क्रेडिट 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
0.169
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 % |
|---|---|---|
Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.13-2041 | 0.0100100.0 | 0.00000.0 |
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.13-2041 | 0.00000.0 | 0.0100100.0 |
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.13-2041 | 0.00000.0 | 0.00000.0 |
Prepare reports that include the degree of risk involved in extending credit or lending money.13-2041 | 0.00000.0 | —0 |
| Not observed on any surface — 7 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. | ||
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval. | —0 | —0 |
Generate financial ratios, using computer programs, to evaluate customers' financial status. | —0 | —0 |
Consult with customers to resolve complaints and verify financial and credit transactions. | —0 | —0 |
Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans. | —0 | —0 |
Confer with credit association and other business representatives to exchange credit information. | —0 | —0 |
Review individual or commercial customer files to identify and select delinquent accounts for collection. | —0 | —0 |
Contact customers to collect payments on delinquent accounts. | —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 |
|---|---|
Generate financial ratios, using computer programs, to evaluate customers' financial status.O*NET Task ID 1255 | 1.0 |
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.O*NET Task ID 1250 | 0.5 |
Prepare reports that include the degree of risk involved in extending credit or lending money.O*NET Task ID 1251 | 0.5 |
Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.O*NET Task ID 1252 | 0.5 |
Confer with credit association and other business representatives to exchange credit information.O*NET Task ID 1253 | 0.5 |
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.O*NET Task ID 1254 | 0.5 |
Review individual or commercial customer files to identify and select delinquent accounts for collection.O*NET Task ID 1256 | 0.5 |
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.O*NET Task ID 1257 | 0.5 |
Consult with customers to resolve complaints and verify financial and credit transactions.O*NET Task ID 1258 | 0.5 |
Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.O*NET Task ID 1259 | 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
व्यावसायिक जानकारी
इस व्यवसाय से जुड़े हालिया बदलाव
मार्च 2026: नए एजेंटिक टास्क एक्सपोज़र (ATE) फ्रेमवर्क ने क्रेडिट एनालिस्ट को 2030 तक एजेंटिक AI वर्कफ़्लो विस्थापन के लिए सबसे अधिक जोखिम वाले व्यवसायों (ATE 0.43-0.47) में रखा है।
[स्रोत: Gupta & Kumar (2026) Agentic AI and Occupational Displacement]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.