Claims Adjusters, Examiners, and Investigators
Business & Financial OperationsAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.082
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.45
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 25% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 24 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Analyze information gathered by investigation and report findings and recommendations.13-1031 | 0.002862.8 | 0.002321.6 |
Examine titles to property to determine validity and act as company agent in transactions with property owners.13-1031 | 0.001737.2 | 0.001614.7 |
Examine claims investigated by insurance adjusters, further investigating questionable claims to determine whether to authorize payments. | —0 | 0.002926.5 |
Prepare reports to be submitted to company's data processing department. | —0 | 0.002321.6 |
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. | —0 | 0.001715.7 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Enter claim payments, reserves and new claims on computer system, inputting concise yet sufficient file documentation.O*NET Task ID 21429 | 1.0 |
Communicate with reinsurance brokers to obtain information necessary for processing claims.O*NET Task ID 21440 | 1.0 |
Prepare reports to be submitted to company's data processing department.O*NET Task ID 21441 | 1.0 |
Examine claims forms and other records to determine insurance coverage.O*NET Task ID 21417 | 0.5 |
Analyze information gathered by investigation and report findings and recommendations.O*NET Task ID 21418 | 0.5 |
Review police reports, medical treatment records, medical bills, or physical property damage to determine the extent of liability.O*NET Task ID 21419 | 0.5 |
Investigate and assess damage to property and create or review property damage estimates.O*NET Task ID 21420 | 0.5 |
Interview or correspond with agents and claimants to correct errors or omissions and to investigate questionable claims.O*NET Task ID 21421 | 0.5 |
Interview or correspond with claimants, witnesses, police, physicians, or other relevant parties to determine claim settlement, denial, or review.O*NET Task ID 21422 | 0.5 |
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.O*NET Task ID 21423 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page