# Claims Adjusters, Examiners, and Investigators

> Review settled claims to determine that payments and settlements are made in accordance with company practices and procedures. Confer with legal counsel on claims requiring litigation. May also settle insurance claims.

- **SOC code:** 13-1031.00
- **Canonical URL:** https://singulariki.com/roles/role-13-1031-00
- **Also known as:** Claims Adjuster, Claims Analyst, Claims Examiner, Claims Representative, Claims Specialist, Corporate Claims Examiner, Field Claims Adjuster, General Adjuster
- **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):
- Examine claims forms and other records to determine insurance coverage.
- Analyze information gathered by investigation and report findings and recommendations.
- Pay and process claims within designated authority level.
- Investigate, evaluate, and settle claims, applying technical knowledge and human relations skills to effect fair and prompt disposal of cases and to contribute to a reduced loss ratio.
- Verify and analyze data used in settling claims to ensure that claims are valid and that settlements are made according to company practices and procedures.
- Review police reports, medical treatment records, medical bills, or physical property damage to determine the extent of liability.
- Investigate and assess damage to property and create or review property damage estimates.
- Interview or correspond with agents and claimants to correct errors or omissions and to investigate questionable claims.
- Interview or correspond with claimants, witnesses, police, physicians, or other relevant parties to determine claim settlement, denial, or review.
- Enter claim payments, reserves and new claims on computer system, inputting concise yet sufficient file documentation.
- Resolve complex, severe exposure claims, using high service oriented file handling.
- Adjust reserves or provide reserve recommendations to ensure that reserve activities are consistent with corporate policies.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- English Language _(knowledge)_
- Written Comprehension _(ability)_
- Reading Comprehension _(essential_skill)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Active Listening _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Deductive Reasoning _(ability)_
- Inductive Reasoning _(ability)_
- Speaking _(essential_skill)_
- Written Expression _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_
- Mathematics _(Common Skill)_
- Writing _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Information Ordering _(Specialized Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Outlook _(hot technology, in demand)_
- Apple Safari _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Mozilla Firefox _(hot technology)_
- Zoom _(hot technology)_
- Xactware Xactimate _(in demand)_
- 4n6xprt Systems StiffCalcs
- ADP software

## AI exposure & outlook

- **AI task-overlap index:** 85th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 85th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 88th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 70th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 97th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -5.1% growth (Declining); 21.1k annual openings; 356.1k → 337.9k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $76,790; 305,020 employed.

## 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-13-1031-00_