Commercial Loan Officers

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

    0.186

    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

    0.60

    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, 4% 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
Explain to customers the different types of loans and credit options that are available, as well as the terms of those services.

O*NET Task ID 3410

1.0
Review loan agreements to ensure that they are complete and accurate according to policy.

O*NET Task ID 3413

1.0
Compute payment schedules.

O*NET Task ID 3414

1.0
Prepare reports to send to customers whose accounts are delinquent, and forward irreconcilable accounts for collector action.

O*NET Task ID 3426

1.0
Authorize or sign mail collection letters.

O*NET Task ID 21636

1.0
Review billing for accuracy.

O*NET Task ID 21647

1.0
Approve loans within specified limits, and refer loan applications outside those limits to management for approval.

O*NET Task ID 3407

0.5
Meet with applicants to obtain information for loan applications and to answer questions about the process.

O*NET Task ID 3408

0.5
Analyze applicants' financial status, credit, and property evaluations to determine feasibility of granting loans.

O*NET Task ID 3409

0.5
Obtain and compile copies of loan applicants' credit histories, corporate financial statements, and other financial information.

O*NET Task ID 3411

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