Financial Risk Specialists

Business & Financial Operations

AI exposure

  • Data source: BLSPublished: 2026-08

    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: 2026-03

    0.265

    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: 2025

    0.44

    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, 28% 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
Draw charts and graphs, using computer spreadsheets, to illustrate technical reports.

O*NET Task ID 21617

1.0
Review or draft risk disclosures for offer documents.

O*NET Task ID 21633

1.0
Analyze areas of potential risk to the assets, earning capacity, or success of organizations.

O*NET Task ID 21605

0.5
Analyze new legislation to determine impact on risk exposure.

O*NET Task ID 21606

0.5
Conduct statistical analyses to quantify risk, using statistical analysis software or econometric models.

O*NET Task ID 21607

0.5
Confer with traders to identify and communicate risks associated with specific trading strategies or positions.

O*NET Task ID 21608

0.5
Consult financial literature to ensure use of the latest models or statistical techniques.

O*NET Task ID 21609

0.5
Contribute to development of risk management systems.

O*NET Task ID 21610

0.5
Determine potential environmental impacts of new products or processes on long-term growth and profitability.

O*NET Task ID 21611

0.5
Develop contingency plans to deal with emergencies.

O*NET Task ID 21612

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