Analistas de Tesoreria
Negocios y FinanzasExposición a la IA
- Fuente de datos: BLSPublicado: 2026-08
Muy alto· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: 2026-03
0.220
0.000Rango de los valores aquí recogidos0.745Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
- Fuente de datos: ILOPublicado: 2025
0.62
0.09Rango de los valores aquí recogidos0.70Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición a la IA generativa, 0–1 tal como se publica
Grupo primario de la CIUO-08 — todas las ocupaciones con ese código reciben este valor
Calculado por este sitio, no publicado por la OIT: de las 1012 ocupaciones que este sitio vincula al conjunto de datos de la OIT, un 3% alcanza o supera este valor.
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Task-level exposure
Solo tareas expuestas| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare requirements documentation for use by software developers.13-2099 | 0.080036.4 | 0.020028.6 |
Provide application or analytical support to researchers or traders on issues such as valuations or data.13-2099 | 0.080036.4 | 0.020028.6 |
Define or recommend model specifications or data collection methods.13-2099 | 0.030013.6 | 0.00000.0 |
Interpret results of financial analysis procedures.13-2099 | 0.01004.5 | 0.010014.3 |
Prepare written reports of investigation findings.13-2099 | 0.01004.5 | 0.00000.0 |
Recommend actions in fraud cases.13-2099 | 0.01004.5 | 0.00000.0 |
Evaluate business operations to identify risk areas for fraud.13-2099 | 0.00000.0 | 0.020028.6 |
Produce written summary reports of financial research results.13-2099 | 0.00000.0 | 0.00000.0 |
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 | 0.00000.0 | 0.00000.0 |
Gather financial documents related to investigations.13-2099 | 0.00000.0 | 0.00000.0 |
Advise businesses or agencies on ways to improve fraud detection.13-2099 | 0.00000.0 | —0 |
Research new financial products or analytics to determine their usefulness. | —0 | 0.00000.0 |
Create and maintain logs, records, or databases of information about fraudulent activity. | —0 | 0.00000.0 |
| Not observed on any surface — 31 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Collaborate in the development or testing of new analytical software to ensure compliance with user requirements, specifications, or scope. | —0 | —0 |
Maintain or modify all financial analytic models in use. | —0 | —0 |
Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques. | —0 | —0 |
Consult traders or other financial industry personnel to determine the need for new or improved analytical applications. | —0 | —0 |
Confer with other financial engineers or analysts on trading strategies, market dynamics, or trading system performance to inform development of quantitative techniques. | —0 | —0 |
Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models. | —0 | —0 |
Devise or apply independent models or tools to help verify results of analytical systems. | —0 | —0 |
Identify, track, or maintain metrics for trading system operations. | —0 | —0 |
Collaborate with product development teams to research, model, validate, or implement quantitative structured solutions for new or expanded markets. | —0 | —0 |
Analyze pricing or risks of carbon trading products. | —0 | —0 |
Assess the potential impact of climate change on business financial issues, such as damage repairs, insurance costs, or potential disruptions of daily activities. | —0 | —0 |
Develop methods of assessing or measuring corporate performance in terms of environmental, social, and governance (ESG) issues. | —0 | —0 |
Develop solutions to help clients hedge carbon exposure or risk. | —0 | —0 |
Develop tools to assess green technologies or green financial products, such as green hedge funds or social responsibility investment funds. | —0 | —0 |
Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques. | —0 | —0 |
Train others in fraud detection and prevention techniques. | —0 | —0 |
Research or evaluate new technologies for use in fraud detection systems. | —0 | —0 |
Prepare evidence for presentation in court. | —0 | —0 |
Negotiate with responsible parties to arrange for recovery of losses due to fraud. | —0 | —0 |
Conduct field surveillance to gather case-related information. | —0 | —0 |
Testify in court regarding investigation findings. | —0 | —0 |
Review reports of suspected fraud to determine need for further investigation. | —0 | —0 |
Lead, or participate in, fraud investigation teams. | —0 | —0 |
Interview witnesses or suspects and take statements. | —0 | —0 |
Design, implement, or maintain fraud detection tools or procedures. | —0 | —0 |
Document all investigative activities. | —0 | —0 |
Coordinate investigative efforts with law enforcement officers and attorneys. | —0 | —0 |
Conduct in-depth investigations of suspicious financial activity, such as suspected money-laundering efforts. | —0 | —0 |
Analyze financial data to detect irregularities in areas such as billing trends, financial relationships, and regulatory compliance procedures. | —0 | —0 |
Obtain and serve subpoenas. | —0 | —0 |
Arrest individuals to be charged with fraud. | —0 | —0 |
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 |
Obtain and serve subpoenas.O*NET Task ID 16039 | 0.0 |
Conduct field surveillance to gather case-related information.O*NET Task ID 16041 | 0.0 |
Arrest individuals to be charged with fraud.O*NET Task ID 16042 | 0.0 |
Testify in court regarding investigation findings.O*NET Task ID 16043 | 0.0 |
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
Información ocupacional
Cambios recientes relacionados con esta ocupación
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.
[Fuente: 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.