Treasury Analysts
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
0.220
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.62
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, 3% 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 46 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models.13-2099 | 0.088521.2 | 0.049617.6 |
Structure or negotiate deals, such as corporate mergers, sales, or acquisitions.13-2099 | 0.050612.1 | 0.00832.9 |
Coordinate due diligence processes and the negotiation or execution of purchase or sale agreements.13-2099 | 0.043310.4 | 0.02318.2 |
Prepare all materials for transactions or execution of deals.13-2099 | 0.02586.2 | 0.01184.2 |
Employ financial models to develop solutions to financial problems or to assess the financial or capital impact of transactions.13-2099 | 0.01664.0 | 0.00752.7 |
Create client presentations of plan details.13-2099 | 0.01664.0 | 0.00220.8 |
Develop or implement risk-assessment models or methodologies.13-2099 | 0.01633.9 | 0.01394.9 |
Interpret results of financial analysis procedures.13-2099 | 0.01633.9 | 0.01264.5 |
Produce written summary reports of financial research results.13-2099 | 0.01253.0 | 0.01786.3 |
Provide application or analytical support to researchers or traders on issues such as valuations or data.13-2099 | 0.01162.8 | 0.00903.2 |
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 |
|---|---|
Prepare written reports of investigation findings.O*NET Task ID 16046 | 1.0 |
Document all investigative activities.O*NET Task ID 16053 | 1.0 |
Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques.O*NET Task ID 16035 | 0.5 |
Train others in fraud detection and prevention techniques.O*NET Task ID 16036 | 0.5 |
Research or evaluate new technologies for use in fraud detection systems.O*NET Task ID 16037 | 0.5 |
Prepare evidence for presentation in court.O*NET Task ID 16038 | 0.5 |
Negotiate with responsible parties to arrange for recovery of losses due to fraud.O*NET Task ID 16040 | 0.5 |
Advise businesses or agencies on ways to improve fraud detection.O*NET Task ID 16044 | 0.5 |
Review reports of suspected fraud to determine need for further investigation.O*NET Task ID 16045 | 0.5 |
Recommend actions in fraud cases.O*NET Task ID 16047 | 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
Occupation information
Recent Changes Related to This Occupation
Oct 2024: Brookings 2024 highlights finance as high-exposure AND low-bargaining-power: union representation in the finance sector is around 1%. Financial analysts face productivity tool-driven task change with minimal institutional counterweight.
[Source: Brookings 2024 — Generative AI, the American worker]These summaries were written by AI Changing Work from the source linked with each one, and any figures in them are given as AI Changing Work summarised them; they can differ from the source's own wording and from figures shown elsewhere on this page, so check the source before relying on them. AI Changing Work matched this page's occupation to an O*NET occupation and chose, by its own judgment, case by case, which summaries relate to that O*NET occupation; a summary appearing here does not mean that its source names this occupation.