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SOC 15-2011 · O*NET 15-2011.00
Actuaries use mathematics, statistics and financial theory to estimate risk and financial liabilities. Explore their daily work, US education and examination routes, pay data and a fictional work sample before comparing this career with adjacent roles.
Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((75+75+50)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.
Use the work sample below to check whether you want to spend more time validating assumptions and explaining results before committing to an examination pathway. Your response to one exercise is an exploration clue, not proof of career suitability.
Try a 60-minute experiment with entirely fictional policy and claim-payment records: write a data dictionary, calculate claim frequency and average loss, and compare loss costs under two sets of assumptions. Produce a calculation sheet, assumption notes and an anomaly list. This is an introductory work sample, not professional actuarial training or a real pricing recommendation.
This career is worth exploring if you enjoy investigating data anomalies, testing assumptions and explaining uncertainty. Strong mathematics is useful, but the work also requires checking calculations and discussing their business consequences.
Actuaries use statistical, financial, and risk models to analyze uncertain future losses and support insurance pricing, reserves, and related risk decisions. Model conclusions must explain data, assumptions, and applicability; actuarial analysis does not automatically confer underwriting, investment, or legal approval authority.
Exact US occupational data (as of 2026-09-04): Actuaries / O*NET 15-2011.00. Actuaries analyze experience data such as mortality and accident rates, build probability models to estimate future benefit-payment risks and liabilities, and may calculate insurance premium rates and required reserves.
Career direction comparison
Insurance underwriters focus on acceptance and terms of specific applications; actuaries provide rate foundations, experience assumptions, and portfolio-level risk measures, operating at different decision granularities. Bioinformatics technicians handle genomic or life-science data; actuaries handle insurance and population risk data, so using statistical software does not make their responsibility boundaries interchangeable.
| Career direction | Core work and output | Key differences | Best-fit choice |
|---|---|---|---|
| Financial Risk Specialists | Measure market and credit risk exposures and recommend risk controls. Occupational responsibilities follow US O*NET descriptions; specific roles still require checking local requirements. | Actuaries input mortality and accident rates into probability models to measure risk liabilities; financial risk specialists mainly assess market, credit, or liquidity risks and produce analyses of risk exposures and limits. | First check whether you prefer studying long-term insurance liabilities or market and credit risks. These work-content comparisons do not guarantee direct career transfers or mutual recognition of qualifications. |
AI impact on this career
Insufficient evidence: this round obtained no actuarial task-level adoption rates, time-saving effects, or causal layoff data. Editorial suggestions include AI assistance with data organization, draft code, and initial reports; assumption selection, model validation, and approval responsibilities must still follow organizational authority. AI Impact Score 67% is an editorial estimate. Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((75+75+50)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.The three tasks, ratings and sources are listed in the FermatMind method source record on this page.
FermatMind AI Impact Score estimation method v1
FermatMind / 费马测试 · 2026-10-18
Chinese mainland salary reference
US Actuaries SOC 15-2011 corresponds to a US statistical population; it does not establish mutual recognition of Chinese occupational qualifications.
Chinese proficiency-assessment rules distinguish associate actuaries and fully qualified actuaries; occupational names, qualifications, employer roles, and US SOC classifications are not equivalent.
Insufficient evidence: this round obtained no comparable monthly wage sample with matching scope.
Insufficient evidence: this round obtained no comparable monthly wage sample with matching scope.
Insufficient evidence: this round obtained no comparable monthly wage sample with matching scope.
US salary reference
Exact US occupational data: the median annual wage for actuaries in month 5 of 2025 is 130,000 USD. This is an OEWS annual wage median, not starting pay, a total remuneration promise, or Chinese wages.
| Position in the wage distribution | Annual wage reference | Conversion boundaries | How to understand this position |
|---|---|---|---|
| Percentile 10 | 78,570 US dollars/year | National annual wage statistics; not converted into Chinese wages or individual take-home income. | Statistical positions are not entry-level, senior, or expert job grades. |
| Median | 130,000 US dollars/year | National annual wage statistics; not converted into Chinese wages or individual take-home income. | Statistical positions are not entry-level, senior, or expert job grades. |
Career fit guide
First observe willingness to check data, repeat calculations, explain assumptions, and accept independent review. Use this page's fictional portfolio to deliver a dictionary, calculation sheet, and one-page limitation notes, then record which tasks engage you and which are difficult; assessment results may support reflection but cannot replace feedback on work samples.
You may be better suited to this work： Willingness to investigate differences in measures, retain versions, accept counterexamples, and explain assumptions clearly provides signals this page suggests observing; they are not recruitment standards or success probabilities.
These situations require careful consideration： If recalculation, records, and questioning remain unwelcome, use small tasks to locate specific difficulties before comparing other occupations; temporary difficulty does not mean abilities cannot be learned.
Try an entry-level practice exercise： Follow the pathway exercise using fictional A/B data to calculate frequency, severity, loss costs, and two separate 10% scenarios, producing a recalculation sheet and limitation notes; do not use real customer details.
Work pressures, risks, and occupational boundaries
Focus on experience data measures, assumption applicability, model changes, sensitive data, and report limitations. Analysis may support pricing and reserves, while formal approval, signing, and access permissions still follow organizational governance.
O*NET and BLS support task and work-environment background, and NIST provides a cross-industry AI risk framework; they provide neither Chinese actuarial accident rates nor this organization's approval rules. The following control suggestions cannot be presented as regulations or actual risk effects.
Typical scenario：Combining samples with different observation periods, products, or claim definitions without recording adjustments.
Roles most affected：Actuarial analysts, data providers, and reviewers
Possible consequence：Pricing or reserve results become incomparable and difficult to reconstruct during review.
Entry, Qualifications, and Career Development
First establish foundations in mathematics, statistics, and reproducible analysis, test interests through fictional work samples, and check education, examination, and role requirements for the target market; after entering a role, accumulate data, modeling, and review experience within authorized limits.
Career outlook and transitions to related occupations
US actuarial employment was approximately 31,200 people in 2025; employment is projected to grow by 9% over 2025—2035, with approximately 1,500 average annual openings over the same period. In 2025, about 78% of actuarial employment was in finance and insurance. Employment stock, growth rate, average annual openings, and industry share are different indicators and cannot represent the Chinese market or an individual employment guarantee.
United States · Actuaries (SOC 15-2011)
Approximately 31,200 people
Employment count
Employment stock, not recruitment count for that year.
Does not establish：US national occupational statistics cannot represent the Chinese market.
Typical work starts with organizing experience data and business conditions, proceeds through assumption selection, calculating loss or benefit-payment distributions, and testing model results, then produces pricing, reserve, or risk recommendations and explains limitations to management and relevant professionals.
Actuaries focus on long-term uncertainty, insurance liabilities, and funding adequacy; financial risk specialists focus more on market and investment risks, statisticians on general statistical inference, insurance underwriters on acceptance and terms of individual business, and bioinformatics technicians on life-science data processing.
Choose a real task from this page for a small practice project or a conversation with someone in the field. Use the planning guides to set a date, an output and feedback questions before deciding whether to continue.
If you dislike ongoing recalculation, version records, and cross-department questioning, or want only to model without explaining business consequences, first compare statisticians or more purely data-focused roles; consulting actuarial work may also add client meetings and travel.
Observe how you handle a dataset with inconsistent definitions: first clarify the measures and observation period, then check inputs and assumptions when results look unusual. Explain your conclusion, its scope and its limitations to someone without a technical background.
Estimate future risks and benefit-payment liabilities from experience data to support judgments on rates, reserves, or capital.
Analysis and collaboration run through model development, validation, and business explanation.
Actuaries assess risks such as rates and future benefit-payment liabilities using population experience and probability models; insurance underwriters mainly review specific insurance applications to decide acceptance and applicable conditions. They collaborate, but model recommendations do not equal approval of individual underwriting cases. The table compares US occupational tasks; actual authorization still depends on local rules and employer policies.
| Comparison dimension | Actuaries (US occupational tasks) | Insurance underwriters (US occupational tasks) |
|---|---|---|
| Core purpose | Estimate future benefit-payment risks and liabilities to support analysis of pricing, reserves, and capital scenarios. | Assess risks in specific insurance applications and decide acceptance and applicable conditions. |
| Main objects and supporting basis | Policy and claims experience, exposure, mortality or accident statistics, assumptions, and insurance plans. | Insurance applications, health or property conditions, insured risks, and company underwriting rules. |
| Core process | Check experience data and measures, select assumptions, build probability or predictive models, and test results. | Review applications and supporting materials, obtain additional risk information, and decide acceptance, rejection, or adjusted conditions. |
| Main outputs | Rate or reserve calculations, risk scenarios, and explanations of their assumptions and limitations. | Underwriting decisions, conditions, or requests for additional materials for specific applications. |
| Responsible party | Actuaries analyze and explain models, collaborating with underwriting, claims, finance, programming staff, and management. | Underwriters review application risks and obtain information from agents, medical staff, and others. |
| Authority and approval | Actuarial model recommendations cannot automatically become approvals of individual underwriting, investment, or other business matters; check role authorization separately. | Occupational tasks include rejecting excessive risks and specific underwriting operations; organizations and local rules define approval limits. |
| Boundaries of conclusions | Population risk models cannot alone determine whether a specific application is accepted or guarantee future losses or individual benefits. | Individual application assessments cannot replace complete actuarial assessments of long-term population liabilities, rates, or capital. |
Confirm statistical definitions, observation periods, and applicability, retaining input-check records.
Analyze relevant statistical data and explain sample and assumption limitations.
Produce calculations and explain methods, assumptions, and the basis for recalculation.
Support financial analysis without treating analysis as approval.
Explain technical results and limitations clearly, checking business questions against outputs.
Keep recalculation and change records; authorized personnel make specific approvals.
First confirm the specific business and role, mathematical and data ability requirements, examination or qualification requirements, review and signing authority, work pace, and data confidentiality conditions item by item; use reproducible exercises to test abilities, without treating exam progress as full role authorization.
Confirm life insurance, property and casualty insurance, pensions, or other business and pricing, reserve, or model support responsibilities with the employer, without inferring authority from an “actuarial” job title alone.
Check probability and statistics, financial fundamentals, and spreadsheet/query/programming abilities against recruitment requirements; demonstrate data measures and recalculation using loss-cost and sensitivity exercises with the same observation period.
Check specific examination, education, and qualification requirements for the target region and employer; verify Chinese proficiency-assessment qualifications separately from US certification pathways and confirm whether the role requires particular progress or credentials.
| Statisticians | Use statistical theories and methods to collect, organize, and interpret data and provide information for different research fields. Occupational responsibilities follow US O*NET descriptions; specific roles still require checking local requirements. | Statisticians design statistical inference for a broader range of questions; actuaries also integrate models into pricing, reserves, and insurance risk management and explain business responsibilities. | First check whether you prefer studying general statistical inference or assumptions and liabilities in insurance business. These work-content comparisons do not guarantee direct career transfers or mutual recognition of qualifications. |
|---|
| Insurance Underwriters | Review risks in individual insurance applications and decide whether to accept them. Occupational responsibilities follow US O*NET descriptions; specific roles still require checking local requirements. | Insurance underwriters focus on acceptance and terms of specific applications; actuaries provide rate foundations, experience assumptions, and portfolio-level risk measures, operating at different decision granularities. | First check whether you prefer handling individual business judgments or portfolio-level pricing and reserves. These work-content comparisons do not guarantee direct career transfers or mutual recognition of qualifications. |
|---|
| Bioinformatics Technicians | Use bioinformatics tools to analyze molecular data and support biological research and database maintenance. Occupational responsibilities follow US O*NET descriptions; specific roles still require checking local requirements. | Bioinformatics technicians handle genomic or life-science data; actuaries handle insurance and population risk data, so using statistical software does not make their responsibility boundaries interchangeable. | First check whether you prefer handling life-science data or insurance and population risk data. These work-content comparisons do not guarantee direct career transfers or mutual recognition of qualifications. |
|---|
Actuaries input mortality and accident rates into probability models to measure risk liabilities; financial risk specialists mainly assess market, credit, or liquidity risks and produce analyses of risk exposures and limits.
Bioinformatics technicians handle genomic or life-science data; actuaries handle insurance and population risk data, so using statistical software does not make their responsibility boundaries interchangeable.
Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
Task exposure assesses whether technology could handle some tasks; it is not a current adoption or job replacement rate.
Adoption is also constrained by data permissions, system integration, validation, and governance; this page has no sample of actuarial employer adoption.
Employment changes also depend on insurance demand, business scale, and personnel movement and cannot be estimated from task exposure alone.
| Work area | Task | Current change | Human control point |
|---|---|---|---|
| Experience analysis | Organize exposure and claims experience | Editorial suggestion: assist with drafting data checks; no task-level effect data obtained. | Check sample scope, observation periods, and missing data; do not upload details to unauthorized tools. |
| Pricing | Compare rate assumptions and scenarios | Editorial suggestion: organize alternative assumptions and sensitivity-test outlines. | Authorized personnel handle assumption selection, applicability, and business approval. |
| Reserve assessment | Check development triangles and estimate differences | Editorial suggestion: draft explanations of differences, checking each against models and original data. | Confirm measures, methods, and major anomalies; do not treat fluent explanations as audit evidence. |
| Model development | Write calculation and test code | Editorial suggestion: draft code and test cases; no promise of time savings. | Use only after fixing the environment, recalculating known samples, and independently reviewing changes. |
| Risk assessment | Organize stress scenarios and results | Editorial suggestion: compile result tables for approved scenarios. | Check model failure conditions and tail risks; cannot independently approve risk limits. |
| Report communication | Explain conclusions and limitations | Editorial suggestion: draft business-facing summaries. | Reports must correspond to model outputs, assumptions, and limitations and be reviewed by responsible personnel. |
| Governance and review | Record versions, approvals, and reviews | Editorial suggestion: organize review checklists and version differences. | Retain the responsibility chain, access authorization, and independent validation; cannot replace signing or approval. |
Broad-scope research explains task exposure, employer expectations, and risk management methods, without giving an actuarial disappearance year or replacement proportion.
The following are editorial suggestions based on actuarial tasks, not observed industry adoption effects.
AI primarily changesEditorial suggestion: draft data checks and code without adding unverified inputs.
Human responsibility remainsCheck data definitions, periods, and calculations.
AI primarily changesEditorial suggestion: organize existing materials and version differences.
Human responsibility remainsEnsure authorization, records, and traceability for sensitive materials.
AI primarily changesEditorial suggestion: organize alternative assumptions and sensitivity results.
Human responsibility remainsPeople remain responsible for assumption selection, applicability judgments, and review.
Retain human review and the responsibility chain from original data to formal conclusions; tool-generated materials are inputs awaiting validation.
Confirm the business question, data measures, and permitted scope of tool use.
Tools process only authorized materials; generated content remains unverified.
Check results using original data, independent calculations, and known samples.
Retain data, model, prompt, and review versions and reasons for changes.
Analysis, review, signing, and approval each follow actual role authority.
Generated content may contain fabrications or omissions.
Control: Return to original data and calculations; do not use conclusions that cannot be verified.
Policy and claims details may involve sensitive materials.
Control: Select data and tools according to organizational authorization; do not upload details to unauthorized services.
Missing version records make results difficult to reproduce.
Control: Record model, data, and tool versions and change reviews.
Incorrect periods or units alter risk estimates.
Control: Check time, exposure, counts, and amounts separately and recalculate independently.
Tool outputs can blur analysis and approval roles.
Control: Identify authors, reviewers, and approvers, escalating matters beyond authority.
Practice data dictionaries, baseline calculations, and limitation notes using fictional portfolios.
Review data and formulas first, then assess whether tool drafts can be recalculated.
Check assumption applicability, exceptional scenarios, and model changes.
Define access authorization, independent validation, and approval responsibilities; do not replace the responsibility chain with tool outputs.
This page's evidence is insufficient to predict replacement of actuarial jobs overall; validate tools and human responsibilities for specific tasks.
Source: ILO exposure research and editorial notesOriginal source ↗
BLS describes the use of statistical and modeling tools; coding ability helps with recalculation but is not a uniform recruitment requirement for all roles.
Source: BLS actuaries occupational handbookOriginal source ↗
Editorial suggestions prioritize data checks, draft code, and summaries; this page has no actuarial task-level exposure ranking data.
Source: Occupational tasks and editorial inferenceOriginal source ↗
BLS projects 9% US employment growth over 2025—2035; this is a national projection and does not establish AI employment causation.
Source: BLS employment projectionsOriginal source ↗
Generated text grants no signing, rate approval, or payment authority; follow actual organizational authorization.
Source: Editorial responsibility boundariesOriginal source ↗
Insufficient evidence: this round has no verifiable year in which actuarial jobs will disappear; task exposure is not a timing projection.
Source: Research boundariesOriginal source ↗
Practice data measures, reproducible models, assumption explanations, and independent review; develop specific abilities according to the target role.
Source: Occupational tasks and editorial suggestionsOriginal source ↗
Practice reproducible analysis, assumption explanations, and review first, then compare whether tools help specific tasks; do not treat tool proficiency as a professional practice qualification.
Insufficient evidence: this round obtained no traceable, deduplicated Chinese actuarial recruitment remuneration sample.
| Educational background | Common role directions | Explanations and checking suggestions |
|---|---|---|
| Foundational mathematics/statistics learning stage | Explore data organization and recalculation | Complete a fictional exercise first and check target-course requirements; no promise of correspondence between education and wages. |
| Foundational finance/insurance learning stage | Explore pricing or reserve tasks | Develop data measures and probability fundamentals; do not infer uniform requirements across all employers. |
| Foundational programming/data learning stage | Explore modeling and analysis processes | Tool abilities must be combined with actuarial subject matter, validation, and business explanations. |
| Existing experience in other work | Develop learning and work samples around transferable tasks | Check target qualifications, job requirements, and costs against specific announcements; no guarantee of a career transfer. |
Without cities, query periods, experience levels, and deduplication rules, general entry requirements or recruitment frequency cannot be reported.
Official occupational materials include pricing, reserves, and insurance/annuity/pension plans; this page does not quantify Chinese demand.
Risk-modeling abilities may overlap; target financial products and authority require separate learning.
May involve explaining analysis to clients; no comparable sample of Chinese recruitment volumes or offers obtained in this round.
AI-related task changes may affect work content, but this page's evidence is insufficient to quantify or attribute changes in Chinese actuarial wages; broad-scope exposure research is not a wage outcome.
| Percentile 90 | 215,100 US dollars/year | National annual wage statistics; not converted into Chinese wages or individual take-home income. | Statistical positions are not entry-level, senior, or expert job grades. |
|---|
Pay · percentile 10：78,570 USD/year (2025)；2025 annual wage 10th percentile (not starting pay)；BLS wages and projections kept separate
Pay · percentile 25：97,680 USD/year (2025)；2025 annual wage 25th percentile (not starting pay)；BLS wages and projections kept separate
Pay · median：130,000 USD/year (2025)；2025 annual wage 50th percentile (not starting pay)；BLS wages and projections kept separate
Pay · percentile 75：170,650 USD/year (2025)；2025 annual wage 75th percentile (not starting pay)；BLS wages and projections kept separate
Pay · percentile 90：215,100 USD/year (2025)；2025 annual wage 90th percentile (not starting pay)；BLS wages and projections kept separate
No. The median is the midpoint of the national distribution of employed workers and cannot serve as an offer for new entrants.
Cannot directly estimate an individual's annual income; check working hours, employment duration, overtime, and benefits separately.
This page's five wage percentile figures are 2025 OEWS wages, while future employment uses 2025—2035 projections; the two programs are separate.
Locations, business directions, experience, employers, and remuneration composition differ; compare actual contracts and responsibilities.
| Industry | Median wage for this occupation within the industry | How to understand this value |
|---|---|---|
| Finance and insurance | 131,450 USD/year (2025) | Median annual wage for this occupation within the industry, not wages for everyone in the industry or entry-level wages. |
| Management of companies and institutions | 129,810 USD/year (2025) | Median annual wage for this occupation within the industry, not wages for everyone in the industry or entry-level wages. |
| Government (excluding state and local hospitals and schools) | 129,650 USD/year (2025) | Median annual wage for this occupation within the industry, not wages for everyone in the industry or entry-level wages. |
| Professional, scientific, and technical services | 124,130 USD/year (2025) | Median annual wage for this occupation within the industry, not wages for everyone in the industry or entry-level wages. |
2025 OEWS actuarial annual wages: 10th percentile 78,570 USD, median 130,000 USD, and 90th percentile 215,100 USD; this is the distribution of employed workers, not a graduate offer range.
When comparing finance and insurance, management of companies and enterprises, government, or professional and technical service roles, first check actuarial tasks and wage-statistics coverage; industry medians cannot be converted into individual offers either.
States, cities, employers, and working hours may differ; this page presents 2025 US national annual wage statistics. Check base pay, bonuses, examination support, and benefits separately against actual offers.
2025 US national employment stock, not recruitment count for that year；BLS actuaries occupational handbook
2025—2035 US national employment projections do not establish AI causation；BLS actuaries occupational handbook
2025—2035 average annual openings include replacement demand, rather than net employment growth；BLS actuaries occupational handbook
Which Holland interest types are closer to actuarial work?
O*NET identifies actuaries' occupational interest environment as CIE: Conventional, Investigative, and Enterprising. These provide exploration clues involving rules and records, problem analysis, and business explanations; use this page's fictional portfolio exercise to observe willingness to check measures, recalculate, and explain assumptions, without judging ability or hiring from interest codes.
Do not use it this way: The CIE order does not require an individual to score high on all three; liking mathematics still requires testing awareness of rules, recordkeeping habits, and communication tasks, and other interest codes cannot directly exclude anyone.
Test my Holland career interestsWhich Big Five personality traits are more relevant to this work?
The Big Five can support discussions of working style, but this page has no dedicated study of actuarial trait scores and performance. Editorial suggestions include observing planned review, acceptance of counterexamples and questions, and clear explanations of assumptions by deadlines; these behavioral observations support self-exploration without setting personality-score entry thresholds.
Do not use it this way: A single self-rating does not replace sustained calculation, review, and feedback; do not treat any trait name as a uniform actuarial personality requirement.
View my Big Five personality traitsCan MBTI be used to judge whether actuarial work suits me?
This page does not recommend an MBTI type as an actuarial qualification. Type preferences can support discussion of independent analysis versus collaborative expression, but actual tasks may require both; observe working styles through a data dictionary, recalculation sheet, and one-page explanation, without predicting examinations, hiring, or career achievements from four-letter types.
Do not use it this way: Type descriptions neither confer nor remove mathematical, validation, or communication abilities; when struggling, locate the specific task and assess abilities through courses, exercises, and feedback.
Understand my MBTI preferencesCan the Enneagram indicate whether this occupation suits me?
The Enneagram can prompt self-discussion, but this page has no dedicated evidence that one type better suits actuaries. Observe responses to anomalous data, model questioning, or deadline pressure, and whether you can record unknowns and revise analysis; do not substitute Enneagram labels for courses, examinations, work samples, or actual feedback.
Do not use it this way: Return discussion to specific behaviors, such as accepting errors found through independent recalculation; personality labels do not establish qualifications, role authority, or inevitable performance.
Explore my Enneagram patternsCan I become an actuary if I struggle with mathematics?
Actuarial work requires foundations in mathematics, statistics, and reproducible analysis. If your mathematics is currently weak, start with probability, statistics, and spreadsheets, then complete this page's frequency, severity, and loss-cost calculations to see whether you understand denominators and scenarios; one IQ test cannot replace learning or determine whether you can become an actuary.
Do not use it this way: This exercise tests only one foundational task; completing it does not equal passing professional examinations or competence in all modeling work. Check later difficulty against target courses and roles.
Understand my intelligence and reasoning performanceDo actuaries need high emotional intelligence?
Actuaries need to explain technical results to business staff and accept questions during review. Editorial suggestions include practicing restating business questions, distinguishing facts from assumptions, and clearly explaining uncertainty and matters beyond authority; this page sets no “high emotional intelligence” score threshold and has no dedicated predictive data linking assessment scores to actuarial performance.
Do not use it this way: Good expression cannot replace correct calculations and professional judgment; communication does not mean hiding limitations to make conclusions sound better or approving beyond authority.
Understand my emotional intelligence abilitiesFits better when: If you prefer assessing specific applications and terms, compare insurance underwriting tasks.
Watch for: Underwriting focuses on acceptance conditions for individual business; actuarial portfolio models do not automatically grant underwriting decision authority.
Explore related career: Insurance UnderwritersFits better when: If you prefer research design, sampling, and general inference, compare statisticians' tasks.
Watch for: Target questions and data design require separate learning; insurance business experience cannot replace general statistical research ability.
Explore related career: StatisticiansFits better when: If you enjoy testing methods, repeating calculations, and explaining model limitations, continue comparing statistical tasks.
Watch for: A task-based exploration suggestion, not a statement that statisticians and actuaries have identical qualifications or responsibilities.
Explore related career: StatisticiansFits better when: If you care more about market, credit, and financial-product exposures, compare financial risk roles.
Watch for: Develop knowledge of target financial products, exposures, and limit management; insurance risk experience does not equal investment authority.
Explore related career: Financial Risk SpecialistsFits better when: If you prefer life-science data and analytical tools, compare bioinformatics tasks.
Watch for: Develop genomic or molecular data knowledge; knowing statistical software does not mean you already have target-domain abilities.
Explore related career: Bioinformatics TechniciansFits better when: If you enjoy explaining scenario results, assumptions, and limitations to business staff, compare risk reporting tasks.
Watch for: Check models, data, and authorization for the target role; communication preferences do not establish readiness for a direct transfer.
Explore related career: Financial Risk SpecialistsDo not screen by one personality type. Editorial suggestions include observing specific behaviors such as data checks, sustained recalculation, assumption explanations, and accepting questions, then testing them through work samples and feedback.
Introverted preferences cannot directly exclude you. Prepare dictionaries and one-page explanations in advance, while still practicing business explanations, answering review questions, and promptly escalating unknowns.
Extroverted preferences do not guarantee competence. Explanations must correspond to correct data, reproducible results, and actual limitations; also observe patience in completing independent checks.
This page has no evidence that one MBTI type best suits actuaries. Observe abilities through specific tasks and work samples without ranking career priorities by type.
O*NET's occupational interest environment is CIE: Conventional, Investigative, and Enterprising. It provides clues involving rules, analysis, and business explanations, not an individual qualification threshold.
This page obtained no dedicated predictive data linking personality scores to actuarial performance. Occupational task descriptions cannot be converted into individual success probabilities; consider learning, work samples, and actual feedback.
If currently weak, first develop probability, statistics, and calculation foundations. Check denominators, units, and two independent 10% scenarios when completing the fictional portfolio; neither one exercise nor an IQ score establishes full occupational competence.
Communication behaviors include clear explanations, accepting questions, and recording unknowns; this page sets no emotional intelligence score threshold, and good expression cannot replace correct calculations and authorization.
This page has no dedicated evidence that the Enneagram predicts actuarial fit. Use it to discuss behaviors under error and pressure, then return to task performance and feedback.
Deliver this page's dictionary, A/B and combined calculations, independent sensitivity results, and one-page limitation notes; compare engagement with data, models, or communication, then check target roles without promising hiring from one work sample.
How to reduce risk：Retain field definitions, data lineage, and exclusion rules; check measures first when differences arise.
Evidence：O*NET actuarial tasks · O*NET actuarial tasks
Typical scenario：Continuing old experience assumptions after products or policyholder behavior have changed.
Roles most affected：Staff responsible for assumptions, pricing, and reserve review
Possible consequence：Model conclusions may persistently diverge from current risks.
How to reduce risk：Check assumptions through experience analysis and sensitivity tests; record grounds for changes and review them.
Evidence：O*NET actuarial tasks · NIST generative AI risk framework
Typical scenario：Moving script or calculation-logic changes directly into formal analysis.
Roles most affected：Model developers, validators, and users
Possible consequence：Produce untraceable calculation errors or biases.
How to reduce risk：Recalculate known samples, retain versions, and independently validate according to processes.
Evidence：NIST generative AI risk framework · O*NET actuarial tasks
Typical scenario：Treating analysis results directly as underwriting, investment, or signing decisions.
Roles most affected：Actuarial staff, business leads, and authorized approvers or signatories
Possible consequence：Unclear responsibility chains and decisions without necessary approval.
How to reduce risk：Define analysis, review, and approval roles; escalate beyond authority instead of signing independently.
Evidence：NIST generative AI risk framework · O*NET actuarial tasks
Typical scenario：Providing policy or claims details to unauthorized external tools.
Roles most affected：Data subjects, data managers, and users
Possible consequence：Customer information and business materials may be accessed or processed improperly.
How to reduce risk：Follow access authorization and rules at the work location; use only approved data and tools.
Evidence：BLS actuarial occupational materials · O*NET actuarial tasks
Typical scenario：Summaries omit important assumptions or model failure conditions.
Roles most affected：Report authors, reviewers, and business users
Possible consequence：Business users may misunderstand result applicability.
How to reduce risk：Align reports with model outputs, list assumptions and limitations, and have responsible personnel review them.
Evidence：O*NET actuarial tasks · NIST generative AI risk framework
| Job direction | Initial tasks | Evidence to present | Exercises without experience | Differences in responsibility |
|---|---|---|---|---|
| Actuarial analysis assistant | Check experience data and calculations | Field dictionary, check logs, and independent recalculation | Calculate frequency and loss costs using this page's fictional data | Does not independently approve rates or reserves |
| Pricing analysis direction | Compare loss costs and rate assumptions | Baseline and sensitivity results | Hold exposure fixed and compare changes in frequency and severity | Calculations are not final product pricing decisions |
| Reserve analysis direction | Organize claims development and explanations of changes | Data measures and methodological limitations | First explain that this page's single-period data are insufficient to estimate complete reserves | Material judgments require review under organizational governance |
| Actuarial modeling direction | Maintain code and model tests | Version, test, and recalculation records | Implement the same formulas and test zero-exposure errors | Tool-generated code does not remove validation obligations |
| Risk analysis direction | Organize stress scenarios and business explanations | Memorandum of assumptions, results, and limitations | Explain impacts of increased frequency or severity | Different from financial risk roles; transfers require developing target-task knowledge |
BLS lists a bachelor's degree as typical entry-level education.
Check mathematics, statistics, finance, and reproducible analysis foundations; not a worldwide statutory requirement.
Chinese proficiency assessment distinguishes associate actuaries and fully qualified actuaries.
Check US occupational examination requirements with target professional organizations and employers; no mutual recognition established.
This page provides fictional portfolio calculations.
Demonstrate abilities through dictionaries, formulas, recalculation, and limitations, without presenting them as actual project experience.
Code and spreadsheets support calculations and records.
Read specific announcements for target-role tool requirements; do not claim this page has obtained general recruitment statistics.
The observation period is the same complete year; Group A has exposure of 1000 policy-years, 200 claims, and total losses of 400000 CNY; Group B has 500 policy-years, 150 claims, and total losses of 450000 CNY.
All data are editorially fictional. Counts refer to claim events; amounts use the same period and currency and exclude expenses, profit, or reserves.
Exposure is measured in policy-years, not the number of policies at year end.
Calculate frequency = count/exposure, severity = loss/count, and loss cost = loss/exposure separately; then calculate combined values.
Increase the combined portfolio's frequency by 10% with severity unchanged; separately increase severity by 10% with frequency unchanged. Produce a recalculation sheet and one page of limitations.
A: frequency 0.2 claims/policy-year, severity 2000 CNY/claim, and loss cost 400 CNY/policy-year. B: 0.3 claims/policy-year, 3000 CNY/claim, and 900 CNY/policy-year.
Combined: exposure 1500, count 350, and losses 850000 CNY; frequency 7/30 ≈ 0.233333, severity 17000/7 ≈ 2428.571429, and loss cost 1700/3 ≈ 566.666667.
Scenario loss cost when each factor is increased separately by 10%: 1870/3 ≈ 623.333333 CNY/policy-year; do not change both factors simultaneously.
Not actual product rates, reserves, or investment advice. Single-period data are insufficient to identify trends, development factors, and product differences.
Check zero denominators, units, periods, and whether A/B group rates were incorrectly averaged; manual checking is possible without real customer data.
List exposure, counts, losses, periods, units, and exclusion rules.
Attach fictional inputs and explain why end-of-period counts cannot replace exposure.
Three rows for A/B/combined values and two separate scenarios.
Show numerators, denominators, and formulas, avoiding an average of the two groups' rates.
Explain the meaning of combined loss cost 566.666667 and the two scenarios 623.333333.
Clearly state that data are fictional, results are not rates, and expenses and development factors are excluded.
Record spreadsheet or code versions, zero-denominator tests, and independent recalculation.
If using AI, restrict it to draft purposes and explain human checks; do not claim an actual project.
| Stage | What may be checked | Observable evidence | Boundaries |
|---|---|---|---|
| Work-sample explanation | Explain exposure and the frequency denominator | Can recalculate A/B/combined values and explain why rates cannot simply be averaged | Editorial preparation question, not a question used by every employer |
| Calculation review | Explain two 10% scenarios | Demonstrate linear increases in loss cost when other quantities are held fixed | Do not directly approve fictional loss costs as rates |
| Exception handling | Identify zero denominators or inconsistent periods | Record where comparisons are impossible and stop unsupported calculations | Escalate real problems according to role processes |
| Business communication | Explain results and limitations on one page | Distinguish data, assumptions, and recommendations and explain insufficient single-period information | Check probation tasks and authorization against specific employer documents |
Read the occupational definition; specify actuarial inputs and outputs and distinctions from underwriting/approval.
Organize fictional A/B data, periods, units, and denominator meanings.
Calculate frequency, severity, and loss costs for both groups and the combined portfolio.
Change frequency and severity separately by 10%, retaining fixed quantities.
Check zero denominators, units, and incorrectly averaged rates; write a review log.
Write one page covering results, assumptions, limitations, and questions that cannot be answered.
Check against reference results or another person's recalculation, recording differences and willingness to continue practicing.
| Education / qualifications / authorization | Who it applies to | Value for the role | Whether it is a uniform requirement | Conditions to check | Investment information | Limitations |
|---|---|---|---|---|---|---|
| Bachelor's-level foundations | People preparing to explore US actuarial roles | Mathematics, statistics, and finance foundations | BLS typical entry-level education, not a worldwide legal requirement | Target-region, institution, and role requirements | Check actual course tuition and time | A degree does not guarantee employment |
| Chinese associate actuary | People meeting current application requirements | First tier of proficiency assessment | Do not infer a uniform requirement for every analytical role | Follow official rules and the current year's examination announcement | Check current-year fees and examination arrangements | Does not automatically grant signing authority |
| Chinese fully qualified actuary | People meeting the corresponding requirements | Higher-tier proficiency assessment | Check against the role and responsibilities | Check qualification rules, announcements, and appointment requirements | Costs and timelines are not estimated on this page | Does not equal role approval authority |
| US professional examinations / certification | Job seekers in the target market | Demonstrates corresponding professional knowledge | Check with professional organizations and employers | This page does not presume mutual recognition of Chinese and US qualifications | Check current-year official announcements | One examination is not full certification |
| Employer data and task authorization | People actually working in the role | Defines accessible materials and tasks | Check governance of the specific role | Written responsibilities and review and approval processes | On-the-job training follows institutional arrangements | Qualification certificates cannot replace authorization |
| Continuing learning and review training | Analysts at different stages | Maintain reproducible analysis and communication | Editorial suggestion, not a new statutory certificate | Check applicable professional standards and work-location requirements | Check actual courses or training plans | Does not guarantee promotion or wages |
Typical scope：Organize experience data and baseline calculations
Readiness evidence：Dictionary, recalculation, and anomaly logs
Credential boundary：Check degrees/examinations against the role; no independent approval
Next moves：Further study of pricing and reserve methods
Typical scope：Compare rate-related assumptions
Readiness evidence：Traceable inputs and sensitivity explanations
Credential boundary：Check professional requirements and authority with the employer
Next moves：Develop product and risk classification knowledge
Typical scope：Explain claims development and estimate changes
Readiness evidence：Explain methods, changes, and limitations
Credential boundary：Keep role review and approval authority separate
Next moves：Study data development and governance requirements
Typical scope：Explain risks and model limitations to business staff
Readiness evidence：Decision memorandum and review traceability
Credential boundary：Check signing/approval against actual roles
Next moves：Choose a professional direction or management responsibilities according to interests
Typical scope：Implement approved methods and recalculate
Readiness evidence：Known-sample tests and version records
Credential boundary：Tool ability is not a professional practice qualification
Next moves：Study statistical modeling and boundary tests
Typical scope：Maintain calculation processes and scenario tools
Readiness evidence：Requirements, code tests, and recalculation evidence
Credential boundary：Separate development and independent validation responsibilities
Next moves：Further study of model validation
Typical scope：Check applicability conditions and failure situations
Readiness evidence：Experience comparisons, sensitivity, and limitations
Credential boundary：Review validation conclusions under governance
Next moves：Develop experience reviewing across models
Typical scope：Confirm fields, periods, and permissions
Readiness evidence：Dictionary, lineage, and cleaning logs
Credential boundary：Access data according to authorization
Next moves：Study traceable data processing
Typical scope：Maintain input checks and result recalculation
Readiness evidence：Anomaly records and reproducible calculations
Credential boundary：Follow institutional task and data authorization
Next moves：Develop data quality and automation experience
Typical scope：Coordinate differences in measures and responsible people
Readiness evidence：Issue closure and impact explanations
Credential boundary：Cannot independently expand data purposes
Next moves：Study model input governance
Can explain exposure, event counts, amounts, observation periods, and input limitations.
Can independently recalculate baselines and scenarios and identify errors in units, formulas, and denominators.
Can explain grounds for assumptions, failure conditions, and sensitivity without hiding uncertainty.
Can retain version and review evidence, explain limitations clearly to business staff, and escalate beyond authority.
United States · Actuaries (SOC 15-2011)
9%
Projected employment growth rate
The proportional change in total employment over the entire projection period, not an annual growth rate.
Does not establish：National projections do not guarantee individual employment or imply simultaneous growth in every region.
United States · Actuaries (SOC 15-2011)
Approximately 1,500 openings/year
Average annual openings
Includes demand to replace people changing occupations or exiting the labor force, rather than equaling net employment additions.
Does not establish：An annual average over the projection period, not a ten-year cumulative total or real-time recruitment count.
These are capability relationships, not automatic promotions or credential waivers.
Transfers：Risk modeling, scenario analysis, and result explanations.
Build next：Check knowledge of credit and market risk exposures, financial products, and limit management.
View career profileTransfers：Statistical modeling, data-quality checking, and uncertainty explanations.
Build next：Develop sampling, research design, and statistical inference methods for the research field.
View career profileTransfers：Insurance risk and product-term knowledge and communication of risk judgments.
Build next：Understand risk review, acceptance standards, and term authorization for individual applications.
View career profileTransfers：Data processing, statistical software, and analytical result checks.
Build next：Develop molecular or genomic data knowledge, life-science foundations, and relevant analytical tools.
View career profileTransfers：Mathematical modeling, scenario analysis, and independent validation.
Build next：Develop financial-instrument valuation, market-data knowledge, and trading-related models; does not directly authorize trading.
View career profileTransfers：Quantitative analysis, risk explanations, and report communication.
Build next：Develop corporate finance, investment-value, and securities analysis knowledge; actuarial experience does not grant investment-advice authority.
View career profileTransfers：Data quality, statistical modeling, and code recalculation.
Build next：Develop data engineering, model evaluation, and deployment methods for the target setting; more than simply knowing how to use AI tools.
View career profileTransfers：Benefit-plan analysis, quantity checking, and business communication.
Build next：Develop knowledge of human resources policies, job evaluation, and labor rules at the work location; different from insurance rate tasks.
View career profileObservable fit signals include willingness to investigate data anomalies, explain why assumption changes affect rates or liabilities, repeatedly validate calculations, and explain technical results to colleagues who do not build models.
Software can accelerate data cleaning, scenario calculations, code generation, and result compilation, but entry into production processes depends on data permissions, reproducibility, model validation, and organizational governance.
Software can accelerate data cleaning, scenario calculations, code generation, and result compilation.
People with defined responsibilities must still review and explain whether assumptions fit business and regulatory contexts, how anomalous data are handled, and whether model results can support rate or reserve decisions, taking responsibility for escalation.
Currently verifiable precise occupational wage materials cover US actuaries and do not support claims about mainland Chinese actuaries' national occupational median, uniform starting pay, or individual take-home income.
The median US actuarial annual wage in month 5 of 2025 was 130,000 USD; in that year's wage distribution, the lowest 10% earned below 78,570 USD and the highest 10% above 215,100 USD. These statistics describe employed workers' wages; the median is not starting pay for new entrants. Lower percentiles provide comparison references without promising individual entry wages.
US occupational materials list a bachelor's degree as typical actuarial entry-level education and state that becoming a certified professional generally requires passing a series of examinations.
Actuarial examination progress and professional certification may provide evidence of abilities, while employers, professional organizations, and local rules jointly determine specific-role authorization, signing responsibilities, and regulated-business scope.
FermatMind editorial compilation
Materials checked and revised in this round: 2026-10-07; statistical years and projection periods are marked separately in each source.
Occupational tasks and interests follow O*NET; US wages and projections are checked separately against BLS/O*NET statistics, and broad-scope Chinese wages follow the National Bureau of Statistics. Each field still needs binding to its corresponding original source.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：O*NET OnLine presents BLS OEWS wage data; preserve the displayed period, worker population, geography and unit. A direct BLS access failure does not invalidate this opened O*NET page.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：O*NET OnLine presents BLS OEWS wage data; preserve the displayed period, worker population, geography and unit. A direct BLS access failure does not invalidate this opened O*NET page.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：O*NET OnLine presents BLS OEWS wage data; preserve the displayed period, worker population, geography and unit. A direct BLS access failure does not invalidate this opened O*NET page.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：US national occupational statistics do not represent Chinese wages or individual employment guarantees.
Limitations：US occupational descriptions; transition conditions still require checking the target role.
Limitations：US occupational descriptions; transition conditions still require checking the target role.
Limitations：US occupational descriptions; transition conditions still require checking the target role.
Limitations：US occupational descriptions; transition conditions still require checking the target role.
Limitations：US occupational tasks and interests only, not Chinese qualifications.
Limitations：US employee wage distribution, not Chinese wages, starting pay, or individual offers.
Limitations：National projections do not equal recruitment volume or individual hiring.
Limitations：Industry means are not precise actuarial wages.
Limitations：Potential task exposure, not an actuarial replacement proportion.
Limitations：Potential exposure, without predicting adoption timing.
Limitations：Cross-industry employer survey, not actuarial-specific employment causation.
Limitations：Risk framework, not empirical evidence of effects.
Limitations：Chinese proficiency-assessment qualifications cannot replace role signing authorization.
Limitations：Supports target-occupation tasks only; comparisons of transition abilities are editorial inferences, not transfer success rates.
Limitations：Supports target-occupation tasks only; comparisons of transition abilities are editorial inferences, not transfer success rates.
Limitations：Supports target-occupation tasks only; comparisons of transition abilities are editorial inferences, not transfer success rates.
Limitations：Supports target-occupation tasks only; comparisons of transition abilities are editorial inferences, not transfer success rates.
Limitations：Supports underwriters' review of specific applications; the comparison is editorial compilation and does not establish Chinese approval limits or qualification requirements.
Limitations：Verification suggestions designed from registered occupational tasks, qualification materials, and data protection boundaries; not measured recruitment, an entry checklist, or independent effect research.
Occupational tasks and interests follow official US classifications; AI uses and risk scenarios are editorial suggestions, and broad-scope research is not presented as actuarial-specific empirical evidence. Precise Chinese occupational wages and recruitment samples retain explicit gaps; no independent practitioner review is claimed.
Big Five (English) · MBTI personality test · Enneagram personality test · IQ reasoning test
Ask who reviews data and assumptions, who recalculates results, and who may sign or approve uses; clarify the escalation path for new entrants' anomalies, without inferring authority from software outputs or examination certificates.
Ask about reporting or product cycles, communication audiences, and deadlines; confirm willingness to continually check anomalies, repeat calculations, and explain limitations to nontechnical colleagues.
Confirm which data may be accessed, how versions are retained, and how personal information is protected; use fictional or lawfully de-identified materials for entry work samples, without uploading real policies or customer information.
Typical scope：Explain version changes and risks
Readiness evidence：Change impacts and independent validation evidence
Credential boundary：Approval authority depends on role authorization
Next moves：Explore professional governance directions
Typical scope：Organize review materials and conclusion limitations
Readiness evidence：Traceable versions, roles, and explanations
Credential boundary：Professional qualifications do not replace approval authority
Next moves：Develop statistical or risk abilities for target tasks
Limitations：Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
