Credit Authorizers, Checkers, and Clerks

Office & Administrative Support

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

    0.130

    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

    0.64

    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, 1% 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
Keep records of customers' charges and payments.

O*NET Task ID 23297

1.0
File sales slips in customers' ledgers for billing purposes.

O*NET Task ID 23302

1.0
Receive charge slips or credit applications by mail, or receive information from salespeople or merchants by telephone.

O*NET Task ID 23303

1.0
Mail charge statements to customers.

O*NET Task ID 23304

1.0
Relay credit report information to subscribers by mail or by telephone.

O*NET Task ID 23306

1.0
Prepare reports of findings and recommendations.

O*NET Task ID 23311

1.0
Compile and analyze credit information gathered by investigation.

O*NET Task ID 23298

0.5
Obtain information about potential creditors from banks, credit bureaus, and other credit services, and provide reciprocal information if requested.

O*NET Task ID 23299

0.5
Interview credit applicants by telephone or in person to obtain personal and financial data needed to complete credit report.

O*NET Task ID 23300

0.5
Evaluate customers' computerized credit records and payment histories to decide whether to approve new credit, based on predetermined standards.

O*NET Task ID 23301

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