Use as a copilot
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
- Provide advice to clients on a contract basis, working as a consultant. · 17.5%
Actuaries
Occupation · SOC 15-2011.00
Analyze statistical data, such as mortality, accident, sickness, disability, and retirement rates and construct probability tables to forecast risk and liability for payment of future benefits. May ascertain insurance rates required and cash reserves necessary to ensure payment of future benefits.
Also called: Actuarial Analyst · Actuary · Consulting Actuary · Pricing Actuary · Actuarial Associate · Actuarial Consultant · Corporate Actuary · Health Actuary · Product Development Actuary · Reserving Actuary · Actuarial Intern · Actuarial Mathematician
Job family: Computer and Mathematical Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-15-2011-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Keep a human in the loop
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
- Provide advice to clients on a contract basis, working as a consultant. · 95.5% need a human
- Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. · 91.7% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- Growing fast employment outlook (+21.8% by 2034)
- 2,400 openings/yr
- Moderate AI exposure
- Observed AI use leans augmentation
- Median pay $125,770/yr
- Can largely be done remotely (teleworkable)
- A reversal from 2013 — rated low automation risk then (33rd), high AI task-overlap now (88th)
88th-percentile task overlap — yet about 2,400 openings a year (+21.8% projected, BLS), and observed AI use leans 7363% copilot, not hand-off (AEI) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 100th | 1.5 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) Moderate | 54th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.5), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.2 · 33rd percentile among occupations · Low
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Provide advice to clients on a contract basis, working as a consultant. | 4.2% | |
| Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings. | 0.8% | |
| Determine policy contract provisions for each type of insurance. | 0.5% | |
| Explain changes in contract provisions to customers. | 0.3% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +21.8% by 2034 |
| Projected annual openings | 2,400 |
| Employment 2024 → 2034 | 33,600 → 40,900 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Actuaries sits at the 94th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
56% mean task exposure (2025)
94th percentile of 427 placed occupations
+6 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Mathematicians, Actuaries and Statisticians · 2120 | 56% | Gradient 3 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 73.6% working with AI · 21.9% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 68.7% |
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Provide advice to clients on a contract basis, working as a consultant. | Iteration | 17.5% |
| Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. | — | 0.4% |
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Provide advice to clients on a contract basis, working as a consultant. | 95.5% | |
| Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. | 91.7% |
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
-
Help me provide advice to clients on a contract basis, working as a consultant.From: Provide advice to clients on a contract basis, working as a consultant. · 17.5% of measured AI use · task iteration
-
Help me design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.From: Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. · 0.4% of measured AI use
Tasks
All 15 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits. Core · importance 4.5
- Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business. Core · importance 4.5
- Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates. Core · importance 4.4
- Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. Core · importance 4.4
- Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public. Core · importance 4.3
- Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information. Core · importance 4.2
- Provide advice to clients on a contract basis, working as a consultant. Core · importance 3.8
- Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies. Core · importance 3.3
- Negotiate terms and conditions of reinsurance with other companies. Core · importance 3.3
- Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings. Supplemental · importance 3.1
- Testify before public agencies on proposed legislation affecting businesses. Core · importance 3.0
- Determine policy contract provisions for each type of insurance. Core · importance 3.0
- Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person disabled or killed in an accident. Core · importance 2.7
- Explain changes in contract provisions to customers. Supplemental · importance 2.4
- Manage credit and help price corporate security offerings. Supplemental · importance 2.4
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Analyze data to determine premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.
Work activities
- Negotiate contracts with clients or service providers.
- Manage financial activities of the organization.
- Analyze health-related data.
- Collaborate with others to develop or implement marketing strategies.
- Develop organizational goals or objectives.
- Testify at legal or legislative proceedings.
- Advise customers on technical or procedural issues.
- Advise others on legal or regulatory compliance matters.
- Analyze data to identify trends or relationships among variables.
- Provide customer service to clients or users.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| Mathematics | 4.9 | |
| Computers and Electronics | 4.0 | |
| Economics and Accounting | 3.8 | |
| English Language | 3.7 | |
| Law and Government | 3.0 | |
| Administration and Management | 2.9 |
Abilities
| Mathematical Reasoning | 4.6 | |
| Inductive Reasoning | 4.1 | |
| Number Facility | 4.1 | |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 4.0 | |
| Deductive Reasoning | 4.0 | |
| Category Flexibility | 4.0 | |
| Problem Sensitivity | 3.9 | |
| Information Ordering | 3.9 | |
| Near Vision | 3.8 | |
| Speech Clarity | 3.6 | |
| Speech Recognition | 3.4 | |
| Flexibility of Closure | 3.3 | |
| Selective Attention | 3.3 | |
| Fluency of Ideas | 3.1 |
Essential skills
| Reading Comprehension | 4.3 | |
| Mathematics | 4.3 | |
| Critical Thinking | 4.3 | |
| Active Listening | 4.0 | |
| Speaking | 3.9 | |
| Writing | 3.5 | |
| Active Learning | 3.3 | |
| Monitoring | 3.1 |
Transferable skills
| Judgment and Decision Making | 4.3 | |
| Complex Problem Solving | 4.0 | |
| Systems Evaluation | 4.0 | |
| Systems Analysis | 3.9 | |
| Social Perceptiveness | 3.0 | |
| Coordination | 3.0 | |
| Service Orientation | 3.0 | |
| Operations Analysis | 3.0 | |
| Time Management | 3.0 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 41.
Tools & technology
Showing the top 40 of 42.
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 4 — Job Zone Four: Considerable Preparation Needed
- Education
- Most of these occupations require a four-year bachelor's degree, but some do not.
- Typical entry-level education
- Bachelor's degree · BLS, the typical path — not a requirement
- Related experience
- A considerable amount of work-related skill, knowledge, or experience is needed for these occupations. For example, an accountant must complete four years of college and work for several years in accounting to be considered qualified.
- Preparation level
- SVP (7.0 to < 8.0) — total schooling plus on-the-job experience.
What to study: Business, Management, Marketing, and Related Support Services , Mathematics and Statistics . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Education of current workers
Share of people in this occupation at each level of education.
| Bachelor's Degree | 78.6% | |
| Post-Baccalaureate Certificate | 10.7% | |
| First Professional Degree | 10.7% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Interest areas
| Mathematics/Statistics | 6.5 | |
| Finance | 5.4 | |
| Accounting | 4.5 | |
| Management/Administration | 3.0 | |
| Information Technology | 2.9 | |
| Public Speaking | 2.9 | |
| Business Initiatives | 2.6 | |
| Law | 2.6 |
Career interests (Holland / RIASEC)
| Conventional | 6.3 | |
| Investigative | 4.5 | |
| Enterprising | 3.3 |
Work styles
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Integrity | 4.0 | |
| Cautiousness | 3.0 | |
| Intellectual Curiosity | 2.6 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $75,240 |
| 25th percentile | $90,970 |
| Median (50th) | $125,770 |
| 75th percentile | $164,860 |
| 90th percentile | $206,430 |
| People employed | 28,340 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Finance and Insurance · Sector | 20,900 | $126,830 |
| Professional, Scientific, and Technical Services · Sector | 4,590 | $111,640 |
| Insurance Agencies and Brokerages · National industry | 3,590 | $138,260 |
| Direct Health and Medical Insurance Carriers · National industry | 3,440 | $107,360 |
| Management of Companies and Enterprises · Sector | 1,130 | $133,030 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 350 | $146,130 |
| Educational Services · Sector | 150 | $81,840 |
| Health Care and Social Assistance · Sector | 120 | $83,710 |
| Other Services (except Public Administration) · Sector | 80 | $170,660 |
| Temporary Help Services · National industry | 60 | $158,760 |
| Information · Sector | — | $162,770 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Direct Health and Medical Insurance Carriers · National industry | 41.67× | 3,440 |
| Insurance Agencies and Brokerages · National industry | 19.72× | 3,590 |
| Finance and Insurance · Sector | 18.26× | 20,900 |
| Professional, Scientific, and Technical Services · Sector | 2.32× | 4,590 |
| Management of Companies and Enterprises · Sector | 2.19× | 1,130 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.21× | 350 |
| Educational Services · Sector | 0.06× | 150 |
| Health Care and Social Assistance · Sector | 0.03× | 120 |
Related occupations
Part of the Financial Services career cluster.
- Personal Financial Advisors · 13-2052.00 Compare →
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- Accountants and Auditors · 13-2011.00 Compare →
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- Financial Quantitative Analysts · 13-2099.01 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Actuaries — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- Bachelor's degree · typical entry level
- Job zone 4 — Job Zone Four: Considerable Preparation Needed · preparation tier
- Business, Management, Marketing, and Related Support Services · field of study
- Mathematics and Statistics · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 94th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Actuaries show 88th-percentile AI task overlap — and about 2,400 annual U.S. openings
- Actuaries rank in the 88th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 2,400 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be growing fast (+21.8%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $125,770, across about 28,340 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 74% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Actuaries show 88th-percentile AI task overlap — and about 2,400 annual U.S. openings • Actuaries rank in the 88th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 2,400 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be growing fast (+21.8%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $125,770, across about 28,340 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 74% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Actuaries". https://singulariki.com/roles/role-15-2011-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Actuaries." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-15-2011-00
APA
Singulariki. (2026). Actuaries. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-15-2011-00
BibTeX
@misc{singulariki-role-15-2011-00,
title = {Actuaries},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-15-2011-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.