Actuaries
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.
BLS SOC 15-2011 · O*NET-SOC 15-2011.00 · BLS 2025–2035 · O*NET 31.0
Median annual wage, 2025
$130,000
Projected change, 2025–2035
9.2%
2025–2035 annual average openings
1,500
AI exposure
Very high
Editorial analysis
AI can run more scenarios. The actuary still has to decide which risks count.
Actuaries already work with sophisticated statistical models, so AI is less a sudden replacement than another expansion of the analytical toolkit. It can help write code, clean data, review documents, discover patterns, generate scenarios, explain model results, and speed research across insurance, pensions, healthcare, finance, and enterprise risk.
More powerful models create more questions about the model
A complex prediction may improve accuracy while becoming harder to explain. Actuaries have to consider whether the underlying data is appropriate, whether a model behaves fairly, how it responds under unusual conditions, and whether decision-makers understand its limitations.
- Automation reduces time spent on routine data preparation and coding.
- Actuaries can examine many more scenarios and emerging risks.
- Model validation and governance become more important as AI models grow more complex.
- Professional judgment remains essential when assumptions affect pricing, reserves, benefits, or long-term financial commitments.
Actuaries who combine traditional probability and financial expertise with data science, AI validation, and strong communication may become especially valuable. The profession's advantage is not simply doing calculations. It is taking responsibility for decisions under uncertainty.
Labor-market picture
Two measures, different questions
These measures offer two different ways to understand the occupation: BLS projections show how national employment may change over time, while relative AI exposure highlights where tasks may intersect with AI-related capabilities. Read them with the work profile and your local context to decide what deserves closer investigation.
Employment
- Employment, 2025
- 31,200 jobs
- Projected employment, 2035
- 34,100 jobs
- Projected change, 2025–2035
- 2,900 jobs (9.2%)
Relative AI exposure
BLS category: Very high. This is a relative task-exposure classification, not a forecast of employment change, automation, wages, or worker replacement.
Relative AI exposure · Very high
Work profile
Representative tasks
These task statements come from O*NET 31.0. They are source facts, not labels assigned by the AI exposure measure.
- Analyze data to determine premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.
- Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.
- Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.
- 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.
- Provide advice to clients on a contract basis, working as a consultant.
- Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.
Skills that matter
Skills in the source profile
Importance uses O*NET's 1–5 scale. Level uses its 0–7 scale. Select a skill to see its source definition and reported values.
Technology and tools
These technology records come from O*NET and show software or tools reported in the occupation profile. They describe work context, not a required checklist for every employer.
- Financial analysis software
- Object or component oriented development software
- Compliance software
- Data base user interface and query software
- Electronic mail software
- Analytical or scientific software
- Spreadsheet software
- Office suite software
Preparation
- Typical entry education
- Bachelor's degree
- Related experience
- None
- On-the-job training
- Long-term on-the-job training
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Source provenance
Labor-market and exposure facts: bls-ep-2025-35. Work profile: onet-31.0. Verify release details on the Sources page.