Lenders Are Under Pressure to Speed Up. AI Is Starting to Deliver

Written by:

Steve Marcinuk

Date:

15 Jul 2026

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The mortgage industry has long been one of the more paper-heavy corners of real estate. Loan applications move through multiple hands, documents get reviewed and re-reviewed, and data gets entered manually into systems that have been around for decades. Industry research puts the average closing timeline at over 40 days per loan. A growing number of fintech companies are now targeting that inefficiency directly, and Fintor is among the more focused players in that space.

Originally launched as a fractional real estate investing platform, Fintor has since pivoted to building what it describes as an AI-native mortgage operating system. The company works primarily with mortgage lenders, integrating with the legacy loan origination systems they already use and layering automation on top of the manual steps that slow things down.

Benjamin Gerochi, an AI engineer and solutions architect at Fintor, describes the core problem: a borrower’s application passes through multiple levels of verification, with one person extracting data from documents, another confirming completeness and accuracy, a third entering it into loan origination systems, and yet another checking the work again. Each layer adds time without necessarily adding judgment.

That kind of layered redundancy exists for good reason. The 2008 financial crisis exposed what happens when mortgage origination moves too fast without adequate checks. The regulatory environment that followed is strict, and lenders operate within tight compliance requirements. Fintor’s goal is not to remove those checks but to automate the parts that don’t require human judgment, so the people involved can focus on the parts that do.

Automation Versus Augmentation

One of the more useful distinctions Gerochi draws is between automation and augmentation. Full automation means handing a process entirely to AI. Augmentation means using AI to make the process faster and more accurate while keeping people in control of the decisions that matter.

Fintor sits firmly in the augmentation camp. The company’s tools assist loan officers with data extraction and document review, but those officers still give final approval and ensure accuracy. “At the end of the day, they’re still the ones that give the final approval; they’re still the ones that ensure that everything is accurate,” Gerochi says.

This distinction matters in a regulated industry. A mistake in a borrower’s application can delay or derail a home purchase entirely. Fintor has responded by building what it calls a mortgage guardrails framework, a layer distinct from the AI systems doing the extraction and review work. Rather than relying on one model to check another’s output, safety-critical decisions run through compiled rules and policy-as-code, which makes the checks consistent, auditable, and reproducible in a way that model-on-model verification isn’t. Those rules are mapped directly to the regulatory surface lenders are accountable to, ECOA, Fair Housing, GLBA, CFPB guidance, the Freddie Mac Selling Guide, the NIST AI Risk Management Framework, and relevant state AI laws, and are designed to flag anything that falls outside expected parameters. The framework ships as drop-in adapters, so lenders can add this layer without touching their existing agents or workflows.

The company also maintains a human production team, but not as a blanket re-check of everything the AI produces; that would just relocate the labor rather than remove it, and invite automation bias, where reviewers start rubber-stamping confident-looking AI output instead of genuinely verifying it. Instead, Fintor uses a confidence-scoring approach: extractions the system is confident about pass through, and only low-confidence outputs get routed to a human reviewer. “We have AI that extracts the data, but then we also have a production team that goes and ensures that those outputs are correct,” Gerochi notes, but the efficiency gain comes specifically from that selective routing, not from humans checking everything twice. The operating system framing, rather than positioning the product as a standalone AI tool, reflects this hybrid approach.

Where Lenders Are Hesitant

Adoption of AI in mortgage lending is uneven, and the pattern Gerochi describes is recognizable across many industries facing similar transitions. At the executive level, there is real pressure to move. Processing speed is becoming a competitive differentiator, and standing still is not a neutral choice.

But in the middle layers of lending organizations, the reception is more cautious. Current AI models are stochastic – their outputs involve probability rather than certainty. In a regulated environment, that creates legitimate concern. “At the middle level, there is a lot of skepticism, because it’s still AI, and AI still makes mistakes,” Gerochi says.

The people closest to the actual work tend to be more receptive. Loan processors and data entry staff, who spend their days moving information between documents and systems, are often the most willing to hand off that work. “A lot of the things that we are automating with AI are the things that they would rather let go of,” Gerochi observes.

The bigger obstacle to closing deals is often organizational readiness rather than skepticism about the technology itself. Established lenders have standard operating procedures that work. They are not broken, just slow. Convincing an organization to change a functional process – even to a faster one – takes time and internal alignment, which do not always come quickly.

Positioning Against Legacy Systems

Rather than competing with entrenched loan origination platforms like Encompass, Fintor positions itself as a complementary layer. Those systems are deeply embedded in lender workflows, carry regulatory ties, and are not going to be displaced. Fintor handles the steps that those platforms were not designed to automate.

“These loan origination systems aren’t going anywhere,” Gerochi says. “We’re integrating with them and figuring out the best approach to make everyone’s lives easier.”

Speed and proximity are real advantages, but Fintor’s own framing concedes their limits: larger, more established platforms move slowly by necessity, but that gap narrows as they catch up. The more durable asset is what accumulates underneath the speed: proprietary workflow data. Every lender’s edge cases, every quirk in a given loan origination system, every failure mode encountered and handled becomes something Fintor has seen and larger competitors haven’t. That kind of data compounds; it’s not a temporary head start so much as a growing body of specific, hard-to-replicate knowledge about how mortgage workflows actually break. Fintor’s team sitting directly with lenders and building around what they find difficult isn’t just a service differentiator, then; it’s the mechanism by which that data gets collected in the first place. It’s worth noting that Fintor is one of several companies pursuing this space; others include Blend, Tavant, and Ocrolus, each with different approaches to layering automation onto existing mortgage infrastructure, and presumably accumulating their own versions of this same asset.

The Broader Implication for Real Estate

For real estate investors, closing speed is a negotiating weapon, not just a convenience. Sellers in multiple offer situations favor the buyer most likely to close fast and clean, which is a big part of why cash wins. A financed buyer who can credibly close in two weeks instead of six competes against cash on something other than price, and the cost of delay is concrete: rate lock extension fees, seller per diem penalties, and, for flippers and developers, carrying costs that accrue daily.

That said, most of the 40-day average closing timeline is borrower-side document collection and third-party steps like appraisal, title, and employment verification, not the clerical data movement platforms like Fintor target. The 7 to 10 day closings that already exist are mostly cash deals that skip the appraisal. What this kind of automation realistically shrinks is the lender-controlled portion of the process, so staying organized on the borrower’s end still matters, and a lender’s tech stack is now a real factor to weigh when choosing who to work with.

The AI safety architecture described earlier matters here as reassurance more than compliance detail: AI handles the clerical grind while humans still own every consequential decision, so a faster file isn’t a riskier one. The technology is still maturing, and the regulatory environment means that any gains must be achieved carefully. But the direction is clear: lenders that figure out how to integrate these tools effectively will gain an operational advantage over those that wait. The question is less whether AI will play a role in mortgage processing and more about how quickly organizations can adapt their workflows to accommodate it without compromising the compliance standards that protect borrowers.

About the Expert: Benjamin Gerochi is an AI engineer and solutions architect at Fintor, a company building an AI-native mortgage operating system for lenders that integrates with existing loan origination platforms.

This article is based on information provided by the expert source cited above. It is intended for general informational purposes only and does not constitute legal, financial, or real estate advice. Readers should conduct their own research and consult qualified professionals before making any real estate or financial decisions.