Insurance Underwriters

Business & Financial Operations

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-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

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.063

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.53

    0.09Range of values carried here0.70

    Group-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, 14% score at or above this value.

    Source dataset

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 3 hidden tasks
TaskClaude.aiRaw / share %APIRaw / share %
Decline excessive risks.

13-2053

0.012389.10.011869.3
Write to field representatives, medical personnel, and others to obtain further information, quote rates, or explain company underwriting policies.

13-2053

0.001510.90
Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe profitable distribution of risks, using reference materials.
00.002816.3
Examine documents to determine degree of risk from such factors as applicant financial standing and value and condition of property.
00.002514.5

Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page

Occupation information

Recent Changes Affecting This Occupation

Apr 2026: ATE 0.42 by 2027 in SF Bay Tier 1. Rule-application heavy roles with thin P1 (interpersonal) and P2 (regulatory) penalties show fast agentic exposure climb.

[Source: arXiv 2604.00186 (Gupta & Kumar, 2026)]

Mar 2026: Published evergreen blog analysis: AI exposure 64%, automation risk 62/100 in 2025.

[Source: AI Changing Work Blog]