- O*NET-SOC コード
- 15-2011.00
Analyze statistical data, such as mortality, accident, sickness, disability, and retirement rates and construct probability tables to forecast risk and liability for payment of future benefits. May ascertain insurance rates required and cash reserves necessary to ensure payment of future benefits.
- 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
0.054
0.000ここに掲載された値の範囲0.745尺度・母数・出典
観測エクスポージャー指数、公開されたまま0–1
O*NETタスクへの対応づけが基準
SOC 2018の職業単位で公表された値で、同じSOC 2018コードを持つO*NET職業はすべてこの値になります
- データ出典: ILO公表時点: 2025
0.56
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の細分類(4桁)単位で公表された値です。米国労働統計局(BLS)の公式対応表(ISCO-08→2010 SOC、2010 SOC→2018 SOC)を公表どおり適用してこの職業に結び付けており、対応は全部または一部です
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
露出のある作業のみ評価済みタスク 15 件 · β ≥ 0.5 のタスク 15 件(100.0%)
β = 直接的な露出(E1)+ 0.5 × ツール利用時の露出(E2)。原典リポジトリの定義に従います。
- 原典の単位:O*NET 27.2 のタスク → O*NET 31.0 の職業コード
- 評価済みタスクのうち 1 件は O*NET 31.0 のタスク一覧になく、O*NET 27.2 基準で公表されたスコアをそのまま残しています。
- 出典
- OpenAI "GPTs are GPTs" exposure rubric
- 版
- gh-main-0471612
- ライセンス
- MIT License, Copyright (c) 2024 OpenAI
| Task (O*NET 20.1) | Claude.aiRaw / share % |
|---|---|
Determine or help determine company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.15-2011.00 | 0.028444.0 |
Provide advice to clients on a contract basis, working as a consultant.15-2011.00 | 0.016926.3 |
Explain changes in contract provisions to customers.15-2011.00 | 0.007711.9 |
Determine policy contract provisions for each type of insurance.15-2011.00 | 0.00558.5 |
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.15-2011.00 | 0.00304.6 |
Design, review and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.15-2011.00 | 0.00304.6 |
| Not observed on any surface — 8 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. | |
Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits. | —0 |
Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business. | —0 |
Testify before public agencies on proposed legislation affecting businesses. | —0 |
Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person who is disabled or killed in an accident. | —0 |
Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information. | —0 |
Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings. | —0 |
Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies. | —0 |
Manage credit and help price corporate security offerings. | —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 (O*NET 27.2) | βE1 + 0.5 × E2 |
|---|---|
Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.O*NET Task ID 3500 | 0.5 |
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.O*NET Task ID 3501 | 0.5 |
Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.O*NET Task ID 3502 | 0.5 |
Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business.O*NET Task ID 3503 | 0.5 |
Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.O*NET Task ID 3504 | 0.5 |
Testify before public agencies on proposed legislation affecting businesses.O*NET Task ID 3505 | 0.5 |
Provide advice to clients on a contract basis, working as a consultant.O*NET Task ID 3506 | 0.5 |
Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person disabled or killed in an accident.O*NET Task ID 3507 | 0.5 |
Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information.O*NET Task ID 3508 | 0.5 |
Determine policy contract provisions for each type of insurance.O*NET Task ID 3509 | 0.5 |
Manage credit and help price corporate security offerings.O*NET Task ID 3510 | 0.5 |
Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings.O*NET Task ID 3511 | 0.5 |
Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies.O*NET Task ID 3512 | 0.5 |
Explain changes in contract provisions to customers.O*NET Task ID 3513 | 0.5 |
Negotiate terms and conditions of reinsurance with other companies.O*NET Task ID 20951 | 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
O*NET 31.0 のタスク全件(15件)
この職業の O*NET 31.0 のタスク全件(英語原文、種別はコア・補足)。露出値はこの一覧には載せず、上の表で各出典が用いた O*NET 版の文言の横にあります。
| タスク(O*NET 31.0) | 種類 |
|---|---|
| Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates. | コア |
| Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. | コア |
| Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business. | コア |
| Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public. | コア |
| Testify before public agencies on proposed legislation affecting businesses. | コア |
| Provide advice to clients on a contract basis, working as a consultant. | コア |
| Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information. | コア |
| Determine policy contract provisions for each type of insurance. | コア |
| Provide expertise to help financial institutions manage risks and maximize returns associated with investment products or credit offerings. | コア |
| Determine equitable basis for distributing surplus earnings under participating insurance and annuity contracts in mutual companies. | コア |
| Negotiate terms and conditions of reinsurance with other companies. | コア |
| Analyze data to determine premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits. | コア |
| Testify in court as expert witness or to provide legal evidence on matters such as the value of potential lifetime earnings of a person disabled or killed in an accident. | 補足 |
| Manage credit and help price corporate security offerings. | 補足 |
| Explain changes in contract provisions to customers. | 補足 |
0.00/ 3· ロボット露出指数
尺度・母数・出典
身体的なタスクそれぞれに、実際に導入・販売・実演されたロボットがこなせる環境に応じて 0–3 の等級を付け(0 不可 · 1 フェンスで囲われた機械セルや瓶詰めラインのようなロボット専用設備 · 2 病院の薬剤部やコンテナ港のような管理された施設 · 3 一般家庭や建設現場のような非定型の環境)、勤務時間に占める割合で平均した値
この職業の O*NET 29.3 タスクのうち身体的なタスクが対象
等級と時間の割合は Anthropic が Claude Opus 5(ウェブ検索)で推定しました(2026年時点)。
ロボット露出指数は、このサイトの AI 露出度とは別の指標であり、足し合わせたり比べたりすることはできません。実際の導入やコスト競争力を示すものではなく、0 には身体的なタスクのない職業も含まれます。
身体的なタスクごとの等級
この職業の O*NET 29.3 タスクのうち、身体的なタスクに分類されたものはありません。
職業情報
雇用見通し(BLS)
- 雇用者数、2025
- 31K
- 雇用者数見通し、2035
- 34K
- 変化率 (2025–35)
- +9.2%
- 年間賃金の中央値、2025
- $130,000
- 入職時に通常必要な学歴
- Bachelor's degree
韓国職業分類との対応(KSCO)
AI Changing Work が作成した Korea Occupation Direct Crosswalk v1.1 現行版の対応です。関係の値は O*NET-SOC 職業の側から読みます。
| KSCO コード | KSCO 名称 | 関係 | 状態 |
|---|---|---|---|
| 28231 | 보험 상품 개발자 | この職業のほうが狭い | 確定 |
関係と状態の定義(kr-direct v1.1)
ILO(国際労働機関)の職業説明
以下のISCO-08職業群は、AI Changing Workが米国労働統計局(BLS)の発行するクロスウォーク(2018 SOC→2010 SOC、2010 SOC→ISCO-08)を個別の選択なしにそのまま適用して、この職業に結び付けたものです。結び付きは全体的な対応の場合も部分的な対応の場合もあります。
- ISCO-08 2120Mathematicians, Actuaries and Statisticians
数学者、保険計理士、統計の専門職
出典: 総務省「国際標準職業分類(ISCO)2008年改定版(仮訳)分類項目名一覧(和英対照)」— 原著:ILO ISCO-08 © International Labour Organization(日本語訳は総務省がILOの許可を得て作成)
以下の定義・作業・職業例は、この職業ではなく、当該ISCO-08職業群についてのILOによる説明です。
Mathematicians, actuaries and statisticians conduct research and improve or develop mathematical, actuarial and statistical concepts, theories and operational methods and techniques and advise on or engage in their practical application in such fields as engineering, business and social and other sciences.
作業
- (a) studying, improving and developing mathematical, actuarial and statistical theories and techniques;
- (b) advising on or applying mathematical principles, models and techniques to a wide range of tasks in the fields of engineering, natural, social or life sciences;
- (c) conducting logical analyses of management problems, especially in terms of input-output effectiveness, and formulating mathematical models of each problem usually for programmeming and solution by computer;
- (c) designing and putting into operation pension schemes and life, health, social and other types of insurance systems;
- (e) applying mathematics, statistics, probability and risk theory to assess potential financial impacts of future events;
- (f) planning and organizing surveys and other statistical collections, and designing questionnaires;
- (g) evaluating, processing, analysing, and interpreting statistical data and preparing them for publication;
- (h) advising on or applying various data collection methods and statistical methods and techniques, and determining reliability of findings, especially in such fields as business or medicine as well as in other areas of natural, social or life sciences;
- (i) preparing scientific papers and reports;
- (j) supervising the work of mathematical, actuarial and statistical assistants and statistical clerks.
職業例
Actuary · Demographer · Mathematician · Operations research analyst · Statistician
Sources: ILO ISCO-08 — details: Credits
ESCO 職業語彙
以下の各項目は、公開されているESCO-O*NET Crosswalk(バージョン1、2022年)で、この職業と一つずつ対応付けられたものです。対応の種類はそのクロスウォークによるもので、表現はAI Changing Workによるものです。
- actuarial consultantESCO 2120.1範囲が同じ
Actuarial consultants analyse, manage and provide guidance on financial impact of risks. They can work in fields related to insurance, pension, investment, banking, healthcare etc. Actuarial consultants apply technical and statistical models and theories to give strategic, commercial, and financial advice.
- actuarial assistantESCO 3314.1この職業のほうが広い
Actuarial assistants perform statistical data research in order to set premium rates and insurance policies. They review the possibility of accidents, injuries and property damage by using statistical formulas and models.
- insurance rating analystESCO 2413.1.2この職業のほうが広い
Insurance rating analysts analyse information related to insurance markets and their credit rating, prepare rating reports and invoices, compile financial data and present and explain credit rating opinions to stakeholders, clients and external parties. They work for insurance companies and calculate the insurance premium and rates for the company’s clients using both manual and automated methods.
- insurance risk consultantESCO 3321.2この職業のほうが広い
Insurance risk consultants prepare reports for insurance underwriters. For this purpose, they undertake surveys in order to determine the potential financial risk for personal products, properties or sites.
出典: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
ライセンス: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
フランス職業分類との対応(ROME)
この職業と ROME 4.0 の職業票(fiche)との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。「暫定」と表示した対応は、同じモデル系列(Claude)の二人の判定者による判定のみ、またはその判定に運営者の規則を適用して採用したものであり、確定した対応より根拠が弱くなります。
| ROME コード | ROME 名称(フランス語) | 関係 |
|---|---|---|
| C1105 | Actuaire en assurances | この職業のほうが広い暫定 |
| C1120 | Conseiller / Conseillère en études actuarielles | この職業のほうが広い暫定 |
| C1106 | Expert / Experte risques en assurances | 関連(対応ではない)暫定 |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
スペイン職業分類との対応(CNO-11)
この職業と CNO-11 の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。
| CNO-11 コード | CNO-11 名称(スペイン語) | 関係 |
|---|---|---|
| 2415 | Matemáticos y actuarios | この職業のほうが狭い |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
ドイツ職業分類との対応(KldB)
この職業と KldB の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。
| KldB コード | KldB 名称(ドイツ語) | 関係 |
|---|---|---|
| 41184 | Berufe in der Mathematik (sonstige spezifische Tätigkeitsangabe) - hoch komplexe Tätigkeiten | この職業のほうが狭い |
| 41114 | Berufe in der Statistik - hoch komplexe Tätigkeiten | 関連(対応ではない) |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
日本職業分類との対応(JSCO)
この職業と JSCO の職業との対応で、AI Changing Work が AI モデルを用いて判定したものです。関係はこの職業の側から読みます。
| JSCO コード | JSCO 名称(日本語) | 関係 |
|---|---|---|
| 184 | 金融・保険専門職業従事者 | この職業のほうが狭い |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
このページのデータの出典・ライセンスと AI Changing Work による改変・翻訳の告知:クレジットと出典