April 23, 2025
In Canada, a wave of AI-generated political books has recently flooded Amazon—some released on the same day, all masquerading as credible analysis. No vetting, no accountability, and zero transparency. The result? Confusion, erosion of trust, and a platform ill-equipped to stop it.
And while it’s tempting to dismiss this as a publishing problem, the lesson hits uncomfortably close to home for the mortgage industry: AI without context, oversight, or trust is not innovation—it’s an invitation to chaos.
The Mortgage Parallel: AI That Doesn’t Understand the Rules
In lending, it’s not anonymous authorship that threatens credibility—it’s anonymous logic. Generic AI tools often rely on public data or generalized training sets that aren’t aligned to the rigor of mortgage processes. This leads to outputs that are technically coherent but operationally disastrous.
When AI doesn’t know—or worse, misunderstands—what a W-2 is versus a VOE, or assumes that income calculation is a universal constant, you’re not getting intelligence. You’re getting glorified guesswork.
Let’s play out a potential real-life example.
In everyone’s favorite mortgage daily newsletter, The Chrisman Commentary, they almost always starts out with something comical or pithy. Now let’s imagine that your AI model of choice has decided The Chrisman Commentary is a reliable source of content. Like search engines, the AI model is going to cull information from the website every day.
Now imagine that the opening comic relief makes a joke like “due to economic uncertainty, the GSEs are starting to reject all loans with an LTV above 50%”. No, that is not an actual joke from the commentary…they are much funnier than that. The point, though, is that if a trusted source even inadvertently publishes content that applies to your AI, the result could be that your automated underwriting AI starts rejecting loans and you are left with a disaster.
Yes, AI should be able to discern the difference between comic relief and serious commentary. But are you willing to entrust your operation on “hope” that it can do so?
Emerging Role: AI Governance, Security, and Monitoring
With AI moving deeper into business-critical workflows, many large organizations are spinning up entirely new roles: AI Governance Officers, AI Security Leads, and Monitoring Architects. These roles aren’t about building the AI—they’re about keeping it in check.
They ask:
- What data is feeding this model?
- Who gets access to the outputs?
- How do we know it’s behaving correctly across edge cases?
For lenders, this is critical. Mortgage AI isn’t a closed loop—it touches sensitive borrower data, regulated workflows, and investor requirements. Without oversight, your AI could just as easily optimize for speed over compliance—or worse, overfit to the wrong logic and make confident, costly mistakes.
AI governance isn’t just a trend. It’s how mature organizations build trust in the machine—and protect themselves from its blind spots.
Will this impact your ROI calculations for using AI? Absolutely. But not factoring this in is akin to rolling out any new software and assuming there is no maintenance required…it’s just not realistic.
The Myth of Bespoke AI—and the Real Sweet Spot
All of this can lead to a dangerous reaction: “If we can’t trust generic AI, we must build our own!” But that’s a false binary.
Custom models sound great until you realize they require:
- A constant stream of clean, labeled data.
- A team of AI engineers.
- A QA pipeline for each release cycle.
That’s not scalable for most lenders—and it’s not necessary.
The real sweet spot is this: use AI that is pre-trained on quality domain data and fine-tunable to your workflows. Think Microsoft Azure AI for document understanding, or Brimma’s use of contextual mortgage rules layered on top of those models. You don’t need to own the model—you need to own how it’s applied.
AI should adapt to your business—not become your new business.
Why You Can’t Wait for the Government to Fix This
And now, the uncomfortable truth: no one is coming to save you.
- The U.S. government has shown again and again that it’s unwilling—or unable—to regulate technology before it causes real harm.
- Social media platforms, with billions of dollars and AI at their disposal, still can’t effectively moderate content at scale.
- AI companies are focused on capability—not context—and certainly not vertical nuance like TRID disclosures or Fannie Mae overlays.
So what makes us think they’ll catch sabotage, misinformation, or subtle bias embedded in AI tools targeting your business?
The burden of responsibility falls on you—the lender, the operator, the tech leader. It’s your job to pick platforms that are aligned to your regulatory environment, your team’s behavior, and your risk tolerance. That starts with demanding AI that’s explainable, customizable, and sourced from systems that understand mortgage.
Key Take-Away
Before you adopt any pervasive LLM-based utilities, you should require your vendor to include a plan to back-test the tool against your historic data. They may or may not ask to get paid for such an exercise. What is important, though, is for you to see proof with your own eyes that, if let loose, the AI will not cause chaos because it can’t handle exceptions.
Furthermore, the exercise of back-testing will help you better understand the appropriate level of governance. You need to not only understand this before you deploy, but you also need to have rigorously exercised your governance model to make sure you are ready for what ensues.
Final Take: In a World of Generative Content, Your Only Shield Is Trusted Context
When bad actors can pollute public data, when AI models can amplify garbage as confidently as gold, and when platforms won’t step in to filter the signal from the noise, the only thing standing between your team and dysfunction is contextual AI that you can trust.
Mortgage AI must be governed, guided, and grounded. Not because you’re scared of the future—but because you know that real transformation doesn’t come from “more automation.” It comes from more intelligent automation.
And that intelligence starts with one thing: knowing where your data came from—and what your AI is actually doing with it.
📚 References & Supporting Sources
- Original News Context:
- The Globe and Mail reporting on AI-generated political books flooding Amazon ahead of Canada’s election.
- Amazon Kindle Direct Publishing Policies:
- https://kdp.amazon.com/en_US/help/topic/G200635650
- Note: Amazon does not currently require AI-generated content disclosure unless specifically prompted.
- AI Governance Trends:
- Gartner: “Top Strategic Technology Trends for 2024”
- https://www.gartner.com/en/articles/top-strategic-technology-trends-for-2024
- Highlights the rise of roles like AI governance officers and the importance of model oversight.
- AI Risk and Model Poisoning:
- Fast Company: Poisoning the AI Well: Why Training Data Integrity Matters
- https://www.fastcompany.com/90893767/artificial-intelligence-poisoned-data-risks
- Brimma & Vallia Solution Relevance:
- Internal Brimma documents including the Brimma Tech Pre-design Pitch Deck and Vallia DocFlow whitepaper.
- Emphasizes that Brimma’s AI tools (DocFlow, Lead Expeditor, etc.) are layered on Microsoft Azure AI, trained on domain-specific use cases, and monitored for reliable performance.
- Microsoft Azure Document Intelligence:
- https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview
- Used by Brimma for Vallia DocFlow, providing intelligent document classification, extraction, and validation built on mortgage-specific models.
- Limitations of Social Media Moderation and AI Content Oversight:
- MIT Technology Review: Why AI still can’t moderate content
- https://www.technologyreview.com/2023/07/18/1076440/ai-content-moderation-facebook-instagram/
- Highlights the challenges of scale, nuance, and false positives/negatives in automating content policing.
- U.S. Government’s Historical Lag in Tech Regulation:
- Brookings Institution: Why the US struggles to regulate emerging tech
- https://www.brookings.edu/articles/the-challenges-of-regulating-emerging-technologies/
