Betrugspruefer
SchutzdiensteKI-Exposition
- Datenquelle: BLSVeröffentlicht: 2026-08
Sehr hoch· relativ
NiedrigVier relative BänderSehr hochWert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Vier relative Bänder (Niedrig / Mittel / Hoch / Sehr hoch)
831 detaillierte Berufe in der BLS-Beschäftigungsprojektionstabelle. Der Wert wird je Code der National Employment Matrix (NEM) vergeben, daher erhalten Berufe mit demselben NEM-Code dasselbe Band
- Datenquelle: AnthropicVeröffentlicht: 2026-03
0.220
0.000Spannweite der hier geführten Werte0.745Skala, Bezugsmenge und Quelle
Index der beobachteten Exposition, 0–1 wie veröffentlicht
Auf O*NET-Aufgaben abgebildet
- Datenquelle: ILOVeröffentlicht: 2025
0.44
0.09Spannweite der hier geführten Werte0.70Wert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Index der Exposition gegenüber generativer KI, 0–1 wie veröffentlicht
ISCO-08-Berufsgattung — alle Berufe mit demselben Code erhalten diesen Wert
Von dieser Website berechnet, nicht von der IAO veröffentlicht: Von den 1.012 Berufen, die diese Website mit dem IAO-Datensatz verknüpft, erreichen oder übertreffen 28% diesen Wert.
Welche Art von Wert diese Quelle veröffentlicht
Die Kategorie von BLS ist ein relativer Rang und kein absolutes Niveau, und sie ist keine eigene Messung: Sie fasst die Perzentilränge des Berufs aus mehreren veröffentlichten Studien in vier Bänder zusammen. Sie ist weder eine Beschäftigungs- oder Lohnprognose noch eine Einführungswahrscheinlichkeit und unterscheidet nicht zwischen Automatisierung und Augmentierung.
Task-level exposure
Nur exponierte Aufgaben| 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