保険査定人
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
0.082
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
0.45
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25%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 18 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Analyze information gathered by investigation and report findings and recommendations.13-1031 | 0.004821.0 | 0.00466.9 |
Investigate, evaluate, and settle claims, applying technical knowledge and human relations skills to effect fair and prompt disposal of cases and to contribute to a reduced loss ratio.13-1031 | 0.003314.4 | 0.00253.7 |
Prepare reports to be submitted to company's data processing department.13-1031 | 0.002711.8 | 0.009313.9 |
Examine claims forms and other records to determine insurance coverage.13-1031 | 0.002410.5 | 0.029844.5 |
Verify and analyze data used in settling claims to ensure that claims are valid and that settlements are made according to company practices and procedures.13-1031 | 0.00219.2 | 0.00436.4 |
Interview or correspond with claimants, witnesses, police, physicians, or other relevant parties to determine claim settlement, denial, or review.13-1031 | 0.00219.2 | 0.00253.7 |
Examine titles to property to determine validity and act as company agent in transactions with property owners.13-1031 | 0.00208.7 | 0.00537.9 |
Obtain credit information from banks and other credit services.13-1031 | 0.00187.9 | 0.00203.0 |
Examine claims investigated by insurance adjusters, further investigating questionable claims to determine whether to authorize payments.13-1031 | 0.00177.4 | 0.00243.6 |
Investigate and assess damage to property and create or review property damage estimates. | —0 | 0.00263.9 |
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 |
|---|---|
Enter claim payments, reserves and new claims on computer system, inputting concise yet sufficient file documentation.O*NET Task ID 21429 | 1.0 |
Communicate with reinsurance brokers to obtain information necessary for processing claims.O*NET Task ID 21440 | 1.0 |
Prepare reports to be submitted to company's data processing department.O*NET Task ID 21441 | 1.0 |
Examine claims forms and other records to determine insurance coverage.O*NET Task ID 21417 | 0.5 |
Analyze information gathered by investigation and report findings and recommendations.O*NET Task ID 21418 | 0.5 |
Review police reports, medical treatment records, medical bills, or physical property damage to determine the extent of liability.O*NET Task ID 21419 | 0.5 |
Investigate and assess damage to property and create or review property damage estimates.O*NET Task ID 21420 | 0.5 |
Interview or correspond with agents and claimants to correct errors or omissions and to investigate questionable claims.O*NET Task ID 21421 | 0.5 |
Interview or correspond with claimants, witnesses, police, physicians, or other relevant parties to determine claim settlement, denial, or review.O*NET Task ID 21422 | 0.5 |
Investigate, evaluate, and settle claims, applying technical knowledge and human relations skills to effect fair and prompt disposal of cases and to contribute to a reduced loss ratio.O*NET Task ID 21423 | 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