# 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.

- **SOC code:** 15-2011.00
- **Canonical URL:** https://singulariki.com/roles/role-15-2011-00
- **Also known as:** Actuarial Analyst, Actuary, Consulting Actuary, Pricing Actuary, Actuarial Associate, Actuarial Consultant, Corporate Actuary, Health Actuary
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.
- 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.
- 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.
- Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.
- Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information.
- Provide advice to clients on a contract basis, working as a consultant.
- Negotiate terms and conditions of reinsurance with other companies.
- Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies.
- Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings.
- Testify before public agencies on proposed legislation affecting businesses.
- Determine policy contract provisions for each type of insurance.

**Emerging tasks** (O*NET):
- Analyze data to determine premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mathematics _(knowledge)_
- Mathematical Reasoning _(ability)_
- Reading Comprehension _(essential_skill)_
- Mathematics _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Judgment and Decision Making _(transferable_skill)_
- Inductive Reasoning _(ability)_
- Number Facility _(ability)_
- Active Listening _(essential_skill)_
- Complex Problem Solving _(transferable_skill)_
- Systems Evaluation _(transferable_skill)_
- Oral Comprehension _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Listening _(Common Skill)_
- Systems Analysis _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- English Language _(Common Skill)_
- Writing _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Power BI _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Microsoft Visual Basic for Applications VBA _(hot technology, in demand)_
- Python _(hot technology, in demand)_
- R _(hot technology, in demand)_
- SAS _(hot technology, in demand)_
- Structured query language SQL _(hot technology, in demand)_
- Tableau _(hot technology, in demand)_
- C++ _(hot technology)_
- IBM SPSS Statistics _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 88th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 100th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 95th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 54th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 33rd percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 21.8% growth (Growing fast); 2.4k annual openings; 33.6k → 40.9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $125,770; 28,340 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 22% automation, 74% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** task iteration.

**Tasks most handed to AI here:**
- Provide advice to clients on a contract basis, working as a consultant. _(17.5% of measured AI use; task iteration)_
- Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. _(0.4% of measured AI use)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me provide advice to clients on a contract basis, working as a consultant.
- Help me design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-15-2011-00_