Actuary

VerdictHigher Risk
Signal 01 · structural exposure
87
/ 100

Actuary scores 13/100 on this site's structural exposure measure, one of the lowest readings on this site, reflecting how close statistical modeling, probability-table construction, and financial forecasting sit to the core task LLMs were built to handle, and real, current AI adoption in the profession confirms it: actuaries already use generative AI daily for drafting reports, debugging code, and summarizing regulatory filings.

Signal 02 · real-world AI usage
Not yet available

We have not ingested real-world usage data for this occupation yet. We show a band only where genuine data exists, rather than estimate one.

What this is not
Neither reading measures whether any single actuary has lost work to AI. That data does not exist anywhere yet — both signals describe tasks, not headcount.
How we calculated this →

What a actuary actually does

Actuaries analyze the financial cost of risk and uncertainty, using statistics, mathematics, and financial theory to figure out how much an insurance policy, pension plan, or annuity should cost and how much money an organization needs to set aside to cover future claims.

The work starts with data: mortality rates, accident frequencies, disability and retirement patterns, then builds probability tables and models that forecast how much a company will actually have to pay out over the life of a policy or plan.

Actuaries design and review insurance, annuity, and pension products, working with underwriters and executives on pricing and product development, and they regularly provide expert testimony in legal and legislative proceedings when a case turns on a technical risk calculation.

A significant part of the job is regulatory: actuarial opinions and reserve calculations get filed with state insurance regulators and are legally required for an insurance company to demonstrate it can pay future claims, filings a credentialed actuary personally signs and stands behind.

Why it reads this way

Actuary scores 13/100 on this site's structural exposure measure, one of the lowest readings on this site, reflecting how close statistical modeling, probability-table construction, and financial forecasting sit to the core task LLMs were built to handle, and real, current AI adoption in the profession confirms it: actuaries already use generative AI daily for drafting reports, debugging code, and summarizing regulatory filings.

What the number misses is a legal wall specific to the profession's final output. In most U. S. states, insurance companies must have a credentialed "Appointed Actuary" personally sign the annual statement of actuarial opinion certifying that reserves are adequate to pay future claims, a specific regulatory filing with real legal consequences if it's wrong, and that signature has to come from a named, credentialed individual, not a firm or a piece of software.

Industry coverage of AI adoption in actuarial work is explicit on this point: generative AI can pass basic probability exams and write code, but it lacks the legal accountability the appointed-actuary role requires, and the documented shift is toward actuaries supervising AI-assisted models, not being replaced by them. BLS projects much-faster-than-average growth for this occupation (7%+, 2024-2034, Bright Outlook designation), with no disclosed AI-driven headwind.

Skills this role draws on

We don't yet have task-by-task time-share data for this occupation, so we can't show which specific tasks carry the exposure score above. This is the real skill set instead.

Statistical and Probability ModelingRisk Assessment and PricingActuarial Software (R, SAS, Python)Regulatory Filing and Reserve CalculationInsurance/Pension Product DesignExpert TestimonyFinancial Forecasting
Pay & demand

Typical salary range (USD)

$78,570–$215,100

Average: $130,000/yr

Demand
High
Growth outlook
Strong
Projected growth
7%+ (2024-2034), much faster than the all-occupation average, with roughly 2,400 openings projected over the decade (BLS Occupational Outlook Handbook, Bright Outlook designation)

Range source: BLS OEWS wage data via O*NET OnLine (2025 release), SOC 15-2011.00 'Actuaries'
Average source: BLS OEWS wage data for Actuaries (15-2011.00) via O*NET OnLine, 2025 wage data (Annual Median Wage)

Training
Bachelor's degree (mathematics, statistics, or actuarial science) plus passing a structured sequence of professional exams administered by the Society of Actuaries (SOA) or Casualty Actuarial Society (CAS); reaching full Fellowship credential typically takes 6-10 years of exam-passing alongside full-time work
Bachelor's Degree (Mathematics, Statistics, or Actuarial Science)SOA or CAS Actuarial Exams (multi-year sequence)Associate then Fellow Credential (ASA/FSA or ACAS/FCAS)Continuing Education for Credential Maintenance
Research verdict
Assessed as of 2026-08-17
AI-resistant despite the paperwork

Actuary scores 13/100 (Low structural confidence — only 1 of 2 pillars covers this code, AIOE ingestion was unavailable during this research pass due to a source-side rate limit; Medium overall confidence given strong corroborating category-2 evidence), one of the lowest readings on this site, reflecting how close statistical/probability modeling sits to core LLM capability, confirmed by real, current daily AI use in the profession (report drafting, code, regulatory-filing summarization). What holds the verdict up: most states require a credentialed "Appointed Actuary" to personally sign the annual statement of actuarial opinion certifying reserve adequacy, a specific regulatory filing with real legal consequences, tied to a named individual. Industry coverage is explicit that generative AI lacks the legal accountability this role requires, with the documented shift being actuaries supervising AI models, not being replaced. BLS projects 7%+ growth (2024-2034, Bright Outlook), as of 2026-08-17.

This is a researched judgment (checklist v1.2), not a statistical measurement — it sits alongside, not instead of, the structural exposure signal above. Next scheduled review: 2027-02-17.

Sources (5)
Last updated: December 2025Source: BLS OEWS wage data via O*NET OnLine (2025 release), SOC 15-2011.00 'Actuaries'

Data sources & methodology

Salary data: BLS OEWS wage data via O*NET OnLine (2025 release), SOC 15-2011.00 'Actuaries'. Min/max figures represent the 10th–90th percentile annual wage range across the United States. Average figure sourced separately: BLS OEWS wage data for Actuaries (15-2011.00) via O*NET OnLine, 2025 wage data (Annual Median Wage).

Automation Risk Score: Based on O*NET occupational analysis (15-2011.00) evaluating task complexity, physical requirements, social intelligence, and environmental variability. Methodology based on research from Frey & Osborne (Oxford, 2017).

Growth projections: 7%+ (2024-2034), much faster than the all-occupation average, with roughly 2,400 openings projected over the decade (BLS Occupational Outlook Handbook, Bright Outlook designation), based on BLS Occupational Outlook Handbook.

Learn more about our methodology