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Billing and Posting Clerks

On the record since 1870, when there were 38,000. There are 429,800 today. every figure cited

Drag the slider to travel 260 years of this work.

2026drag to travel through time
180018251850187519001925195019752000now
Country
2026
Known today as Billing and Posting Clerks (BLS SOC 43-3021)
Latest actual · 2024
430K
BLS OEWS May 2024, sourced from O*NET which reflects the same BLS establishment-survey figure. Employment has declined from its early-2000s peak as AI-powered revenue cycle platforms automate claim scrubbing, payment posting, and eligibility verification. Healthcare industry growth offsets some of this loss, but productivity per billing clerk has risen sharply. BLS projects -0.4% employment change 2024-2034, from 429,800 to approximately 427,900 -- classified as "little or no change."
Latest actual · 2024
$47,170
Source: BLS-OEWS
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Beat · 2026

By early 2026, AI platforms have demonstrably automated the majority of structured billing tasks in leading health systems. Waystar AltitudeAI has prevented $15.5 billion in claim denials since launch and reduced manual correction workload by 40%; CodaMetrix holds the #1 Best in KLAS rating for autonomous medical coding, reducing manual coding 70% at major academic medical centers; AKASA removed 71% of accounts from staff queues at Cleveland Clinic in a 2025 partnership. Experian Health's 2025 survey reports 63% of providers using AI in revenue cycle workflows. The billing clerk role is not gone, but the job has shifted: fewer clerks handle more volume by exception, with AI platforms processing the routine and humans managing the edge cases, appeals, and patient-facing work.

Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Hand ledger and quill pen (counting-house era)

    The original billing clerk worked entirely by hand: double-entry ledgers recording debits and credits in columns, with totals balanced at end-of-day or end-of-month. Errors meant re-totaling entire columns. The craft required literacy, numeracy, and the patience to copy the same figures into multiple books (day book, journal, ledger). Every invoice was written out in longhand, and "posting" meant literally carrying numbers from one book to another. This was skilled manual labor by the standards of the era, and it was paid accordingly.

    Ledger workPaper recordkeeping
  • Typewriter and bookkeeping machines (Burroughs adding machine, 1886)

    William S. Burroughs introduced the first practical adding and listing machine in 1886, patented in 1888, and by the mid-1890s the Burroughs Adding Machine Company was selling them to banks, insurance companies, and counting houses. The typewriter (Remington No. 1, 1874) transformed invoice preparation from handwriting to mechanical key-striking. Together, these tools changed billing work in two ways: they made it faster and more standardized per worker, and -- crucially -- they made it easier to hire women to do it, because the new machine-mediated tasks were framed as requiring operator skill rather than the professional judgment of an accountant. Employers paid women less for the same output. The result was a feminization of bookkeeping and billing between 1880 and 1930 that permanently associated the occupation with female labor and lower wages.

    Effect on the work

    The mechanization of billing work between 1880 and 1930 roughly doubled output per worker while simultaneously reducing per-worker wages by substituting cheaper female labor for more expensive male bookkeepers. The workforce grew in absolute numbers as commerce expanded, but the wage premium for bookkeeping eroded relative to professional accounting.

    Mechanical calculationTen-key speed
  • IBM punched-card tabulating machines (electromechanical accounting era)

    By the mid-1930s, IBM held 85% of the office machine market. Keypunch operators -- a separate but adjacent occupational class to billing clerks -- used IBM tabulating equipment to process payrolls, utility bills, insurance premiums, and eventually federal Social Security records. The Social Security Act of 1935 drove a massive expansion in punched-card record-keeping: for the first time, every wage earner in the United States had to be tracked by an employer account, processed through a central file. The electromechanical accounting machine (EAM) -- keypunch, sorter, collator, tabulator -- was the core billing technology of large organizations from the 1930s through the 1960s. For billing clerks at the clerical level, the keypunch era meant that batch processing could handle volumes no hand-ledger team could match, but it also meant strict input discipline: keypunch errors propagated through every downstream report.

    Effect on the work

    IBM tabulating machines expanded the throughput of billing departments significantly but did not reduce headcount proportionally because the New Deal era and post-war economic growth generated far more transaction volume. The Social Security Administration's punched-card requirement alone created thousands of new keypunch and billing-clerk positions at companies nationwide.

    Punch-card systemsBatch accounting
  • Medicare and Medicaid claim forms (HCFA-1500, UB-82) -- paper-based third-party era

    The creation of Medicare and Medicaid in July 1965 transformed medical billing from a simple cash-and-charity transaction into a specialized discipline requiring mastery of ICD diagnosis codes, CPT procedure codes (introduced by the AMA in 1966), and federal claim form requirements. The HCFA-1500 form (introduced for professional claims) and UB-82 (for institutional claims) became the standard submission vehicles. Hospitals that had employed a handful of billing clerks before 1965 now needed dozens: one to verify insurance eligibility, others to assign codes, others to type and submit claims, and still others to post payments and follow up on denials. Medical billing became its own sub-profession, separate from general bookkeeping, with its own training programs and eventually its own certification bodies.

    Effect on the work

    Medicare and Medicaid directly created a new class of billing specialists concentrated in the healthcare sector. Hospital billing department headcounts grew 3-5x between 1965 and 1980 at many institutions as the claims volume and complexity of third-party reimbursement became clear. The occupation shifted from broadly distributed across industries to heavily concentrated in healthcare and professional services.

    Work toolChanging equipment
  • Practice management software and HCPCS coding (personal computer era, pre-HIPAA)

    The early 1980s brought two converging changes: affordable personal computers, which allowed medical practices to run practice management software on a desktop rather than sending data batches to a mainframe service bureau, and the 1983 introduction of the HCPCS coding system by CMS (which combined HCFA's own codes with the AMA's CPT system and mandated their use for all Medicare billing). The same year, HCPCS mandated standardization of how procedures were coded across all Medicare claims. Billing clerks now needed to understand not just form requirements but a standardized three-tier code structure: ICD-9 for diagnoses, CPT/HCPCS Level I for procedures, HCPCS Level II for supplies and services. Practice management software (early Medisoft, Medical Manager) automated some of the claim-typing work but required clerks to learn software operation on top of coding knowledge.

    Work toolChanging equipment
  • HIPAA EDI mandates (X12 837/835 electronic transactions, claims clearinghouses)

    The Health Insurance Portability and Accountability Act of 1996 required the Department of Health and Human Services to create national standards for electronic healthcare transactions. HIPAA's Administrative Simplification provisions mandated that covered entities (hospitals, physicians, insurers) use the ANSI X12 837 transaction format for electronic claim submission and the 835 format for electronic remittance advice (ERA). Implementation dates ran into the early 2000s, but by 2005 most large healthcare organizations were filing claims electronically through clearinghouses. For billing clerks, HIPAA EDI meant a profound change in workflow: claim "submission" shifted from stuffing paper forms into envelopes to uploading electronic files, and "payment posting" shifted from manually interpreting an EOB letter to processing an 835 remittance file. Billing clerks who could not navigate electronic transactions became unemployable in larger practices.

    Effect on the work

    Electronic claim submission and ERA processing roughly doubled the volume of claims a single billing clerk could process compared to paper, beginning a long-run productivity trend that put structural pressure on headcount even as healthcare utilization grew. Claims clearinghouses (WebMD Health, NaviMedix, now Change Healthcare) emerged as a new intermediary industry, outsourcing some billing clerk functions to specialist firms.

    Work toolChanging equipment
  • EHR mandates (HITECH Act 2009) and ICD-10 transition (2015)

    The American Recovery and Reinvestment Act of 2009 included the HITECH Act, which created financial incentives for adoption of certified EHR systems and penalties for non-adoption beginning in 2015. By 2021, 78% of office-based physicians and 96% of non-federal acute care hospitals used certified EHR systems, up from 28% and 34% in 2011. For billing clerks, EHR adoption meant that clinical documentation -- from which billing codes were derived -- now lived in a structured digital system they could access directly rather than deciphering handwritten chart notes. The ICD-10 transition (October 1, 2015) expanded the diagnosis code set from approximately 17,000 ICD-9 codes to over 68,000 ICD-10 codes, requiring every billing clerk who worked with diagnosis coding to undergo substantial retraining.

    Effect on the work

    The ICD-10 transition temporarily increased demand for billing staff (and billing education programs) between 2013 and 2016, as practices needed more coders to handle the expanded code set. Post-transition, the productivity of EHR-integrated billing workflows began compressing headcount needs again.

    Electronic recordDigital charting
  • AI-powered revenue cycle platforms (Waystar AltitudeAI, CodaMetrix, AKASA -- autonomous claim processing)

    The deployment of AI revenue cycle platforms beginning around 2018-2020 represents the most disruptive tool transition in the history of billing and posting work. CodaMetrix (founded 2017, major health system deployments from 2020) cuts manual medical coding work by 70% and coding costs by 60% at large health systems including Mass General Brigham, Mayo Clinic, and Yale Medicine. AKASA removed 71% of accounts from staff queues at Cleveland Clinic in a 2025 partnership, saving 5,559 staff hours in eight months. Waystar AltitudeAI has prevented $15.5 billion in denials since launch and reduces manual correction workload by 40%. Experian Health's 2025 survey found 63% of healthcare providers using AI in revenue cycle workflows. For billing and posting clerks, the effect is a structural narrowing of the job: routine claim submission, payment posting, and eligibility verification are being absorbed by autonomous platforms, leaving a human workforce concentrated on exception-handling, denial appeals, patient financial counseling, and compliance oversight. The volume of claims handled per remaining clerk rises; the total number of clerks needed falls.

    Effect on the work

    BLS projects -0.4% employment change 2024-2034 for 43-3021, a net loss of approximately 1,900 positions. The projection understates the productivity shift because it counts headcount, not workload absorbed per clerk. Healthcare industry growth offsets some of the automation-driven headcount reduction, masking what would otherwise be a steeper decline.

    Work toolChanging equipment
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
BLS National Employment Matrix 2024-34
2034
-0.4%
BLS Employment Projections industry-occupation matrix, 2024-2034 cycle. Projects -0.4% employment change for 43-3021, from 429,800 (2024) to approximately 427,900 (2034) -- a net loss of about 1,900 positions. Classified as "little or no change" against an all-occupations average of +4%. BLS methodology models healthcare industry growth as a partial offset to automation-driven productivity gains; automated invoice processing software and AI claim-scrubbing platforms are the primary headwinds. The projection does not explicitly model the pace of agentic AI adoption (Waystar Jan 2026, AKASA Cleveland Clinic 2025), which could accelerate the decline.
BLS Occupational Outlook Handbook -- Financial Clerks group 2024-34
2034
-5%
BLS OOH projects the broader Financial Clerks group (which includes billing and posting clerks, bookkeeping clerks, payroll clerks, and others) to decline -5% overall from 2024-2034. The group held about 1.2 million jobs in 2024 and about 102,200 openings are projected annually, primarily from worker replacement rather than growth. The group-level projection is more pessimistic than the 43-3021 specific figure because it captures adjacent clerks (bookkeeping, payroll) facing steeper automation. Reported here as context; the occupation-specific -0.4% figure is the more precise anchor.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Frey and Osborne (2013)
2033
97%
of tasks
Gaussian-process classifier on O*NET task features. Frey and Osborne placed bookkeeping and accounting clerks at approximately 0.97 probability of computerization -- among the highest in their 702-occupation dataset. The reasoning was straightforward: billing and bookkeeping tasks follow "precise, well-defined procedures" involving data entry, arithmetic, and rules application -- exactly the class of structured work most susceptible to automation. Frey and Osborne were correct in their direction but significantly overstated speed: their framing assumed automation of jobs rather than tasks, and the actual trajectory has been a decades-long productivity shift that compresses headcount gradually rather than eliminating the occupation. A 2013-2024 employment decline of roughly 15-20% from peak is far below the near-total displacement their probability implied. The figure here represents the automation exposure probability as an upper-ceiling scenario, not a realized forecast.
Eloundou et al. -- "GPTs are GPTs" (2023)
2028
82%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for billing and financial clerk occupations. Billing and posting clerks score in the high range for LLM exposure because many of their core tasks -- processing structured data, applying standardized rules, drafting routine communications (denial appeals, patient balance letters) -- are precisely the tasks where large language models perform well. The key distinction from Frey and Osborne: Eloundou measures LLM-specific capability, not general automation. AI coding and billing platforms have been productizing this capability since 2018; the 82% exposure estimate reflects how much of the billing clerk's task portfolio has been or could be automated by the current generation of AI tools. The -5% to -15% realized employment change since peak is far below this exposure rate because adoption friction, exception-handling requirements, regulatory oversight, and patient-facing work create a residual demand that the exposure metric does not fully account for.
Goldman Sachs (2023) -- Generative AI and clerical work
2030
45%
of tasks
Goldman Sachs 2023 research estimated that approximately 45% of clerical support work in the US and Europe is automatable by generative AI. This is the most conservative of the three exposure estimates here -- Goldman weights the constraints on automation adoption (regulatory requirements, transition costs, exception-handling irreducibility) more heavily than Frey and Osborne or Eloundou. For billing clerks specifically, Goldman's 45% aligns with the task decomposition observable in live deployments: AI platforms at leading health systems are handling 60-80% of routine claim volume autonomously, but the full pipeline including denials, patient communications, and compliance oversight requires substantially more human judgment. Goldman's figure is rendered here as a moderate-scenario exposure floor.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

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

AI is sitting alongside you hereVerify patient insurance eligibility and benefits coverage prior to claim submission using AI-powered eligibility tools

Verify patient insurance eligibility and benefits coverage prior to claim submission using AI-powered eligibility tools; review AI-flagged exceptions where coverage is ambiguous, expired, or requires secondary payer coordination; update patient account records accordingly.[8],[10],[1]

Where your edge is

Specialize in secondary payer and coordination-of-benefits edge cases -- the high-confidence routine checks are fully automated, but complex multi-payer scenarios, retroactive terminations, and Medicare/Medicaid duals still require a human who understands payer hierarchies.

AI is sitting alongside you hereConfigure and monitor AI claim-scrubbing rules in billing platforms (Waystar, athenahealth)

Configure and monitor AI claim-scrubbing rules in billing platforms (Waystar, athenahealth); review claims flagged by the rules engine for modifier mismatches, missing documentation, or National Correct Coding Initiative (NCCI) edits; submit corrected claims within payer timely-filing windows.[10],[9],[1]

Where your edge is

Build deep familiarity with your platform's rules engine configuration. Billers who can write and tune scrubbing rules -- rather than just following the flags -- transition naturally into revenue integrity and billing operations roles that pay 20-30% more.

AI is sitting alongside you hereReview AI-generated ICD-10, CPT, and HCPCS code suggestions for procedures and diagnoses

Review AI-generated ICD-10, CPT, and HCPCS code suggestions for procedures and diagnoses; apply clinical documentation guidelines to validate AI coding on complex encounters that fall below the system confidence threshold; submit corrections and flag documentation gaps to clinicians.[6],[9],[7]

Tools picking this up
Where your edge is

Earn a Certified Professional Coder (CPC) or Certified Coding Specialist (CCS) credential. AI autonomous coding covers routine and high-volume specialties (radiology, outpatient E/M) at 95%+ accuracy; complex inpatient and multi-comorbidity cases still require credentialed coders who can apply clinical judgment.

Where this role is heading

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

A direction you could grow

Accountants and Auditors

Billing clerks who work in professional services (law firms, consulting, staffing) already track billable hours, prepare client invoices, and reconcile accounts -- tasks that are directly adjacent to bookkeeping and junior accounting work. The pivot requires formal accounting education (associate or bachelor level) and eventually CPA licensure for full credentialing, but the day-to-day transition is a lateral step in cognitive terms. As AI billing platforms compress the need for high-volume clerk headcount, accounting offers more stable employment projections and a 20-40% wage premium over the median billing clerk salary.

What you'd add
  • · Foundational accounting (debits/credits, financial statements, GAAP principles)
  • · QuickBooks or Sage 50 advanced usage, plus Excel financial modeling
  • · Associate or bachelor degree in accounting or completion of accounting coursework
  • · Bookkeeping certification (AIPB CB) as an interim credential before CPA track
What it takesSome new skills to pick up
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The data behind this timeline

On record since1776
Latest tracked employment429,800 (US, 2024)
Latest median pay$47,170 (2024)
Outlook-0.4% by 2034 (BLS National Employment Matrix 2024-34)
View all 28 cited data points
YearUS employmentMedian annual paySource
187038,000n/aESTIMATE
1900250,000n/aCENSUS-DECENNIAL
1910n/a$624ESTIMATE
1940486,000n/aCENSUS-DECENNIAL
1950n/a$2,600ESTIMATE
2000530,000$25,000BLS-OEWS
2003487,420$26,290BLS-OEWS
2004496,780$27,040BLS-OEWS
2005513,020$27,780BLS-OEWS
2006517,750$28,850BLS-OEWS
2007515,060$29,970BLS-OEWS
2008512,120$30,950BLS-OEWS
2009493,780$31,720BLS-OEWS
2010483,440$32,170BLS-OEWS
2011485,820$32,880BLS-OEWS
2012490,850$33,450BLS-OEWS
2013493,840$33,820BLS-OEWS
2014490,860$34,410BLS-OEWS
2015491,070$35,050BLS-OEWS
2016485,220$36,150BLS-OEWS
2017476,010$36,860BLS-OEWS
2018469,250$37,800BLS-OEWS
2019466,450$38,740BLS-OEWS
2020445,160$39,590BLS-OEWS
2021429,080$38,330BLS-OEWS
2022441,980$42,810BLS-OEWS
2023430,220$45,590BLS-OEWS
2024429,800$47,170BLS-OEWS
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