Actuaries
Also called Pricing actuary, Reserving actuary, Casualty actuary, Property casualty actuary, P&C actuary
In brief
In May 2025, BLS counted 26,670 US actuaries, with median pay of $130,000 a year. Of the BLS counts since 2003, the highest was 28,340, in May 2024. The earliest figure here is an estimate of 1,069 from 1949, not measured the same way as today's count. The last full technology era for actuaries: Akur8, hyperexponential, WTW Radar 5, 2022 to 2024.
What the sources say
Official outlook
BLS projects employment to grow 9.2% from 2025 to 2035.
AI exposure studies
- 53.7% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
- 0.21 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 0.21. BLS counted 20,080 of these jobs in May 2013 and 26,670 in May 2025 (+33%, a change derived by Future History).
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
Future History. (2026, October 11). Actuaries: jobs and pay since 1949 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/15-2011
Future History. "Actuaries: jobs and pay since 1949." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/15-2011.
@misc{futurehistory_15_2011, author = {{Future History}}, title = {Actuaries: jobs and pay since 1949}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/15-2011}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }
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≈ $126,667 in 2024 prices (derived by us, CPI-adjusted)
The Society for Equitable Assurances on Lives and Survivorships is founded in London (September 1762): the world's first mutual life insurance company to set premiums on actuarially calculated, age-stratified mortality data. Edward Rowe Mores coins the title 'actuary' for its chief officer. The Society conducts the world's first actuarial liability valuation in 1776 and declares the first reversionary bonus in 1781, introducing with-profits policy features that remain standard today.
The Institute of Actuaries is founded in London on 8 July 1848: the world's first professional actuarial body, formalizing the occupation with examinations, ethics standards, and a journal. John Finlaison, actuary to the UK government, is elected its first president. The Institute is granted a Royal Charter in 1884; it merges with the Faculty of Actuaries (Scotland, founded 1856) in 2010 to form the Institute and Faculty of Actuaries (IFoA). The 2023 175th anniversary of the founding is described by the IFoA as "a milestone in actuarial history."
The Casualty Actuarial Society (CAS) is founded in 1914 by actuaries and statisticians at US property and liability insurance companies, formalizing the life/casualty professional split that has defined US actuarial credentialing ever since. The CAS remained independent in the 1949 merger that created the SOA, producing the dual-credential structure (FSA for life/health/pensions; FCAS for property-casualty) that is still the organizing frame of the profession.
The Society of Actuaries is founded through the merger of the Actuarial Society of America (1889; 38 founding members) and the American Institute of Actuaries (1909), consolidating North American life and health actuarial credentialing under one body. At founding, SOA membership stands at 1,069 (642 Fellows + 427 Associates). Average annual growth exceeds 6% for the next half-century, reaching 16,942 members by 1995 and 34,000+ worldwide by 2024.
The HIV/AIDS epidemic forces a fundamental recalibration of life insurance mortality assumptions. The Institute of Actuaries (UK) establishes an AIDS Working Party that issues three bulletins between 1987-1988; the SOA creates a Task Force on AIDS in 1988. The crisis reverses decades of improving mortality trends and compels insurers to introduce widespread laboratory testing at lower policy amounts: a practice that generates the first systematic biomarker data on insured populations and inadvertently leads to the introduction of preferred mortality rates (non-smoker / preferred / standard / substandard rate classes) across North America in the 1980s-1990s.
The 2008 financial crisis delivers the largest single shock to pension actuarial assumptions in modern history. Equity markets fall 40-50%, investment return assumptions collapse, and discount rates on corporate pension liabilities drop dramatically: mechanically expanding the actuarial present value of pension liabilities by tens of billions of dollars at large plan sponsors. The crisis generates years of remediation work for pension actuaries and triggers regulatory tightening under ERISA, PPA (Pension Protection Act, 2006, whose funding requirements became binding in the crisis years), and international Solvency II framework development. The crisis also exposes gaps in actuarial catastrophe-correlation modeling for financial guaranty products, creating demand for actuaries with capital-markets expertise.
IFRS 17 Insurance Contracts takes effect on 1 January 2023: the most significant change to global insurance financial reporting since IFRS 4. IFRS 17 introduces fulfillment-value liability measurement, contractual service margin (CSM) amortization, and risk-adjustment disclosures, requiring actuaries to produce reserve estimates at more granular levels and deliver substantially more written actuarial narrative per reporting period. More than 70% of surveyed insurers reported liability valuations changing by more than 15% under IFRS 17 versus prior IFRS 4 figures. The standard simultaneously increases actuarial workload and creates demand for LLM-assisted reporting, making 2023 the year that two major forces (regulatory complexity + generative AI) converge on the actuarial profession at once.
Will AI replace actuaries? 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's changing day to day for actuaries
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is sitting alongside you hereWrite Python or R code for bespoke actuarial analyses (experience studies, credibility-weighted development factors, frequency-severity separation, predictive loss cost models) using GitHub Copilot for code generation acceleration
Write Python or R code for bespoke actuarial analyses (experience studies, credibility-weighted development factors, frequency-severity separation, predictive loss cost models) using GitHub Copilot for code generation acceleration; review and validate AI-generated statistical code against actuarial standards before it is used in reserve or pricing deliverables that carry professional sign-off.[10],[11]
AI code generation for actuarial work is fast but requires rigorous validation: Copilot hallucinations in chain-ladder factor selection or credibility-weighting syntax can produce plausible-looking but actuarially wrong outputs. Build a test-suite discipline: always validate generated code against a small dataset with known analytical results before applying it to production reserve or pricing work. Python proficiency with scikit-learn, pandas, and statsmodels is now listed in job postings by major carriers as a baseline requirement.
AI is sitting alongside you hereBuild and validate insurance pricing models using Akur8 or hyperexponential (hx Renew): use the platform's transparent ML engine to fit GLMs and GAMs to carrier loss experience, validate model assumptions and lift curves against holdout data, and deploy rate plans to the production pricing engine, replacing the historical cycle of manual R/Excel GLM builds that took weeks with an actuarially governed ML pipeline that takes days.
Build and validate insurance pricing models using Akur8 or hyperexponential (hx Renew): use the platform's transparent ML engine to fit GLMs and GAMs to carrier loss experience, validate model assumptions and lift curves against holdout data, and deploy rate plans to the production pricing engine, replacing the historical cycle of manual R/Excel GLM builds that took weeks with an actuarially governed ML pipeline that takes days.[12],[13]
The platform accelerates model execution; the actuary's irreplaceable contribution is the judgment calls around variable selection, credibility weighting, territorial relativities, and regulatory defensibility. Develop fluency in explaining ML model outputs to regulators and underwriters: the "black box" risk is highest in Gradient Boosting models, and transparent GLM/GAM variants require the actuary to justify the model form in rate filings.
AI is sitting alongside you hereRun quarterly IBNR reserve analyses using Akur8 Arius (formerly Milliman Arius) or equivalent actuarial reserving software: set up loss development triangles, select development factors, apply chain-ladder and Bornhuetter-Ferguson methods, run stochastic reserve variability analyses, and produce the reserve exhibits required by GAAP, STAT, and IFRS 17 reporting, a workflow where AI platforms have reduced quarterly file-preparation time by up to 10× vs
Run quarterly IBNR reserve analyses using Akur8 Arius (formerly Milliman Arius) or equivalent actuarial reserving software: set up loss development triangles, select development factors, apply chain-ladder and Bornhuetter-Ferguson methods, run stochastic reserve variability analyses, and produce the reserve exhibits required by GAAP, STAT, and IFRS 17 reporting, a workflow where AI platforms have reduced quarterly file-preparation time by up to 10× vs. manual Excel-based reserving.[14],[15]
AI-assisted reserving platforms automate the mechanical execution of triangle runs and variance analyses; the actuary's value is in the judgment-intensive steps: selecting the appropriate development method for each line (given data credibility and maturity), detecting anomalies in development patterns that signal claim operations changes or mix shifts, and translating reserve variability into business-language risk communication for CFOs and audit committees.
Where actuaries go next
Natural next steps for someone with your foundation: not exits, evolutions.
- Financial Managers
- US median pay
- $166,570 (May 2025)
- BLS outlook 2025-35
- +9.7%
- Insurance Underwriters
- US median pay
- $81,370 (May 2025)
- BLS outlook 2025-35
- -3.8%
- Operations Research Analysts
- US median pay
- $88,940 (May 2025)
- BLS outlook 2025-35
- +11.9%
What each move takes
Financial Managers
Senior actuaries, particularly Chief Actuaries and appointed actuaries at mid-size carriers, frequently transition into CFO or Financial Manager roles within insurance. The actuarial background provides direct preparation for insurance financial management: GAAP/STAT reserve adequacy, investment portfolio management under insurance liability constraints, reinsurance program oversight, and ORSA (Own Risk and Solvency Assessment) governance are all actuarial functions that scale naturally into financial leadership. Carriers actively look for actuarial-credentialed CFOs who can bridge quantitative risk modeling and financial reporting. The CRI is marginally higher because Financial Managers have a broader organizational mandate and the financial management credential moat (CPA, CFA, or equivalent) is complementary to the FSA/FCAS rather than redundant.
- · GAAP and STAT insurance financial statement analysis: combined ratio management, investment income attribution, loss and LAE ratio decomposition
- · Investment portfolio management under insurance liability constraints: duration matching, credit quality governance, NAIC investment category compliance
- · ORSA and Solvency II / RBC capital framework: economic capital modeling, stress testing, regulatory capital reporting
- · Executive communication: board-level risk appetite framing, investor relations for publicly traded carriers, rating agency engagement
- · CPA or CFA credential as financial leadership credential complement to FSA/FCAS
How has the work of actuaries changed since 1693?
The tools that defined the work, one line per era. Select an era to see how it reshaped the work.
Mortality tables, logarithm tables, and hand calculation
Edmond Halley's 1693 Breslau life table (Philosophical Transactions of the Royal Society, No. 196) gave actuaries their first rigorous empirical foundation for pricing life annuities: a tabulated count of survivors at each age from a city-wide birth cohort. For the next two centuries, actuarial computation meant manually constructing and applying life tables using logarithm tables and arithmetic shortcuts called 'commutation functions': algebraic condensations that reduced compound-interest-and-mortality calculations to lookups in precomputed columns. Abraham de Moivre's 1725 'Annuities upon Lives' introduced a simplified uniform-distribution approximation for mortality, giving actuaries a practical tool before full life tables were available. Every premium calculation was carried out by hand, with errors checked by a second actuary performing the same arithmetic. The job was essentially the work of a human computer.
Mechanical calculators (Marchant, Comptometer, Burroughs)
Mechanical adding and multiplying machines (the Burroughs adding machine [1888], the Comptometer [1887], and the Marchant calculator [adopted widely by the 1920s]) accelerated actuarial arithmetic without displacing the actuary's judgment function. Insurance company "computation departments" staffed largely by women performed the mechanical steps under an actuary's direction. The principal computational product was the actuarial reserve calculation, the present value of future benefit payments, which required thousands of multiplications per policy for large portfolios. Slide rules were standard individual tools. The 1941 American Experience Table of Mortality (standard in US life insurance pricing through the 1950s) was constructed using these mechanical tools on data from nineteen large life insurers.
IBM mainframes and FORTRAN: reserve and valuation routines
Travelers Insurance Company installed one of the first IBM mainframe computers in the 1960s; the IBM System/360, introduced in 1964, became the industry workhorse for actuarial valuation. FORTRAN, released by IBM in 1957, became the programming language of choice for actuarial reserve models. For the first time, a life insurer could mechanically value every policy in force overnight rather than sampling or approximating. The mainframe did not eliminate actuarial judgment; it redirected it from arithmetic to model design. The job of the valuation actuary shifted from calculating reserves manually to writing, validating, and governing the programs that calculated them. Mainframe computing was a shared resource, batch jobs queued overnight, which constrained the actuary's ability to run scenarios interactively.
Effect on the workMainframe computing dramatically reduced the clerical actuarial staff ("computation departments") of insurance companies while increasing demand for actuaries who could specify and validate the machine models. The net effect on the credentialed actuarial profession was employment growth, not displacement.
MoSes and domain-specific actuarial modelling languages (1970s-80s)
MoSes (Model of Stochastic Events System), developed by Tillinghast (later Towers Perrin, now Moody's Analytics), emerged in the late 1970s and early 1980s as the first domain-specific actuarial modelling language: purpose-built for life insurance product pricing, valuation, and profit testing. Unlike FORTRAN, which required programming expertise, MoSes allowed actuaries to describe policy structures and projection logic in their own vocabulary. It became the standard platform for complex life product modelling at large insurers and consulting firms through the 1980s. APL (A Programming Language), an array-oriented language popular in IBM environments, was also widely adopted by insurance companies in this era for its ability to manipulate actuarial tables as arrays without nested loops.
Lotus 1-2-3 and PC spreadsheets: actuarial liberation from the mainframe
The early 1980s PC revolution liberated actuaries from the mainframe's overnight batch queue. Lotus 1-2-3 (1983), running on the IBM PC, gave individual actuaries interactive computation for the first time: build a mortality table, change an assumption, see the result in seconds. The effect on actuarial practice was profound. 'The personal computer was liberating: the actuary was free,' as SOA Systems Evolution histories describe it. Actuaries could now prototype pricing models, run sensitivity analyses, and present results to management in a single day, rather than waiting for the data-processing department. The spreadsheet also democratized actuarial modelling within insurance companies: finance and underwriting staff could interrogate actuarial results without going through the actuarial department.
Prophet and AXIS: enterprise actuarial valuation platforms
Prophet (originally developed in the early 1990s; later acquired by SunGard, then FIS in 2015) and AXIS (developed by Milliman's actuarial consulting practice) became the dominant actuarial cash-flow projection platforms for life insurance valuation through the 1990s and 2000s. Unlike spreadsheets, these platforms could project the full liability of an insurer's in-force book, hundreds of thousands of policies across multiple product lines, into hundreds of future time periods, applying stochastic interest and mortality scenarios. They produced the reserve exhibits required by NAIC statutory accounting, GAAP, and (from 2023) IFRS 17. 'Prophet is a projection system and can be compared to an Excel spreadsheet on steroids,' as one SOA practitioner described it in 2013. Prophet served 10,000+ users in about a thousand sites across many countries at its peak.
R and Python: predictive modeling enters actuarial practice
The shift from traditional GLMs constructed in Excel to gradient-boosting models, random forests, and neural networks deployed in R and Python redefined what actuarial pricing models could do. By the mid-2010s, personal auto insurers were building telematics-based pricing models using sensor data; Lemonade (founded 2015, launched NY 2016) built its entire underwriting and claims operation on machine learning from day one. Usage-based insurance (pricing based on actual miles driven, time of day, and driving behavior) required ML infrastructure that spreadsheets and Prophet could not deliver. By 2020, Python had become a listed job-posting requirement at major US carriers (per SOA research), and the CAS launched its Exam MAS series explicitly covering predictive analytics and machine learning.
Effect on the workPython/ML skills widened the gap between junior actuaries proficient only in Excel-and-Prophet and those who could build end-to-end ML pricing pipelines. Early evidence (hyperexponential 2025 research) showed that data-manipulation work consumed roughly 70% of a junior actuary's time before ML platforms arrived; with ML tooling, that dropped to ~30%, compressing the entry-level work pyramid.
Akur8, hyperexponential (hx), WTW Radar 5: AI-native actuarial pricing platforms
Akur8 (founded 2019; expanded to hundreds of carrier clients in many countries by 2025) and hyperexponential hx Renew brought transparent machine learning, gradient-boosting models with actuarially interpretable outputs, directly into the pricing actuary's workflow, replacing the weeks-long cycle of building GLMs in Excel with an ML pipeline that delivers model outputs in hours. Akur8 expanded its platform further by acquiring Arius (Milliman's reserving software) in September 2024 and Slope Software (life actuarial modeling) in 2024, building an end-to-end actuarial AI platform spanning pricing, reserving, and life modeling. WTW Radar 5, relaunched with generative AI capabilities in October 2025, added natural-language model construction. hyperexponential's own research documented that AI deployment cut actuarial data-manipulation time from 70% to 30% of total work hours.
Effect on the workEntry-level actuarial work (chain-ladder runs, factor triangulations, experience-rating spreadsheets) is the first category being automated by these platforms, compressing the base of the traditional junior-actuary work pyramid. The net effect on total actuary headcount is contested: BLS projects +22% growth despite (or because of) AI adoption, as carriers need more actuaries to govern the AI models than to run the manual calculations they replace.
Generative AI for actuarial memos, IFRS 17 reporting, and reserve narratives
The arrival of GPT-4 in March 2023 and Claude 3 in 2024 introduced LLMs into the writing-intensive portions of actuarial work: Statement of Actuarial Opinion narratives, actuarial memoranda supporting rate filings, DOI interrogatory responses, and the new IFRS 17 disclosure requirements (effective 1 January 2023) that significantly expanded the volume of written actuarial narrative required per reporting period. The SOA's January 2026 newsletter "Navigating the AI Transformation in Actuarial Science" described governance, oversight, and accountability decisions as the profession's strongest AI moat, not because LLMs cannot draft the prose, but because the actuary's personal professional attestation (SAO, rate-filing certification, ERISA pension stamp) cannot be delegated to any model regardless of its prose quality. CAS launched an AI Fast Track Program in 2025, acknowledging that ML-based stochastic reserving reduces analysis time by 50%.
See the same long-arc view for your own profession.
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Browse all rolesActuaries employment and pay by year
View all 27 years of cited US data, each linked to its source
| Year | US employment | Annual pay (median unless marked) | Source | Report |
|---|---|---|---|---|
| 1949 | 1,069 | n/a | Est., Huggins Actuarial Services Researched context | Report |
| 1969 | 5,500 | n/a | Est., Huggins Actuarial Researched context | Report |
| 1989 | 18,000 | n/a | Est., Huggins Actuarial Researched context | Report |
| 2000 | 12,890 | $66,590 | BLS OEWS 2000 Official statistic | Report |
| 2003 | 14,680 | $72,520 | BLS OEWS May 2003 Official statistic | Report |
| 2004 | 16,350 | $76,340 | BLS OEWS May 2004 Official statistic | Report |
| 2005 | 15,770 | $81,640 | BLS OEWS May 2005 Official statistic | Report |
| 2006 | 16,620 | $82,800 | BLS OEWS May 2006 Official statistic | Report |
| 2007 | 18,030 | $85,690 | BLS OEWS May 2007 Official statistic | Report |
| 2008 | 18,220 | $84,810 | BLS OEWS May 2008 Official statistic | Report |
| 2009 | 17,940 | $87,210 | BLS OEWS May 2009 Official statistic | Report |
| 2010 | 18,320 | $87,650 | BLS OEWS May 2010 (employment) Official statisticBLS OEWS May 2010 (pay) Official statistic | Report |
| 2011 | 19,590 | $91,060 | BLS OEWS May 2011 Official statistic | Report |
| 2012 | 21,340 | $93,680 | BLS OEWS May 2012 Official statistic | Report |
| 2013 | 20,080 | $94,340 | BLS OEWS May 2013 Official statistic | Report |
| 2014 | 21,490 | $96,700 | BLS OEWS May 2014 Official statistic | Report |
| 2015 | 19,770 | $97,070 | BLS OEWS May 2015 Official statistic | Report |
| 2016 | 19,940 | $100,610 | BLS OEWS May 2016 Official statistic | Report |
| 2017 | 19,210 | $101,560 | BLS OEWS May 2017 Official statistic | Report |
| 2018 | 20,760 | $102,880 | BLS OEWS May 2018 Official statistic | Report |
| 2019 | 22,260 | $108,350 | BLS OEWS May 2019 Official statistic | Report |
| 2020 | 22,480 | $111,030 | BLS OEWS May 2020 Official statistic | Report |
| 2021 | 23,040 | $105,900 | BLS OEWS May 2021 (employment) Official statisticBLS OEWS (pay) Official statistic | Report |
| 2022 | 25,010 | $113,990 | BLS OEWS May 2022 Official statistic | Report |
| 2023 | 25,470 | $120,000 | BLS OEWS May 2023 Official statistic | Report |
| 2024 | 28,340 | $125,770 | BLS OEWS May 2024 Official statistic | Report |
| 2025 | 26,670 | $130,000 | BLS OEWS May 2025 Official statistic | Report |
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