that require human judgment. But here’s where AI can still be valuable: instead of making a “yes” or “no” decision, it can generate a draft underwriting assessment that an actual human refines. This way, underwriters still control the decision-making process, but they start with AI-organized data rather than a blank slate.
Brimma’s Vallia AUS Sandbox operates on this principle. It doesn’t pretend to be perfect. Instead, it classifies and validates loan documents, but when something seems off, it doesn’t just flag an error—it lets underwriters edit directly within the system. And those edits? That’s the feedback loop. Every correction subtly teaches the AI how to improve without requiring users to jump through hoops.
Designing Feedback Loops That Don’t Annoy Everyone
If AI is going to get better, it needs a steady stream of high-quality feedback. But here’s the trick: The feedback system has to be so simple that users don’t even realize they’re providing it.
A HousingWire study on AI adoption in financial services found that the most successful AI implementations used passive feedback collection rather than requiring users to submit formal reports. Instead of making people tell AI when it’s wrong, smart systems observe behavior and adjust accordingly.
Brimma’s Vallia AUS Sandbox does exactly this. It suggests three loan scenarios for a borrower, but it never explicitly asks for feedback. Instead, it watches what happens:
- Did the loan officer accept one of the suggested scenarios?
- Did they tweak the inputs and run their own version?
These actions are the feedback. The AI doesn’t need a separate survey or rating system—it just learns from what the user actually does.
The key takeaway? AI should be designed to gather feedback passively. If users have to stop what they’re doing to train the system, it’s not happening.
The Right Way to Integrate AI into Mortgage Workflows
The more embedded AI is in a user’s workflow, the more likely they are to interact with it—and the more feedback it will receive. AI that lives in a separate portal, requiring users to switch screens and manually input corrections, is doomed from the start.
For AI to succeed, it must:
✔ Be directly integrated into existing platforms: Loan officers and underwriters shouldn’t have to “go to the AI”—it should be right where they already work.
✔ Frame itself as an assistant, not a boss: AI shouldn’t dictate decisions but should instead provide structured insights that professionals can refine.
✔ Make feedback a natural part of the workflow: Edits and modifications should be captured as feedback automatically, without extra steps.
✔ Show immediate improvements: If users provide feedback, they should see the AI adjust quickly. If changes take months to reflect, users will stop engaging.
Why Simplicity Wins (And Why No One Wants a Feedback Form)
One of the biggest mistakes in AI implementation is overcomplicating feedback. If you give mortgage professionals a multi-step form to report errors, forget it. They’re too busy closing loans.
A McKinsey & Company report on AI in financial services found that companies with simple feedback mechanisms—like a thumbs-up/thumbs-down model or inline comment editing—saw 35% higher engagement with their AI systems compared to those with complex feedback workflows.
Think about AI-powered chat interfaces. The best ones don’t just ask, “Was this helpful?” and stop there. When a user gives a thumbs-down, the system follows up with, “What was wrong?” and offers quick options like “Incomplete information” or “Incorrect calculation.”
The same principle applies to mortgage AI. If underwriters or loan officers reject an AI suggestion, the system should ask why in the most frictionless way possible. And if that feedback isn’t reflected in the AI’s behavior quickly, people will stop giving it altogether.
Final Thoughts: AI Needs Mortgage Lenders More Than Mortgage Lenders Need AI
AI in mortgage lending isn’t inevitable. If AI systems aren’t built with user-friendly feedback loops, they won’t be adopted—plain and simple. Lenders will ignore them, override them, or use them only for the most basic tasks.
The solution isn’t better AI. It’s better AI design so AI CAN DO BETTER—systems that integrate seamlessly, learn passively, and don’t demand extra effort from already busy professionals.
Mortgage lenders don’t need AI that tells them what to do. They need AI that listens.
