Bank Tellers
Office & Administrative SupportAI exposure
- Data source: BLSPublished: 2026-08
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
0.023
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.58
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, 5% 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 26 hidden tasks| Task | Claude.aiRaw / share % |
|---|---|
Identify transaction mistakes when debits and credits do not balance.43-3071 | 0.006051.3 |
Compute financial fees, interest, and service charges.43-3071 | 0.005648.7 |
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 |
|---|---|
Balance currency, coin, and checks in cash drawers at ends of shifts and calculate daily transactions, using computers, calculators, or adding machines.O*NET Task ID 2549 | 1.0 |
Enter customers' transactions into computers to record transactions and issue computer-generated receipts.O*NET Task ID 2553 | 1.0 |
Identify transaction mistakes when debits and credits do not balance.O*NET Task ID 2555 | 1.0 |
Perform clerical tasks, such as typing, filing, and microfilm photography.O*NET Task ID 2562 | 1.0 |
Compute financial fees, interest, and service charges.O*NET Task ID 2570 | 1.0 |
Compose, type, and mail customer statements and other correspondence related to issues such as discrepancies and outstanding unpaid items.O*NET Task ID 2572 | 1.0 |
Issue checks to bond owners in settlement of transactions.O*NET Task ID 2573 | 1.0 |
Inform customers about foreign currency regulations and compute transaction fees for currency exchanges.O*NET Task ID 2574 | 1.0 |
Quote unit exchange rates, following daily international rate sheets or computer displays.O*NET Task ID 2575 | 1.0 |
Prepare work schedules for staff.O*NET Task ID 2576 | 1.0 |
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