Escrow 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
0.220
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.44
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, 28% 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 53 hidden tasks| Task | Claude.aiRaw / share % |
|---|---|
Interpret results of financial analysis procedures.13-2099 | 0.101018.4 |
Analyze financial or operational performance of companies facing financial difficulties to identify or recommend remedies.13-2099 | 0.064911.8 |
Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.13-2099 | 0.05349.7 |
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 | 0.04999.1 |
Produce written summary reports of financial research results.13-2099 | 0.03857.0 |
Structure or negotiate deals, such as corporate mergers, sales, or acquisitions.13-2099 | 0.02955.4 |
Develop or implement risk-assessment models or methodologies.13-2099 | 0.02795.1 |
Employ financial models to develop solutions to financial problems or to assess the financial or capital impact of transactions.13-2099 | 0.02364.3 |
Provide application or analytical support to researchers or traders on issues such as valuations or data.13-2099 | 0.01693.1 |
Identify, track, or maintain metrics for trading system operations.13-2099 | 0.01292.4 |
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