Bill and Account Collectors

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

    0.299

    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

    0.43

    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, 30% 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
Receive payments and post amounts paid to customer accounts.

O*NET Task ID 2472

1.0
Record information about financial status of customers and status of collection efforts.

O*NET Task ID 2474

1.0
Advise customers of necessary actions and strategies for debt repayment.

O*NET Task ID 2477

1.0
Sort and file correspondence and perform miscellaneous clerical duties, such as answering correspondence and writing reports.

O*NET Task ID 2479

1.0
Perform various administrative functions for assigned accounts, such as recording address changes and purging the records of deceased customers.

O*NET Task ID 2480

1.0
Contact insurance companies to check on status of claims payments and write appeal letters for denial on claims.

O*NET Task ID 18560

1.0
Locate and monitor overdue accounts, using computers and a variety of automated systems.

O*NET Task ID 2473

0.5
Locate and notify customers of delinquent accounts by mail, telephone, or personal visits to solicit payment.

O*NET Task ID 2475

0.5
Confer with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales, service, or credit contracts.

O*NET Task ID 2476

0.5
Persuade customers to pay amounts due on credit accounts, damage claims, or nonpayable checks, or to return merchandise.

O*NET Task ID 2478

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