Commercial Loan Officers
Business & Financial OperationsAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-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
- Data source: AnthropicPublished: 2026-03
0.186
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.60
0.09Range of values carried here0.70Group-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.
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 15 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Explain to customers the different types of loans and credit options that are available, as well as the terms of those services.13-2072 | 0.057064.5 | —0 |
Market bank products to individuals and firms, promoting bank services that may meet customers' needs.13-2072 | 0.011112.5 | 0.019125.5 |
Handle customer complaints and take appropriate action to resolve them.13-2072 | 0.009510.8 | 0.051268.7 |
Work with clients to identify their financial goals and to find ways of reaching those goals.13-2072 | 0.00374.2 | —0 |
Prepare reports to send to customers whose accounts are delinquent, and forward irreconcilable accounts for collector action.13-2072 | 0.00262.9 | —0 |
Analyze potential loan markets and develop referral networks to locate prospects for loans.13-2072 | 0.00242.7 | —0 |
Analyze applicants' financial status, credit, and property evaluations to determine feasibility of granting loans.13-2072 | 0.00212.3 | 0.00202.7 |
Obtain and compile copies of loan applicants' credit histories, corporate financial statements, and other financial information. | —0 | 0.00233.1 |
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.
| Task | βE1 + 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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page