Share and intensity of work current AI systems can materially affect.
SOC 15-2011
Actuaries AI displacement risk
Data preparation, rate-table construction, and draft analysis are increasingly AI-assisted. Model governance, assumption judgment, regulatory defense of pricing, and the exam credential keep actuarial work augmentation-led.
Likely potential for exposed tasks to move to software after workflow integration.
Routine valuation and reserving work automates fastest. Actuaries who own model validation, assumption setting, and communication of uncertainty to executives remain central to insurance economics.
Distribution
Where Actuaries sits across 620 tracked roles
Displacement pressure 42 — higher than 70% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
15 O*NET task statements matched to SOC 15-2011. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $130,000 (May 2025, US national). The latest BLS row matched SOC 15-2011.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Actuaries
SOC 15-2011 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 42/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Actuaries
The current evidence import matched 15 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks15
SOC15-2011
- Core task / ID 24015
Analyze data to determine premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.
- Core task / ID 3501
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.
- Core task / ID 3502
Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.
- Core task / ID 3503
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 task / ID 3506
Provide advice to clients on a contract basis, working as a consultant.
- Core task / ID 3504
Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Analyze statistical data for rate setting
Exposure 68, automation 38%, augmentation 72%.
O*NET evidence: Analyze statistical information to estimate mortality, accident, sickness, disability, ... (ID 3501)
Construct probability tables
Exposure 66, automation 38%, augmentation 68%.
O*NET evidence: Construct probability tables for events such as fires, natural disasters, and unemploym... (ID 3508)
Design and review insurance plans
Exposure 42, automation 16%, augmentation 56%.
O*NET evidence: Design, review, and help administer insurance, annuity and pension plans, determining f... (ID 3502)
Explain technical matters to executives
Exposure 30, automation 8%, augmentation 46%.
O*NET evidence: Determine, or help determine, company policy, and explain complex technical matters to ... (ID 3504)
TaskExposureAutomationAugmentation
Analyze statistical data for rate setting6838%72%
Construct probability tables6638%68%
Design and review insurance plans4216%56%
Explain technical matters to executives308%46%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateModel Risk Manager
Training horizon: 3-6 months. Skill overlap 72. Wage preservation signal 108.
- Validate predictive models
- Document assumption governance
- Audit AI pricing output
adjacent role
ModerateChief Risk Analyst
Training horizon: 3-8 months. Skill overlap 66. Wage preservation signal 104.
- Own enterprise risk frameworks
- Quantify emerging risks
- Present risk appetite to leadership
Comparison guides
Compare the next move before you commit
3-6 months
Actuaries to Model Risk Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Actuaries into Model Risk Manager.
3-8 months
Actuaries to Chief Risk Analyst
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Actuaries into Chief Risk Analyst.
What the AI risk score means for Actuaries
The displacement pressure score for Actuaries is 42. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Analyze statistical data for rate setting carries 38% automation pressure, while Analyze statistical data for rate setting carries 72% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $130,000 (May 2025, US national). Employment context: Credentialed modeling profession with steady demand. Typical education: Bachelor's degree plus actuarial exam series.
Wage vulnerability is 26, while transition feasibility is 72. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Model oversight demand is growing
- Credential barriers protect the profession
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Actuaries, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Actuarial modeling
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Assumption governance
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Regulatory communication
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
AI-assisted analysis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Model Risk Manager, such as validate predictive models.
- By 90 days, compare internal openings and external postings for Model Risk Manager or Chief Risk Analyst and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Actuaries
Will AI replace Actuaries?
Data preparation, rate-table construction, and draft analysis are increasingly AI-assisted. Model governance, assumption judgment, regulatory defense of pricing, and the exam credential keep actuarial work augmentation-led. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Actuaries work are most exposed to AI?
Analyze statistical data for rate setting and Construct probability tables show the strongest automation pressure in this model. Analyze statistical data for rate setting and Construct probability tables are better treated as AI-augmented work.
What should Actuaries learn next?
Start with Actuarial modeling, Assumption governance, Regulatory communication. The most practical adjacent paths in this model are Model Risk Manager and Chief Risk Analyst.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureMcKinsey Global Institute / 26 July 2023Generative AI and the Future of Work in AmericaO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics