محللو الائتمان
الأعمال والماليةالتعرّض للذكاء الاصطناعي
- مصدر البيانات: BLSتاريخ النشر: 2026-08
مرتفع جدًا· نسبي
منخفضأربع فئات نسبيةمرتفع جدًاقيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
أربع فئات نسبية (منخفض / متوسط / مرتفع / مرتفع جدًا)
831 مهنة تفصيلية في جدول توقعات التوظيف لدى BLS. وتُسند القيمة على مستوى رمز مصفوفة التوظيف الوطنية (NEM)، فالمهن التي تشترك في الرمز نفسه تأخذ النطاق نفسه
- مصدر البيانات: Anthropicتاريخ النشر: 2026-03
0.169
0.000نطاق القيم المعروضة هنا0.745المقياس والأساس والمصدر
- مصدر البيانات: ILOتاريخ النشر: 2025
0.62
0.09نطاق القيم المعروضة هنا0.70قيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
مؤشر التعرّض للذكاء الاصطناعي التوليدي، 0–1 كما نُشر
وحدة التصنيف ISCO-08 — تأخذ كل المهن ذات الرمز نفسه هذه القيمة
محسوب من قِبل هذا الموقع وليس منشورًا من منظمة العمل الدولية: من بين 1,012 مهنة يربطها هذا الموقع بمجموعة بيانات المنظمة، تبلغ نسبة التي تساوي هذه القيمة أو تتجاوزها 3%.
ما نوع القيمة التي ينشرها هذا المصدر
فئة BLS رتبة نسبية لا مستوى مطلق، وليست قياسًا مباشرًا: فهي تجمع الرتب المئينية للمهنة في عدة دراسات منشورة في أربع فئات. وهي ليست توقّعًا للتوظيف أو الأجور، ولا احتمالًا للتبني، ولا تفرّق بين الأتمتة والتعزيز.
Task-level exposure
المهام ذات التعرض فقط| Task | Claude.aiRaw / share % |
|---|---|
Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.13-2041 | 0.011251.8 |
Analyze financial data such as income growth, quality of management, and market share to determine expected profitability of loans.13-2041 | 0.005022.9 |
Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.13-2041 | 0.003415.7 |
Generate financial ratios, using computer programs, to evaluate customers' financial status.13-2041 | 0.00219.6 |
| Not observed on any surface — 6 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. | |
Prepare reports that include the degree of risk involved in extending credit or lending money. | —0 |
Confer with credit association and other business representatives to exchange credit information. | —0 |
Review individual or commercial customer files to identify and select delinquent accounts for collection. | —0 |
Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations. | —0 |
Consult with customers to resolve complaints and verify financial and credit transactions. | —0 |
Evaluate customer records and recommend payment plans based on earnings, savings data, payment history, and purchase activity. | —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
معلومات مهنية
تغيّرات حديثة تخصّ هذه المهنة
مارس 2026: New Agentic Task Exposure (ATE) framework scores credit analysts among the highest-risk occupations (ATE 0.43-0.47) for agentic AI workflow displacement by 2030.
[المصدر: 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.