# Insurance Appraisers, Auto Damage

> Appraise automobile or other vehicle damage to determine repair costs for insurance claim settlement. Prepare insurance forms to indicate repair cost or cost estimates and recommendations. May seek agreement with automotive repair shop on repair costs.

- **SOC code:** 13-1032.00
- **Canonical URL:** https://singulariki.com/roles/role-13-1032-00
- **Also known as:** Appraiser, Automobile Appraiser (Auto Appraiser), Automobile Damage Appraiser (Auto Damage Appraiser), Material Damage Appraiser, Damage Appraiser, Field Appraiser, Field Inspector, Insurance Appraiser
- **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):
- Evaluate practicality of repair as opposed to payment of market value of vehicle before accident.
- Review repair cost estimates with automobile repair shop to secure agreement on cost of repairs.
- Examine damaged vehicle to determine extent of structural, body, mechanical, electrical, or interior damage.
- Prepare insurance forms to indicate repair cost estimates and recommendations.
- Estimate parts and labor to repair damage, using standard automotive labor and parts cost manuals and knowledge of automotive repair.
- Determine salvage value on total-loss vehicle.
- Arrange to have damage appraised by another appraiser to resolve disagreement with shop on repair cost.

**Emerging tasks** (O*NET):
- Contact vendors to locate replacement parts for vehicles.
- Discuss insurance claims with customers or damage claimants.
- Review repair cost estimates and negotiate with automobile repair shops to secure agreement on cost of repairs.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- English Language _(knowledge)_
- Writing _(essential_skill)_
- Speaking _(essential_skill)_
- Written Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Written Expression _(ability)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Speech Recognition _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Writing _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Active Listening _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Time Management _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Social Perceptiveness _(Common Skill)_
- Microsoft Word _(Common Skill)_

**Tools & technology:**
- Microsoft Office software _(hot technology, in demand)_
- Adobe Acrobat _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Disassembler software _(in demand)_
- A-T Solutions Easy Street Draw
- App Software Associations AppTrak.net
- Cost estimating software
- Email 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.):** 63rd percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 95th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 84th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 97th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -8.2% growth (Declining); 0.5k annual openings; 9.2k → 8.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $76,650; 7,790 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-13-1032-00_