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Loan interviewers and clerks

Also called Loan clerk, Mortgage loan processor, Loan processor, Loan interviewer, Mortgage clerk

In brief

In May 2025, BLS counted 164,790 US loan interviewers and clerks, with median pay of $50,020 a year. Of the BLS counts since 2021, the highest was 242,630, in May 2022. The earliest figure here is an estimate of 15,000 from 1890, not measured the same way as today's count. The last full technology era for loan interviewers and clerks: Enterprise LOS platforms, 2004 to 2018.

What the sources say

Official outlook

BLS projects employment to decline 2.4% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

  • 50% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
  • 0.92 estimated probability that the whole occupation could be computerised, over an unspecified number of years. Not a forecast of job losses. Frey and Osborne, 2013

Since the 2013 prediction

In 2013, Frey and Osborne put this job's probability of computerisation at 0.92. BLS counted 213,270 of these jobs in May 2013 and 164,790 in May 2025. The count moved sharply in one step, May 2020 to May 2021, when BLS changed how it estimates these counts, so no change is derived here.

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Loan interviewers and clerks: jobs and pay since 1890 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/43-4131

Chicago

Future History. "Loan interviewers and clerks: jobs and pay since 1890." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/43-4131.

BibTeX

@misc{futurehistory_43_4131, author = {{Future History}}, title = {Loan interviewers and clerks: jobs and pay since 1890}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/43-4131}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
187519001925195019752000now
2026
Known then as Mortgage Clerk / Building Society Clerk (savings bank era)
Known then as Loan Clerk / Mortgage Loan Clerk (building-and-loan era)
Known then as Loan Processor / Loan Interviewer (postwar consumer-credit expansion)
Known then as Mortgage Loan Processor / Loan Document Specialist (HUD/RESPA era)
Known then as Loan Interviewers and Clerks (BLS SOC 43-4131)
Known today as Loan Interviewers and Clerks (BLS SOC 43-4131)
Projected to still be called Loan Interviewers and Clerks (BLS SOC 43-4131)
Data from the May 2025 survey
US Employment
165K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$50,020
Est.

≈ $48,738 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1934

The National Housing Act of 1934 creates the Federal Housing Administration (FHA) and for the first time introduces standardized federal underwriting forms and credit criteria for residential mortgages. For loan clerks, this transforms the role from institution-specific ad hoc processing into a federally structured workflow: every FHA loan now requires specific appraisal reports, credit verification forms, and documentation checklists. The FHA's standardization is the single most important regulatory event in the history of the loan processing occupation, because it made the work trainable at scale and created the conditions for mass employment of processing clerks across thousands of savings institutions.

Beat · 1944

The Servicemen's Readjustment Act of 1944 (the GI Bill) authorizes the Veterans Administration to guarantee home loans for returning servicemen at low interest rates with no down payment. By 1952, approximately 2.4 million GI Bill home loans had been made. The sheer volume of applications -- millions of returning veterans seeking homes in suburbs that barely existed yet -- created an acute shortage of mortgage processing staff. Savings and loan associations, commercial banks, and VA-approved lenders hired processing clerks in volume throughout the late 1940s and early 1950s, cementing the occupation as a significant category of clerical employment in the finance industry.

Beat · 1974

The Real Estate Settlement Procedures Act (RESPA) of 1974 requires lenders to provide borrowers with a Good Faith Estimate of settlement costs within three business days of application and a HUD-1 Settlement Statement at closing. Combined with the Truth in Lending Act (TILA) disclosure requirements already in force, RESPA substantially increases the per-loan documentation burden: clerks must now prepare and deliver additional federal disclosure forms at defined points in the process. RESPA is often cited by industry observers as the event that most expanded the clerical headcount per loan originated, because each new form must be prepared, dated, delivered, and retained in the file.

Beat · 1994

Fannie Mae launches Desktop Underwriter (DU) in 1994, the first automated underwriting system in the US mortgage market. Freddie Mac follows in 1995 with Loan Prospector (LP). Before AUS, a loan processor assembled the entire file and submitted it to a human underwriter, who might take days or weeks to issue a decision. With DU/LP, the processor can get an automated approval in minutes and receive a precise list of required documentation conditions. This changes the job from open-ended file assembly to condition-driven document collection -- a more predictable workflow that also proves easier to further automate in subsequent technology generations.

Beat · 2008

The financial crisis of 2007-2008 and the collapse of the subprime mortgage market results in the closure or merger of hundreds of mortgage companies and savings institutions. Lenders that had expanded processing staff aggressively during the 2003-2006 origination boom shed staff rapidly. The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 and the creation of the Consumer Financial Protection Bureau (CFPB) subsequently added substantial new compliance documentation requirements (TRID in 2015, HMDA expanded data fields in 2018), which partially re-expanded clerical compliance work even as overall origination volumes declined from their peak.

Beat · 2015

TRID -- the TILA-RESPA Integrated Disclosure rule -- takes effect on October 3, 2015, replacing the HUD-1 Settlement Statement and Good Faith Estimate with new standardized Loan Estimate and Closing Disclosure forms. TRID imposes strict three-day waiting periods and fee-tolerance requirements that make accurate document preparation and timely delivery non-negotiable. The rule was widely cited in the industry as the most complex regulatory change to origination processing since RESPA in 1974, and it drove significant investment in LOS platform upgrades at lenders of all sizes. Clerks needed to learn the new form structures, tolerance buckets, and re-disclosure triggers, reinforcing the compliance-knowledge dimension of the job.

Beat · 2024

A Stratmor Group survey of lenders published in 2025 (cited in ABA Banking Journal April 2026) finds that approximately 14% of mortgage lenders have deployed full-stack AI automation for document processing, with the leading-edge users reporting 30-40% reductions in per-loan processing labor costs. A further 48% report using robotic process automation for at least one workflow step (appraisal ordering, flood cert retrieval, or disclosure delivery). Workers with AI fluency in the LOS platform earn on average 25% more than peers without it (MPA Magazine 2026), reflecting the shift from manual processing toward AI oversight as the core skill.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace loan interviewers and clerks? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
-2.4%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 43-4131 (Loan interviewers and clerks): 169.1 thousand jobs in 2025, projected 165.0 thousand in 2035, a change of -4.1 thousand (-2.4%). Matrix employment counts all jobs in the occupation, including self-employed workers (0.4% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Frey & Osborne (2013), "The Future of Employment"
2013 study · Probability of computerisation (2013 model)
92%
probability
What this measures

Published appendix value for SOC 2010 43-4131 Loan Interviewers and Clerks: probability of computerisation 0.92, rank 563 of 702 (rank 1 is least computerisable). Frey and Osborne estimated in 2013, with a Gaussian-process classifier on O*NET variables, how likely the whole occupation is to be technically automatable over "some unspecified number of years, perhaps a decade or two". It is a probability for the occupation, not a share of tasks and not a forecast of jobs lost.

Official statisticReport an error
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
50%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 43-4131.00 Loan Interviewers and Clerks. Shown: human-rated β = 0.5, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.125 and γ (all LLM-assisted) 0.875; GPT-4-rated β 0.594. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for loan interviewers and clerks

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onReview the AI-generated document classification and data extraction report from Ocrolus or Areal Copilot after a new loan application arrives

Review the AI-generated document classification and data extraction report from Ocrolus or Areal Copilot after a new loan application arrives; confirm that pay stubs, W-2s, tax returns, bank statements, and title abstracts were correctly identified and keyed; investigate the 1-3% of fields the platform flagged as low-confidence or missing.[7],[6]

Where your edge is

Ocrolus and Areal achieve high field-extraction accuracy on standard mortgage document types, so the clerk's job has shifted from keying data to auditing the AI's output. Build skills in reading AI confidence scores and exception queues, and in recognizing document edge cases (unusual pay structures, handwritten addenda, non-standard W-2 formats) the model struggles with.

AI is taking this onVerify income and employment by reviewing the Ocrolus-calculated income analysis alongside the supporting documents

Verify income and employment by reviewing the Ocrolus-calculated income analysis alongside the supporting documents; cross-check calculated qualifying income against the loan origination system's figures; escalate discrepancies (self-employment income calculations, year-over-year variance explanations) that fall outside the platform's standard rules.[8],[9]

Where your edge is

Ocrolus and Argyle together cover income verification from digital payroll data for roughly 80% of borrowers. The edge cases -- self-employed borrowers, gig workers, non-standard income streams, borrowers with income from multiple sources -- require human judgment to calculate qualifying income under agency guidelines. Build skills in analyzing tax returns (Schedule C, K-1, about a thousand worksheet) for non-W-2 borrowers.

AI is sitting alongside you hereManage the conditions queue in the loan origination system (ICE Encompass, MeridianLink): review AI-generated conditions attached to the underwriting findings, contact borrowers or third parties to collect outstanding documents, track receipt, and update the LOS once conditions are satisfied so the file advances to underwriting or closing.

Manage the conditions queue in the loan origination system (ICE Encompass, MeridianLink): review AI-generated conditions attached to the underwriting findings, contact borrowers or third parties to collect outstanding documents, track receipt, and update the LOS once conditions are satisfied so the file advances to underwriting or closing.[10],[11]

Where your edge is

Blend Autopilot and MeridianLink Doc Agent now auto-generate conditions and route document requests, but the human follow-up call -- explaining what is needed, why, and in what format -- still requires a person. Develop skills in reading AUS (automated underwriting system) findings, translating technical conditions into plain language for borrowers, and escalating stale conditions to the loan officer before they age into fallout.

Where loan interviewers and clerks go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Compliance Officers

Loan clerks accumulate significant practical compliance knowledge: TRID timing rules, ECOA adverse action requirements, HMDA data integrity, RESPA referral restrictions, and GSE delivery guidelines. This regulatory foundation is a genuine asset in compliance officer roles at community banks, credit unions, and mortgage companies. BLS projects growth for compliance officers 2024-2034, contrasting sharply with the financial clerks projection. The CRCM (Certified Regulatory Compliance Manager) from the American Bankers Association is the primary credential pathway.

What you'd add
  • · Certified Regulatory Compliance Manager (CRCM) -- ABA-issued; requires 3 years experience + comprehensive exam
  • · TRID (TILA-RESPA Integrated Disclosure) rules: timing requirements, fee tolerance buckets, cure procedures
  • · HMDA data reporting: LAR preparation, reportable transactions, demographic data collection rules
  • · Fair lending analysis: ECOA, Fair Housing Act, disparate impact testing concepts
  • · Bank Secrecy Act / AML fundamentals: suspicious activity reporting, customer due diligence
What it takesSome new skills to pick up
Tools of the era

How has the work of loan interviewers and clerks changed since 1870?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Ledger book + county deed records (building-and-loan paper era)

    The loan clerk of the building-and-loan era worked entirely in paper: a bound ledger recording each loan, a desk copy of the state building-and-loan statutes, correspondence with county recorders to obtain deed abstracts, and a tickler file of installment due dates. The work was slow and manual, but the transaction volume was modest. A clerk at a typical building-and-loan association might process a few new loan applications per week. Document assembly, title verification, and installment tracking were all done by hand, and the clerk was the institutional memory of the loan portfolio.

  • FHA standardized underwriting forms + GI Bill volume processing

    The National Housing Act of 1934 created the Federal Housing Administration and with it the first standardized mortgage underwriting form. For the loan clerk, this was a profound change: processing now followed a federal checklist rather than local custom. The FHA Form 2900 appraisal and the FNMA Form 1003 loan application (introduced in 1970) structured the clerks' work around specific data fields that had to be collected and verified. The GI Bill of 1944 multiplied origination volume dramatically and made loan processing a large clerical specialty for the first time. By the late 1950s, savings institutions were hiring processing clerks in volume, predominantly women, for their back offices.

    Effect on the work

    Standardized federal forms increased the per-loan documentation burden but also made the work trainable and scalable. The GI Bill era saw the first real mass employment of women in mortgage processing, as the volume overwhelmed existing staff and the structured form design made the work learnable in weeks rather than years.

  • RESPA/TILA disclosure paperwork + mainframe loan accounting

    The Real Estate Settlement Procedures Act of 1974 and the Truth in Lending Act disclosure regime substantially increased the per-loan document count, adding HUD-1 settlement statements, Good Faith Estimates, and Regulation Z disclosure forms to every file. At the same time, large banks and savings institutions deployed IBM and NCR mainframe loan accounting systems that automated the back-end payment tracking and amortization calculations that clerks had previously done by hand. The net effect on employment was roughly neutral: the automation of payment calculations freed clerks from manual arithmetic, but RESPA and TILA compliance requirements added new document-handling tasks at roughly the same pace.

  • Desktop PCs + early loan origination software (Calyx Point, early LOS systems)

    Personal computers arrived in mortgage processing offices in the mid-1980s and transformed the administrative workflow. Spreadsheet software replaced manual amortization schedules; word processors replaced typed form letters; and early loan origination software (Calyx Point was one of the first widely adopted systems, launched in 1991) began automating the assembly of disclosure packages. The 1985-1995 decade was a period of productivity gain that absorbed rather than displaced clerks: origination volumes were growing faster than productivity gains could shrink headcount, particularly during the refinance boom of 1992-1993 (when interest rates fell sharply and millions of homeowners refinanced simultaneously).

  • Automated Underwriting Systems (Fannie Mae Desktop Underwriter 1994, Freddie Mac Loan Prospector 1995)

    Fannie Mae launched Desktop Underwriter (DU) in 1994 and Freddie Mac launched Loan Prospector (LP) in 1995, introducing the first automated underwriting decisions to the mortgage industry. For the loan clerk, this changed the relationship between processing and underwriting: instead of assembling a paper file and waiting for a human underwriter to review it, the processor could get an automated approval within minutes and receive a precisely enumerated list of required documentation conditions. DU and LP did not eliminate the clerk's job; they restructured it around condition clearing rather than open-ended file assembly. The role became more predictable but also more narrowly focused on document collection and verification.

    Effect on the work

    Automated underwriting increased throughput significantly: a processor using DU or LP could manage more files simultaneously because the system defined exactly what was needed for each file, reducing time spent on back-and-forth with underwriters. MBA industry surveys from the late 1990s showed per-loan processing costs declining modestly as AUS adoption increased.

  • Enterprise LOS platforms (ICE Encompass, MeridianLink, Ellie Mae cloud era)

    Ellie Mae (later ICE Mortgage Technology) launched Encompass as a cloud-based loan origination system in the mid-2000s; it became the dominant LOS for independent mortgage banks and mid-size lenders. Encompass consolidated what had been separate systems for document management, conditions tracking, disclosure delivery, and regulatory compliance into a single workflow platform. For loan clerks, this was the first truly integrated tool environment: file status, conditions, timelines, and disclosure obligations were all visible in one screen. The compliance burden of TRID (the TILA-RESPA Integrated Disclosure rule, effective October 2015) further standardized the LOS workflow and made manual tracking essentially unviable for any lender operating at scale.

  • Agentic AI mortgage automation (Ocrolus, Areal, Blend Autopilot, MeridianLink Doc Agent)

    Beginning around 2018, purpose-built AI platforms began automating the core document-extraction and income-verification tasks that had defined the loan clerk role for a century. Ocrolus (founded 2014, scaled commercially 2018) classifies mortgage documents and extracts data. Areal Copilot Processor Agent (2024) saves a few hours per loan during intake and assembles closing disclosures in minutes rather than many minutes. Blend Autopilot (2025) reviews borrower documents and validates against compliance rules in several seconds. MeridianLink Doc Agent (2025) auto-generates conditions and routes exceptions. By 2025-2026, the core workflow of the loan clerk has been restructured from doing these tasks to auditing AI doing them.

    Effect on the work

    MBA data and lender case studies suggest AI mortgage automation reduces per-loan processing labor. Lenders using full-stack AI origination report handling the same loan volume with significantly fewer processing staff, while repositioning surviving clerks as exception specialists.

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Loan Interviewers and Clerks employment and pay by year

On record since1870
Latest US employment164,790 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$50,020 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook-2.4% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 29 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
189015,000n/aEst., FDIC Banking Review Researched contextReport
195095,000n/aEst., Federal Home Loan Bank Board Researched contextReport
1956n/a$3,200Est., University of Missouri Libraries Researched contextReport
1980155,000n/aEst., FDIC Historical Statistics on Banking Researched contextReport
1995n/a$22,000Est., BLS Occupational Outlook Handbook Researched contextReport
2000215,000n/aEst., O*NET OnLine Researched contextReport
2003179,080$28,330BLS OEWS May 2003 Official statisticReport
2004209,320$29,000BLS OEWS May 2004 Official statisticReport
2005231,700$30,200BLS OEWS May 2005 Official statisticReport
2006248,050$30,970BLS OEWS May 2006 Official statisticReport
2007239,810$31,680BLS OEWS May 2007 Official statisticReport
2008212,340$32,470BLS OEWS May 2008 Official statisticReport
2009195,310$33,350BLS OEWS May 2009 Official statisticReport
2010181,600$33,970BLS OEWS May 2010 Official statisticReport
2011186,240$34,820BLS OEWS May 2011 Official statisticReport
2012192,010$35,310BLS OEWS May 2012 Official statisticReport
2013213,270$36,050BLS OEWS May 2013 Official statisticReport
2014212,440$36,880BLS OEWS May 2014 Official statisticReport
2015216,380$37,710BLS OEWS May 2015 Official statisticReport
2016224,340$38,630BLS OEWS May 2016 Official statisticReport
2017227,430$39,060BLS OEWS May 2017 Official statisticReport
2018222,620$39,890BLS OEWS May 2018 Official statisticReport
2019208,530$40,640BLS OEWS May 2019 Official statisticReport
2020204,100$41,370BLS OEWS May 2020 Official statisticReport
2021238,610$45,940BLS OEWS May 2021 Official statisticReport
2022242,630$46,490BLS OEWS May 2022 Official statisticReport
2023203,940$47,380BLS OEWS May 2023 Official statisticReport
2024173,100$48,950BLS OEWS May 2024 Official statisticReport
2025164,790$50,020BLS OEWS May 2025 Official statisticReport
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