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

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.

TaskβE1 + 0.5 × E2
Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.

O*NET Task ID 1262

1.0
Decline excessive risks.

O*NET Task ID 1261

0.5
Evaluate possibility of losses due to catastrophe or excessive insurance.

O*NET Task ID 1263

0.5
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.

O*NET Task ID 1264

0.5
Review company records to determine amount of insurance in force on single risk or group of closely related risks.

O*NET Task ID 1265

0.5
Authorize reinsurance of policy when risk is high.

O*NET Task ID 1266

0.5
Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.

O*NET Task ID 21045

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®, 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]