Financial Examiners

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

    0.043

    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

    0.62

    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, 3% 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 1 hidden task

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
Train other examiners in the financial examination process.

O*NET Task ID 7322

1.0
Investigate activities of institutions to enforce laws and regulations and to ensure legality of transactions and operations or financial solvency.

O*NET Task ID 7314

0.5
Review and analyze new, proposed, or revised laws, regulations, policies, and procedures to interpret their meaning and determine their impact.

O*NET Task ID 7315

0.5
Plan, supervise, and review work of assigned subordinates.

O*NET Task ID 7316

0.5
Recommend actions to ensure compliance with laws and regulations, or to protect solvency of institutions.

O*NET Task ID 7317

0.5
Examine the minutes of meetings of directors, stockholders, and committees to investigate the specific authority extended at various levels of management.

O*NET Task ID 7318

0.5
Prepare reports, exhibits, and other supporting schedules that detail an institution's safety and soundness, compliance with laws and regulations, and recommended solutions to questionable financial conditions.

O*NET Task ID 7319

0.5
Review balance sheets, operating income and expense accounts, and loan documentation to confirm institution assets and liabilities.

O*NET Task ID 7320

0.5
Review audit reports of internal and external auditors to monitor adequacy of scope of reports or to discover specific weaknesses in internal routines.

O*NET Task ID 7321

0.5
Establish guidelines for procedures and policies that comply with new and revised regulations and direct their implementation.

O*NET Task ID 7323

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. Part of the 91.7% of financial occupations crossing moderate-risk threshold in Tier 1 by 2027.

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

Mar 2026: Published evergreen blog analysis: AI exposure 63%, automation risk 46/100 in 2025.

[Source: AI Changing Work Blog]