金融リスクアナリスト
ビジネス・金融AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分(低い / 中程度 / 高い / 非常に高い)
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.265
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.44
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは28%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 20 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Recommend ways to control or reduce risk.13-2054 | 0.110025.0 | 0.02003.2 |
Recommend investments and investment timing to companies, investment firm staff, or the public.13-2054 | 0.070015.9 | 0.080012.9 |
Prepare plans of action for investment, using financial analyses.13-2054 | 0.070015.9 | 0.01001.6 |
Identify key risks and mitigating factors of potential investments, such as asset types and values, legal and ownership structures, professional reputations, customer bases, or industry segments.13-2054 | 0.060013.6 | 0.130021.0 |
Analyze areas of potential risk to the assets, earning capacity, or success of organizations.13-2054 | 0.04009.1 | 0.100016.1 |
Interpret data on price, yield, stability, future investment-risk trends, economic influences, and other factors affecting investment programs.13-2054 | 0.03006.8 | 0.210033.9 |
Monitor developments in the fields of industrial technology, business, finance, and economic theory.13-2054 | 0.03006.8 | 0.03004.8 |
Produce reports or presentations that outline findings, explain risk positions, or recommend changes.13-2054 | 0.01002.3 | 0.03004.8 |
Devise scenario analyses reflecting possible severe market events.13-2054 | 0.01002.3 | 0.01001.6 |
Provide statistical modeling advice to other departments.13-2054 | 0.01002.3 | —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 |
|---|---|
Draw charts and graphs, using computer spreadsheets, to illustrate technical reports.O*NET Task ID 21617 | 1.0 |
Review or draft risk disclosures for offer documents.O*NET Task ID 21633 | 1.0 |
Analyze areas of potential risk to the assets, earning capacity, or success of organizations.O*NET Task ID 21605 | 0.5 |
Analyze new legislation to determine impact on risk exposure.O*NET Task ID 21606 | 0.5 |
Conduct statistical analyses to quantify risk, using statistical analysis software or econometric models.O*NET Task ID 21607 | 0.5 |
Confer with traders to identify and communicate risks associated with specific trading strategies or positions.O*NET Task ID 21608 | 0.5 |
Consult financial literature to ensure use of the latest models or statistical techniques.O*NET Task ID 21609 | 0.5 |
Contribute to development of risk management systems.O*NET Task ID 21610 | 0.5 |
Determine potential environmental impacts of new products or processes on long-term growth and profitability.O*NET Task ID 21611 | 0.5 |
Develop contingency plans to deal with emergencies.O*NET Task ID 21612 | 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