ट्रेजरी विश्लेषक
व्यापार और वित्तAI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: 2026-08
बहुत उच्च· सापेक्ष
कमचार सापेक्ष श्रेणियाँबहुत उच्चव्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
चार सापेक्ष बैंड (कम / मध्यम / उच्च / बहुत उच्च)
BLS रोज़गार पूर्वानुमान तालिका के 831 विस्तृत व्यवसायों के आधार पर। मान NEM (नेशनल एम्प्लॉयमेंट मैट्रिक्स) कोड के स्तर पर दिया जाता है, इसलिए एक ही NEM कोड वाले व्यवसायों को वही बैंड मिलता है
- डेटा स्रोत: Anthropicप्रकाशन: 2026-03
0.220
0.000यहाँ दिए गए मानों की सीमा0.745मापक्रम, आधार और स्रोत
- डेटा स्रोत: ILOप्रकाशन: 2025
0.62
0.09यहाँ दिए गए मानों की सीमा0.70व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
जेनरेटिव AI एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
ISCO-08 यूनिट समूह — समान कोड वाले सभी व्यवसायों को यही मान मिलता है
यह इस साइट की गणना है, ILO द्वारा प्रकाशित आँकड़ा नहीं: इस साइट द्वारा ILO डेटासेट से जोड़े गए 1,012 व्यवसायों में से 3% इस मान के बराबर या उससे अधिक हैं।
यह स्रोत किस प्रकार का आँकड़ा प्रकाशित करता है
BLS की श्रेणी सापेक्ष रैंक है, निरपेक्ष स्तर नहीं, और यह प्रथम-हस्त माप भी नहीं है: यह कई प्रकाशित अध्ययनों में व्यवसाय की पर्सेंटाइल रैंकों को चार बैंडों में बांटती है। यह रोज़गार या वेतन का पूर्वानुमान नहीं है, न अपनाए जाने की संभावना, और यह स्वचालन और संवर्धन में अंतर नहीं करती।
Task-level exposure
केवल एक्सपोज़र वाले कार्य| 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
व्यावसायिक जानकारी
इस व्यवसाय से जुड़े हालिया बदलाव
मार्च 2026: एवरग्रीन ब्लॉग विश्लेषण प्रकाशित: 2025 में AI एक्सपोजर 55%, ऑटोमेशन रिस्क 42/100।
[स्रोत: AI Changing Work Blog]