AI Reshapes Mortgage Lending: Transparency, Automation and the End of the Paper Chase — Tomo - July 8, 2025

By Staff Reports - July 8th, 2025

The U.S. mortgage industry—long known for its paperwork-heavy processes and lack of pricing clarity—is facing mounting pressure to modernize. As artificial intelligence matures and consumer expectations shift, lenders are rethinking how home loans are originated, processed, and priced.

Traditionally, mortgage lending has been resistant to change. High costs, opaque pricing structures, and a reliance on manual underwriting have persisted despite the rise of digital experiences in retail, travel, and other financial services. But today, AI and automation are beginning to challenge those conventions.

“The complexity of the mortgage process isn’t a feature—it’s a byproduct of legacy systems and institutional inertia,” said Will Begeny, Vice President of True Rate at Tomo, in a recent BizTechReports executive vidcast. “AI gives us an opportunity to decouple the inefficiency from the value.”

Transparency Becomes a Competitive Advantage

One of the clearest signs of industry evolution is the growing focus on price transparency. Historically, it was difficult for consumers to compare mortgage rates in real-time or understand the full cost of borrowing without speaking to a loan officer. Pricing varied not only by borrower profile but by lender discretion—sometimes even from one loan officer to another.

But the stakes are changing. “In 2018, the spread between the best and worst available mortgage rate might have been $80 a month,” Begeny noted. “Now it’s nearly $300. That delta is no longer a rounding error. It’s a material cost.”

This pricing opacity has created an opportunity for AI-powered tools to help borrowers benchmark competitive offers and navigate increasingly fragmented rate structures. Unlike traditional systems that rely on static quoting or manual comparisons, AI platforms can parse thousands of loan combinations and fee structures to generate more accurate, personalized insights.

Automation Moves from Back Office to Front Line

AI’s most visible impact in mortgage lending may be on the operational side. The industry has long been defined by human-intensive processes—underwriting, document review, risk assessment—that drive up costs and introduce delays. Many digital-first lenders in the past decade adopted online interfaces but retained largely manual workflows under the hood.

That’s starting to change. New platforms now use AI to automate document ingestion, detect errors or omissions, verify data across systems, and flag exceptions—dramatically accelerating timelines and reducing reliance on large operational teams.

“The traditional model might see a single staffer handle a handful of loans per week,” Begeny explained. “Today, with automation, some platforms are enabling staff to manage 10 times that volume.”

The operational shift also transforms workforce dynamics. Rather than hiring based on legacy experience, mortgage tech firms are increasingly seeking adaptable talent that can identify inefficiencies and collaborate with AI systems. Roles are being redefined around orchestration and exception management, not repetitive processing.

From Risk Assessment to Exception Management

While most of the early AI adoption in mortgages has centered on automation and convenience, the technology is also beginning to influence risk assessment and underwriting.

Historically, borrower qualification has relied on strict rule sets driven by regulatory oversight—especially in the aftermath of the 2008 housing crisis. But lenders are now experimenting with AI’s ability to streamline data collection and enrich borrower profiles without undermining compliance.

“Most of the innovation so far is on the convenience side—capturing and verifying bank statements or pay stubs automatically,” said Begeny. “But we’re approaching a point where AI may play a larger role in evaluating creditworthiness and pricing risk more dynamically.”

The implications of this shift remain uncertain. Agencies such as the Consumer Financial Protection Bureau (CFPB) are closely monitoring how AI is used in lending decisions, especially when it comes to fairness, explainability, and access. For now, most AI tools remain narrowly focused on improving efficiency rather than altering underwriting standards.

Shifting Consumer Expectations

As in other industries, digital consumer behavior is reshaping expectations in home lending. Borrowers now expect immediate price visibility, faster turnarounds, and fewer administrative burdens.

“There’s an Amazon-effect in play,” Begeny said. “People don’t just want fast—they expect accurate, seamless, and on-demand service. And they want to avoid the runaround.”

AI enables that shift by reducing close times from weeks to days and by eliminating repetitive tasks such as uploading the same financial documents multiple times. It also supports new user experiences, such as chat-based interfaces that answer borrower questions without routing them through layers of customer support.

The net effect is that borrowers increasingly favor lenders who can provide clarity, consistency, and control—something AI-powered platforms are uniquely positioned to deliver.

Ecosystem Complexity Still Limits Speed

Despite its promise, AI’s impact on the mortgage lifecycle remains constrained by the broader ecosystem. Title companies, appraisers, real estate attorneys, and other intermediaries are essential to the transaction and often operate on outdated or fragmented systems.

“Every time we step outside of a modernized workflow—say, for an external appraisal or closing—the process slows down,” Begeny observed. “That variability introduces friction that AI alone can’t solve.”

As a result, the full benefits of intelligent automation won’t be realized until there is broader industry alignment. This has led some players to pursue platform consolidation through acquisitions, aiming to bring more of the transaction in-house. Others advocate for interoperability—designing AI systems that can integrate with a patchwork of external tools and stakeholders.

The long-term viability of AI in mortgage lending may hinge on this question: whether transformation can scale across a decentralized and regulation-heavy ecosystem without requiring a single dominant platform to emerge.

Redefining Economic Models

Perhaps the most profound change AI is driving is economic. Traditional mortgage lenders rely on large salesforces, regional office networks, and human-intensive underwriting models. Each of these elements adds cost—costs that are ultimately passed on to borrowers.

AI-driven platforms are challenging that equation. By minimizing labor and maximizing throughput, they offer an alternative cost structure that is more aligned with consumer expectations and digital convenience.

Begeny estimates that inefficiencies in the mortgage process cost U.S. consumers more than $11 billion annually—through excessive fees, delayed closings, and higher-than-necessary interest rates. “It’s not just lost money,” he said. “It’s lost time. Many borrowers are buying homes a year later than they should because of systemic friction.”

This recalibration of margins and operating costs is forcing incumbents to reassess how they compete—not just on rate, but on speed, reliability, and user experience. For many, AI is no longer a technology play; it’s a strategic necessity.

A Sector at the Tipping Point

Despite regulatory constraints and structural complexity, the mortgage industry is nearing a critical inflection point. With AI delivering meaningful gains in efficiency, transparency, and borrower satisfaction, the pressure to modernize is rising—from both consumers and competitive forces.

What remains unclear is how quickly the rest of the industry will adapt. Real estate agents, small banks, credit unions, and regional title firms will all need to reimagine their roles in a more automated, data-driven environment.

Still, Begeny is optimistic. “You don’t have to blow up the system to make progress,” he said. “But you do need to design tools that others want to use—tools that make things easier, not harder.”

In a market where trust, cost, and speed all matter, the mortgage industry may finally be catching up to the digital economy it serves.