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.
- Decline excessive risks. · 100.0% need a human
Insurance Underwriters
Occupation · SOC 13-2053.00
Review individual applications for insurance to evaluate degree of risk involved and determine acceptance of applications.
Also called: Account Underwriter · Life Underwriter · Personal Lines Underwriter · Underwriter · Automobile and Property Underwriter · Commercial Lines Underwriter · Health Underwriter · Underwriting Consultant · Account Manager Underwriter · Bond Underwriter · Casualty Underwriter · Commercial Credit Underwriter
Job family: Business and Financial Operations Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-13-2053-00/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.
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.
- Declining employment outlook (-2.6% by 2034)
- 8,200 openings/yr
- Moderate AI exposure
- Median pay $79,880/yr
- Can largely be done remotely (teleworkable)
- Both the 2013 computerization estimate and today's AI exposure rank this high
83rd-percentile task overlap — yet about 8,200 openings a year (-2.6% projected, BLS) . 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 | 89th | 1.3 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) Moderate | 55th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.6), 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 1.0 · 99th percentile among occupations · High
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.
| Decline excessive risks. | 0.9% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Declining · -2.6% by 2034 |
| Projected annual openings | 8,200 |
| Employment 2024 → 2034 | 127,000 → 123,700 |
“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.
Insurance Underwriters sits at the 90th 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.
53% mean task exposure (2025)
90th percentile of 427 placed occupations
+7 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Insurance Representatives · 3321 | 53% | 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.
| Most common way people use AI here | none · |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
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 |
|---|---|---|
| Decline excessive risks. | none | 1.2% |
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.
| Decline excessive risks. | 100.0% |
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 decline excessive risks.From: Decline excessive risks. · 1.2% of measured AI use · none
Tasks
All 7 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property. Core · importance 4.5
- Decline excessive risks. Core · importance 4.4
- Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies. Core · importance 4.3
- Evaluate possibility of losses due to catastrophe or excessive insurance. Core · importance 4.2
- Review company records to determine amount of insurance in force on single risk or group of closely related risks. Core · importance 4.0
- Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, profitable distribution of risks, using reference materials. Core · importance 4.0
- Authorize reinsurance of policy when risk is high. Core · importance 3.9
Emerging tasks
Newer responsibilities O*NET has flagged as growing for this occupation.
- Answer agents' questions about insurance coverage.
Work activities
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
| English Language | 4.0 | |
| Customer and Personal Service | 3.9 | |
| Mathematics | 3.3 | |
| Sales and Marketing | 3.2 | |
| Administrative | 2.9 |
Abilities
| Written Comprehension | 3.9 | |
| Written Expression | 3.8 | |
| Inductive Reasoning | 3.8 | |
| Oral Expression | 3.6 | |
| Oral Comprehension | 3.5 | |
| Deductive Reasoning | 3.5 | |
| Problem Sensitivity | 3.4 | |
| Speech Clarity | 3.4 | |
| Category Flexibility | 3.3 | |
| Near Vision | 3.3 | |
| Speech Recognition | 3.3 | |
| Information Ordering | 3.1 | |
| Flexibility of Closure | 3.0 | |
| Fluency of Ideas | 2.9 | |
| Originality | 2.9 | |
| Mathematical Reasoning | 2.9 | |
| Selective Attention | 2.9 |
Essential skills
| Reading Comprehension | 3.8 | |
| Active Listening | 3.8 | |
| Writing | 3.8 | |
| Critical Thinking | 3.8 | |
| Speaking | 3.6 | |
| Active Learning | 3.1 | |
| Monitoring | 3.0 | |
| Mathematics | 2.9 | |
| Learning Strategies | 2.8 |
Transferable skills
| Judgment and Decision Making | 3.5 | |
| Complex Problem Solving | 3.1 | |
| Social Perceptiveness | 3.0 | |
| Coordination | 3.0 | |
| Service Orientation | 3.0 | |
| Persuasion | 2.9 | |
| Time Management | 2.9 | |
| Negotiation | 2.8 | |
| Instructing | 2.8 |
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
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 . 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 | 70.0% | |
| High School Diploma | 10.0% | |
| Associate's Degree (or other 2-year degree) | 10.0% | |
| Some College Courses | 6.7% | |
| Post-Baccalaureate Certificate | 3.3% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Conventional | 6.2 | |
| Enterprising | 4.5 | |
| Investigative | 3.6 | |
| Social | 2.8 |
Interest areas
| Office Work | 5.4 | |
| Finance | 4.3 | |
| Accounting | 3.4 | |
| Mathematics/Statistics | 3.0 | |
| Management/Administration | 2.3 | |
| Law | 2.1 | |
| Sales | 2.1 | |
| Business Initiatives | 1.9 |
Work styles
| Dependability | 4.0 | |
| Attention to Detail | 3.0 | |
| Cautiousness | 2.7 | |
| Integrity | 2.2 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $51,640 |
| 25th percentile | $63,070 |
| Median (50th) | $79,880 |
| 75th percentile | $104,820 |
| 90th percentile | $138,020 |
| People employed | 107,820 |
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 | 101,320 | $79,960 |
| Insurance Agencies and Brokerages · National industry | 31,670 | $79,200 |
| Direct Health and Medical Insurance Carriers · National industry | 3,920 | $81,240 |
| Management of Companies and Enterprises · Sector | 3,410 | $78,490 |
| Professional, Scientific, and Technical Services · Sector | 1,080 | $78,970 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 870 | $75,000 |
| Real Estate and Rental and Leasing · Sector | 240 | $82,980 |
| Temporary Help Services · National industry | 170 | $60,830 |
| Health Care and Social Assistance · Sector | 140 | $99,940 |
| Other Services (except Public Administration) · Sector | 90 | $78,870 |
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 |
|---|---|---|
| Insurance Agencies and Brokerages · National industry | 45.74× | 31,670 |
| Finance and Insurance · Sector | 23.27× | 101,320 |
| Direct Health and Medical Insurance Carriers · National industry | 12.48× | 3,920 |
| Management of Companies and Enterprises · Sector | 1.74× | 3,410 |
| Real Estate and Rental and Leasing · Sector | 0.14× | 240 |
| Professional, Scientific, and Technical Services · Sector | 0.14× | 1,080 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.14× | 870 |
| Temporary Help Services · National industry | 0.09× | 170 |
Related occupations
Part of the Financial Services career cluster.
- Financial Risk Specialists · 13-2054.00 Compare →
- Credit Analysts · 13-2041.00 Compare →
- Loan Officers · 13-2072.00 Compare →
- Insurance Sales Agents · 41-3021.00 Compare →
- Claims Adjusters, Examiners, and Investigators · 13-1031.00 Compare →
- Personal Financial Advisors · 13-2052.00 Compare →
- Securities, Commodities, and Financial Services Sales Agents · 41-3031.00 Compare →
- Loan Interviewers and Clerks · 43-4131.00 Compare →
- Financial and Investment Analysts · 13-2051.00 Compare →
- Credit Counselors · 13-2071.00 Compare →
- Insurance Claims and Policy Processing Clerks · 43-9041.00 Compare →
- Credit Authorizers, Checkers, and Clerks · 43-4041.00 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 Insurance Underwriters — 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
Zoom out
On the global GenAI exposure gradient this work sits around the 90th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Insurance Underwriters show 83rd-percentile AI task overlap — and about 8,200 annual U.S. openings
- Insurance Underwriters rank in the 83rd 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 8,200 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 declining (-2.6%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $79,880, across about 107,820 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Insurance Underwriters show 83rd-percentile AI task overlap — and about 8,200 annual U.S. openings • Insurance Underwriters rank in the 83rd 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 8,200 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 declining (-2.6%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $79,880, across about 107,820 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Insurance Underwriters". https://singulariki.com/roles/role-13-2053-00 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. "Insurance Underwriters." 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-13-2053-00
APA
Singulariki. (2026). Insurance Underwriters. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-13-2053-00
BibTeX
@misc{singulariki-role-13-2053-00,
title = {Insurance Underwriters},
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-13-2053-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.