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Direct Health and Medical Insurance Carriers

National industry · NAICS 524114

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Direct Health and Medical Insurance Carriers is a U.S. industry in the NAICS classification. The Bureau of Labor Statistics estimates about 449,090 workers across 157 detailed occupations in it. A typical worker earns around $86,781 a year (Singulariki estimate, see below).

This U.S. industry comprises establishments primarily engaged in initially underwriting (i.e., assuming the risk and assigning premiums) health and medical insurance policies. Group hospitalization plans and HMO establishments that provide health and medical insurance policies without providing health care services are included in this industry. Cross-References.

Employment is national May 2024 OEWS. "Typical pay" is Singulariki's own figure — the employment-weighted average of each occupation's national median wage — a rough center of the industry, not an official BLS number.

How exposed this industry is to AI

Weighting every occupation in this industry by its employment and its unified AI-exposure index (the OpenAI "GPTs are GPTs" human-rated task overlap folded with the Felten/Raj/Seamans AIOE index), this industry sits in the High band — 96th percentile across all industries.

Exposure measures how much of the work overlaps with what today's AI can do, not a prediction of automation; high-exposure industries are where AI is most likely to reshape tasks. Employment-weighted across 145 occupations that carry an exposure score. Compare every industry on the AI exposure hub.

How AI is actually used in this industry

Among measured Claude.ai (Free and Pro) conversations mapped to O*NET task statements (Anthropic Economic Index, 2026-01-15), these patterns are most associated with the occupations in this industry, weighted by its employment mix. They are shares of observed AI conversations — not of worker time, revenue, or what could be automated — and reflect one AI assistant's consumer sample, not all AI.

Signal coverage 81.7% of employment · 106/155 occupations have AEI task data
Augmentation vs. automation 49.5% working with AI · 33.4% handed to AI
Most common pattern Directive · AI does it; you give the instruction
Typical AI autonomy 3.5 / 5 · higher = AI acts more independently

Tasks driving the signal

The task families that account for the most AI activity across this industry's occupations (employment × observed usage), each attributed to the occupation it comes from.

Task Occupation How Share of signal
Troubleshoot problems involving office equipment, such as computer hardware and software. Office Clerks, General Feedback loop 7.9%
Direct or provide home health services. Registered Nurses Learning 5.6%
Participate in the work of subordinates to facilitate productivity or to overcome difficult aspects of work. First-Line Supervisors of Office and Administrative Support Workers Iteration 4.1%
Educate patients and family members about mental health and medical conditions, preventive health measures, medications, or treatment plans. Registered Nurses Learning 3.9%
Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken. Customer Service Representatives Directive 3.5%
Document findings of study and prepare recommendations for implementation of new systems, procedures, or organizational changes. Management Analysts Iteration 3.4%
Confer with customers by telephone or in person to provide information about products or services, take or enter orders, cancel accounts, or obtain details of complaints. Customer Service Representatives Directive 3.0%
Develop and distribute newsletters, brochures, or other printed materials to share information with patients or medical staff. Customer Service Representatives Iteration 2.0%
Compose business correspondence for supervisors, managers, and professionals. Insurance Claims and Policy Processing Clerks Iteration 2.0%
Present investment information, such as product risks, fees, or fund performance statistics. Managers, All Other Learning 1.8%
Explain policies, procedures, or services to patients using medical or administrative knowledge. Customer Service Representatives Learning 1.8%
Teach patient education programs that include information required to make informed health care and treatment decisions. Registered Nurses Directive 1.5%

Occupations behind the signal

The occupations whose AI-touched tasks contribute most to this industry's signal, by employment here.

Occupation Workers Share How they use AI
Customer Service Representatives 68,480 15.3% Directive
Registered Nurses 34,380 7.7% Learning
Insurance Claims and Policy Processing Clerks 24,150 5.4% Iteration
Insurance Sales Agents 21,460 4.8% Learning
Management Analysts 20,380 4.5% Iteration
Claims Adjusters, Examiners, and Investigators 18,840 4.2% Learning
First-Line Supervisors of Office and Administrative Support Workers 13,020 2.9% Iteration
Business Operations Specialists, All Other 9,200 2.1% Directive
Computer and Information Systems Managers 8,520 1.9% Learning
Accountants and Auditors 8,290 1.8% Directive
General and Operations Managers 8,140 1.8% Iteration
Medical and Health Services Managers 8,020 1.8% Iteration

This rollup is only as complete as the occupation-task matches available for the industry; the coverage figure above is shown so sparse industries do not look falsely precise. AI exposure is not the same as replacement.

Skill & tool metabolism

What this industry's work actually runs on. Each figure is the share of the industry's workers in occupations that significantly rely on a skill, knowledge area, or ability (O*NET importance ≥ 3 of 5), or that use a tool category — its employment reach. This is a measure of how widespread a requirement is across the workforce, not how intensively any one worker uses it. Shares are independent and need not add to 100%.

Based on 94.6% of this industry's employment that maps to a detailed occupation with an O*NET skill profile.

Skills

Skill Employment reach Workers
Reading Comprehension 94.6% 424,640
Active Listening 94.4% 423,900
Critical Thinking 94.3% 423,280
Speaking 94.3% 423,440
Writing 93.6% 420,280
Time Management 92.2% 414,150
Monitoring 87.3% 392,060
Social Perceptiveness 85.7% 384,660
Complex Problem Solving 84.3% 378,630
Service Orientation 79.1% 355,330
Coordination 74.9% 336,500
Judgment and Decision Making 74.1% 332,820

Knowledge areas

Knowledge area Employment reach Workers
English Language 94.5% 424,450
Customer and Personal Service 90.8% 407,570
Administration and Management 76.0% 341,140
Computers and Electronics 72.9% 327,490
Mathematics 70.2% 315,480
Administrative 52.7% 236,890
Education and Training 38.5% 172,840
Sales and Marketing 29.9% 134,210
Law and Government 27.1% 121,830
Economics and Accounting 25.3% 113,680
Personnel and Human Resources 21.7% 97,520
Psychology 21.6% 96,910

Abilities

Abilitie Employment reach Workers
Oral Comprehension 94.6% 424,710
Oral Expression 94.6% 424,710
Written Comprehension 94.6% 424,640
Speech Recognition 94.5% 424,520
Speech Clarity 94.3% 423,710
Near Vision 94.1% 422,740
Written Expression 93.8% 421,300
Information Ordering 93.7% 420,960
Problem Sensitivity 93.7% 420,840
Deductive Reasoning 93.4% 419,570
Inductive Reasoning 93.4% 419,640
Category Flexibility 78.5% 352,380

Tool categories

Tool category Employment reach Workers
Office suite software 98.9% 444,110
Spreadsheet software 98.9% 444,180
Electronic mail software 98.8% 443,860
Presentation software 98.6% 442,690
Word processing software 98.3% 441,390
Data base user interface and query software 97.8% 439,140
Document management software 91.9% 412,590
Enterprise resource planning ERP software 85.8% 385,280
Operating system software 84.8% 380,770
Internet browser software 83.8% 376,540
Medical software 78.1% 350,850
Project management software 76.5% 343,660
Business intelligence and data analysis software 70.8% 317,910
Financial analysis software 70.7% 317,510
Information retrieval or search software 70.1% 315,030

Reach = share of industry employment in occupations where the requirement is significant; it is not a per-worker usage or proficiency measure. Skill, knowledge, and ability importance is from O*NET; tool use is reported presence of a technology category.

Largest occupations

Exposure quadrant: AI task-overlap percentile vs Median pay AI task-overlap (horizontal) versus median pay (vertical), each as a percentile across all scored occupations, for 40 occupations in Direct Health and Medical Insurance Carriers. Overlap measures shared tasks with AI, not automation. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Registered Nurses General and Operations Managers Healthcare Social Workers Managers, All Other Compliance Officers Medical and Health Services Managers Office Clerks, General Business Operations Specialists, All Other Billing and Posting Clerks Computer User Support Specialists Executive Secretaries and Executive Administrative Assistants Bookkeeping, Accounting, and Auditing Clerks Operations Research Analysts AI task-overlap percentile → ↑ Median pay
The largest occupations in this industry with both an AI task-overlap score and a wage, plotted by task-overlap percentile (horizontal) and median-pay percentile (vertical). Overlap measures shared tasks with AI, not automation.

The occupations that employ the most people in this industry, with their share of the industry's workforce and national median pay for the occupation (not industry-specific pay).

Occupation Workers Share National median pay
Customer Service Representatives 68,480 15.2% $46,500
Registered Nurses 34,380 7.7% $91,370
Insurance Claims and Policy Processing Clerks 24,150 5.4% $48,280
Insurance Sales Agents 21,460 4.8% $74,060
Management Analysts 20,380 4.5% $97,710
Claims Adjusters, Examiners, and Investigators 18,840 4.2% $60,370
Software Developers 15,480 3.4% $126,950
First-Line Supervisors of Office and Administrative Support Workers 13,020 2.9% $77,370
Computer Systems Analysts 10,890 2.4% $102,580
Business Operations Specialists, All Other 9,200 2.0% $79,040
Computer and Information Systems Managers 8,520 1.9% $169,310
Accountants and Auditors 8,290 1.8% $82,750
General and Operations Managers 8,140 1.8% $161,290
Medical and Health Services Managers 8,020 1.8% $167,130
Project Management Specialists 7,650 1.7% $104,480
Managers, All Other 7,200 1.6% $159,290
Data Scientists 6,620 1.5% $104,950
Financial Managers 6,550 1.5% $167,720
Market Research Analysts and Marketing Specialists 6,110 1.4% $90,460
Compliance Officers 5,750 1.3% $80,440
Training and Development Specialists 5,020 1.1% $76,060
Human Resources Specialists 4,810 1.1% $79,330
Medical Secretaries and Administrative Assistants 4,470 1.0% $46,880
Medical Records Specialists 4,290 1.0% $63,790
Financial and Investment Analysts 4,120 0.9% $85,110
Healthcare Social Workers 4,040 0.9% $78,430
Office Clerks, General 4,030 0.9% $47,260
Bookkeeping, Accounting, and Auditing Clerks 3,930 0.9% $58,040
Insurance Underwriters 3,920 0.9% $81,240
Operations Research Analysts 3,880 0.9% $89,620
Computer Occupations, All Other 3,860 0.9% $117,770
Pharmacists 3,590 0.8% $134,630
Software Quality Assurance Analysts and Testers 3,490 0.8% $99,090
Actuaries 3,440 0.8% $107,360
Sales Managers 3,310 0.7% $165,730
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive 3,160 0.7% $52,780
Computer User Support Specialists 3,070 0.7% $67,510
Billing and Posting Clerks 3,010 0.7% $54,950
Marketing Managers 2,990 0.7% $164,100
Executive Secretaries and Executive Administrative Assistants 2,820 0.6% $78,700

Showing the top 40 of 157 occupations by employment.

Most distinctive occupations

The occupations most unusually concentrated in this industry compared with the economy as a whole. The location quotient is how many times more common an occupation is here versus its economy-wide share (a value of 5 means five times as concentrated).

Occupation Concentration Workers
Actuaries 41.67× 3,440
Insurance Claims and Policy Processing Clerks 36.2× 24,150
Claims Adjusters, Examiners, and Investigators 21.21× 18,840
Correspondence Clerks 17× 310
Insurance Sales Agents 15.69× 21,460
Insurance Underwriters 12.48× 3,920
Community Health Workers 12.44× 2,200
Operations Research Analysts 12.36× 3,880
Health Education Specialists 11.8× 2,240
Telemarketers 10.18× 1,970
Data Scientists 9.74× 6,620
Database Architects 8.8× 1,660
Customer Service Representatives 8.63× 68,480
Medical Records Specialists 7.84× 4,290
Management Analysts 7.83× 20,380
Computer Systems Analysts 7.51× 10,890
Healthcare Social Workers 7.46× 4,040
Compensation, Benefits, and Job Analysis Specialists 6.77× 2,020
Compensation and Benefits Managers 6.33× 370
Health Information Technologists and Medical Registrars 6.21× 680
Write a report on thisheadline · factoids · citation

The Direct Health and Medical Insurance Carriers workforce sits at the 96th percentile of AI task overlap — 449,090 U.S. workers

  • Weighting every occupation by its real share of Direct Health and Medical Insurance Carriers employment, the industry's workforce ranks in the 96th percentile (High band) for AI task overlap — overlap with what AI can attempt, not a measure of jobs at risk.Eloundou et al. + Felten AIOE, weighted by BLS OEWS
  • The industry employs about 449,090 U.S. workers across 157 occupations.BLS OEWS (May 2024)
  • Employment-weighted typical annual pay is about $86,781.BLS OEWS (May 2024)
  • Of AI use observed across this industry's occupations, 50% looks like augmentation rather than automation — from a Claude.ai sample, not a census.Anthropic Economic Index
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The Direct Health and Medical Insurance Carriers workforce sits at the 96th percentile of AI task overlap — 449,090 U.S. workers

• Weighting every occupation by its real share of Direct Health and Medical Insurance Carriers employment, the industry's workforce ranks in the 96th percentile (High band) for AI task overlap — overlap with what AI can attempt, not a measure of jobs at risk. (Eloundou et al. + Felten AIOE, weighted by BLS OEWS)
• The industry employs about 449,090 U.S. workers across 157 occupations. (BLS OEWS (May 2024))
• Employment-weighted typical annual pay is about $86,781. (BLS OEWS (May 2024))
• Of AI use observed across this industry's occupations, 50% looks like augmentation rather than automation — from a Claude.ai sample, not a census. (Anthropic Economic Index)

Source: Singulariki — "Direct Health and Medical Insurance Carriers". https://singulariki.com/industries/524114
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.

Data compiled June 3, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Direct Health and Medical Insurance Carriers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026. https://singulariki.com/industries/524114

APA

Singulariki. (2026). Direct Health and Medical Insurance Carriers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/industries/524114

BibTeX 
@misc{singulariki-524114,
  title  = {Direct Health and Medical Insurance Carriers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026},
  url    = {https://singulariki.com/industries/524114}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.