New research shows exactly which mortgage industry workers face the greatest risk from AI displacement
For anyone who has spent time inside a mortgage company, the names are familiar: the loan processors who chase conditions, the closers who coordinate with title, the assistants who manage pipeline spreadsheets, the compliance clerks who review disclosures, the data-entry staff who move information between systems.
These workers are the operational infrastructure of the American mortgage machine. They are also, according to a sweeping new analysis from the Brookings Institution and the National Bureau of Economic Research, among the most exposed workers in the U.S. economy to AI-driven job displacement — and among the least equipped to recover from it.
That combination — high exposure, low adaptive capacity — is the finding that should stop every mortgage company executive, branch manager, and originator in their tracks.
The research, in plain terms
The new analysis, published in January 2026 by researchers Sam Manning and Tomás Aguirre of the Centre for the Governance of AI, alongside Brookings senior fellow Mark Muro, takes a different approach than prior AI studies. Rather than simply cataloguing which jobs AI can theoretically perform, it asks a harder question: if a worker loses their job to AI, how likely are they to land on their feet?
To answer that, the researchers built what they call an "adaptive capacity index" for 356 occupations covering 95.9% of the U.S. workforce. The index draws on four factors: a worker's liquid savings, the transferability of their skills to other roles, the density of job opportunities in their local labor market, and their age. Combine that index with standard AI exposure scores and a picture emerges that is both reassuring in places and deeply concerning in others.
Of the 37.1 million workers whose jobs fall in the top quartile of AI exposure, about 26.5 million also have above-median adaptive capacity. Software developers, financial analysts, lawyers — yes, their jobs are highly exposed to AI, but they tend to have savings, broad skill sets, and professional networks that make transitions manageable. The system, for them, has some give.
For 6.1 million workers, it does not. These are people whose jobs are both highly exposed to AI automation and who score in the bottom quartile for adaptive capacity. They have limited savings. Their skills don't transfer easily. They are often older. And they frequently live in smaller cities and towns where the next job simply isn't there. If AI takes their work, the research suggests, the consequences will be lasting.
The occupations concentrated in that vulnerable group read like a staffing directory for a mid-sized mortgage lender.
The mortgage industry's specific exposure
Loan processors, underwriting assistants, compliance clerks, escrow coordinators, closing assistants, data-entry specialists — these roles sit precisely at the intersection of high AI exposure and low adaptive capacity that the Brookings research identifies as most at risk.
Consider the tasks these workers perform daily: reviewing documents for completeness, checking data against checklists, ordering third-party verifications, flagging discrepancies, routing files between departments, preparing disclosures, and coordinating closing packages. These are exactly the kinds of rule-based, information-processing tasks that large language models and AI automation tools are already beginning to perform — and in several cases, perform faster and with fewer errors than human processors working under production pressure.
The insurance industry, a close cousin to mortgage in its administrative structure, is already seeing the numbers move. A Q1 2026 labor market study by The Jacobson Group and Aon found that job openings in finance and insurance fell to their lowest monthly level in a decade by December 2025 — dropping from an annual average of 281,000 openings to roughly 138,000 in a single month. Forty-three percent of industry respondents said they plan to hold staffing steady, a figure that rose 10 percentage points in just one year. Automation was the most common reason cited by companies that reduced headcount.
There is no structural reason to believe mortgage will be immune to the same dynamic. Loan origination software is already automating condition clearing, income calculation, and AUS resubmission. The next wave of tools is targeting processor workflow more directly.
The loan officer is not off the hook
Here is where the research adds a note of nuance that mortgage originators should take seriously and not misread.
Many loan officers, branch managers, and senior underwriters will score relatively well on the adaptive capacity index — they tend to earn more, have broader professional networks, and work in markets with more alternative opportunities. In that sense, they resemble the financial analysts and software developers who, while highly exposed to AI, are reasonably well-positioned to adapt.
But high adaptive capacity is not the same as low exposure. The Brookings analysis is explicit: exposure measures do not predict which workers will be displaced. They predict where AI's labor market effects are most likely to emerge first. And the tasks that define much of a loan officer's role — gathering documentation, explaining product options, calculating payment scenarios, preparing preapprovals, following up on conditions — overlap substantially with what today's AI tools can already do.
The originators who will be most resilient are not those who assume their relationship skills make them immune. They are those who are actively integrating AI tools into their workflow, building the kind of demonstrable AI competency that the labor market is already beginning to price. Research from PwC's 2025 AI Jobs Barometer found that workers with demonstrable AI skills earn on average 25% more than peers without them. That premium is a market signal worth heeding.
A hidden gender crisis
The Brookings research carries a finding that the mortgage industry — like most of financial services — has not yet fully reckoned with. Of the 6.1 million workers identified as facing both high AI exposure and low adaptive capacity, approximately 86% are women.
That figure is not a coincidence. It reflects the occupational sorting that has concentrated women in exactly the administrative and clerical roles most susceptible to large language model automation. In the mortgage industry, the loan processor role is predominantly female. So is the closing coordinator role. So is the compliance assistant role.
The International Labour Organization and Poland's NASK Research Institute found in a major 2025 report that if the jobs most highly exposed to generative AI were to disappear, two women would be displaced for every man. The Gender Snapshot 2025 report found that employed women are nearly twice as likely as men to work in jobs at high risk of automation — 4.7% of women's jobs compared to 2.4% of men's, representing some 65 million jobs for women globally versus 51 million for men.
There is a second dimension to the gender risk that is less discussed but equally important. Research published by Harvard Business School found that women are adopting AI tools at roughly 25% lower rates than men on average. A 2024 Federal Reserve Bank of New York survey found that half of men had used generative AI tools in the previous 12 months, compared with roughly a third of women.
The reasons are complex — ethical concerns about the technology, worry about being judged for relying on AI-generated output, and historically lower exposure to STEM fields — but the consequence is straightforward: workers who do not build AI skills are at greater risk of being replaced by those who do, or by the tools themselves.
For mortgage companies with predominantly female processing and operations teams, this is both a human resources challenge and an operational risk. The staff most likely to be displaced are also, at present, the least likely to be upskilling into AI-augmented roles.
What mortgage originators and their managers should do now
The Brookings research is not a prediction of mass unemployment. It is a targeting tool — a way to identify where the risk is concentrated before the displacement arrives. For the mortgage industry, several practical implications stand out.
Invest in processor and operations upskilling before it becomes urgent. The loan processors and closing coordinators who are retrained to work alongside AI tools — auditing AI outputs, managing exception workflows, handling the complex files that automation struggles with — are far more valuable than those who are simply replaced by automation. Companies that build that transition path proactively will retain institutional knowledge and avoid the recruiting cost of replacing experienced staff later.
Rethink the entry-level pipeline. Many of the roles most exposed to AI automation have historically served as the first rung on the ladder for people entering the mortgage business — positions where new hires learn the business before moving into underwriting, origination, or operations management. As those roles contract, lenders need to think carefully about how they will develop the next generation of experienced professionals. The pipeline does not refill automatically.
Take the geography seriously. The Brookings data shows that vulnerable workers are disproportionately concentrated in smaller markets — smaller cities and towns where independent mortgage companies and regional lenders are most prevalent. For those operators, AI-driven displacement is not an abstraction about large institutions. It is their workforce, their community, and potentially their customer base.
Originators: build AI fluency, not just AI awareness. Knowing that AI exists is not the same as knowing how to use it to qualify borrowers faster, generate pre-approval letters, prepare rate comparisons, or identify refinance candidates in your pipeline. The originators who treat AI as a productivity tool rather than a threat will be the ones writing loans when others are figuring out what happened.
The broader stakes
There is an uncomfortable irony at the heart of this research. The workers most frequently cited in media coverage as being threatened by AI — software developers, lawyers, financial analysts — are largely the ones with the savings, networks, and skills to land on their feet. The workers who will struggle most are those who rarely make the headlines: the loan processor in a mid-sized city, the closing coordinator in a college town, the compliance assistant at a regional lender. Mostly women. Mostly in places where the next comparable job is not around the corner.
The mortgage industry has lived through technology cycles before — the move to digital applications, the rise of automated underwriting, the growth of point-of-sale platforms. Each time, the humans adapted. But the Brookings research is a reminder that adaptation is not equally distributed, and that the industry's responsibility to the people who run its operations does not disappear just because a software company has found a way to automate their tasks.
The data is visible now. The question is what the industry does with it before the displacement arrives.
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