What Is Actuarial Science? Research Areas, Funding, and Career Paths

Actuarial science is the discipline that applies mathematics, statistics, and financial theory to measure and manage the financial consequences of risk and uncertainty — principally the risk of death, illness, disability, accident, property loss, and longevity. Its core output is the technical machinery insurers, pension plans, and social-insurance systems use to price coverage, set reserves, and manage long-term solvency: mortality and morbidity tables, loss models, and stochastic projections of future cash flows under uncertainty. Actuarial science sits at the intersection of applied mathematics, statistics, economics, and finance, but it is distinguished from all of them by its focus on a specific professional output — a certified, defensible number (a premium, a reserve, a funding ratio) that a regulator, auditor, or board can rely on.

This guide answers “what is actuarial science” in real depth: its core questions and methods, its major subfields, and — because this page is published by CASRAI, a research-administration standards body — the funding landscape, research methods, and career pathways that matter to anyone conducting or administering actuarial research specifically.

What Is Actuarial Science? A Working Definition

Actuarial science studies how to quantify the financial impact of uncertain future events and how to structure financial arrangements — insurance contracts, pension plans, social-insurance programs — so that an institution can meet its obligations even though the timing and size of individual claims cannot be known in advance. Its central working tools are:

• Mortality and morbidity modeling. Building and validating tables and stochastic models of the probability that a given population experiences death, illness, disability, or other insured events at each future age or duration.
• Loss and claims modeling. For property, casualty, and health lines, modeling the frequency and severity of claims, and how both evolve as claims are reported and settled over time.
• Discounting and reserving. Converting a stream of uncertain future cash flows into a present-value liability an insurer or pension plan must hold reserves against today, under an assumed interest-rate and inflation environment.
• Pricing and product design. Setting premiums or contribution rates that are adequate to cover expected claims, expenses, and a margin for adverse deviation, while remaining competitive and regulator-approved.
• Risk aggregation and capital adequacy. Modeling how individual risks combine across a whole portfolio, and how much capital an insurer or pension fund needs to hold to remain solvent under stress scenarios — the domain of enterprise risk management (ERM).

Actuarial work is unusual among quantitative disciplines in being tightly coupled to professional certification and legal/regulatory accountability: an actuary’s signed opinion on reserve adequacy or pension funding is a formal, auditable statement with legal weight, not just an analytical result.

How Actuarial Science Relates to Neighboring Disciplines

Actuarial science is fundamentally an applied field — it borrows its theoretical core from mathematics and statistics and applies it to the specific problem of pricing and reserving for risk:

• Mathematics. Actuarial science rests directly on probability theory, stochastic processes, and calculus — the same foundation described in CASRAI’s guide to mathematics. Interest theory (the time value of money) and stochastic modeling of claims are both, at root, applied probability.
• Statistics. Actuarial methods — credibility theory, generalized linear models for claims, survival analysis for mortality — are specialized applications of the broader statistical toolkit described in CASRAI’s guide to statistics. Many actuarial techniques (e.g., credibility theory, a Bayesian shrinkage method for pricing) were developed inside the actuarial profession before being recognized more broadly as statistical methods.
• Economics and econometrics. Actuarial pricing depends on economic assumptions — interest rates, inflation, unemployment — and actuarial claims and mortality models increasingly borrow econometric techniques for modeling time series and panel data. See CASRAI’s guide to econometrics for the shared regression and time-series toolkit.
• Finance. Investment actuaries and enterprise-risk actuaries work directly with asset pricing, derivatives, and portfolio theory to manage the asset side of an insurer’s or pension plan’s balance sheet, not just the liability side.
• Decision and game theory. Setting premiums, reserves, and capital levels under competition and asymmetric information connects actuarial pricing to the formal study of strategic decision-making under uncertainty; see CASRAI’s guide to game theory for the underlying framework.
• Demography. Mortality and longevity modeling — the actuarial core of life insurance and pension work — overlaps substantially with demographic methods for projecting population survival and life expectancy.

What distinguishes actuarial science from all of these is its professional and regulatory context: an actuary’s work product is typically a certified opinion filed with an insurance regulator or pension oversight body, not a research finding alone.

Major Subfields of Actuarial Science

• Life and annuity actuarial science. Pricing and reserving for life insurance and annuity products, built on mortality-table construction, survival analysis, and long-duration discounting.
• Health actuarial science. Pricing and reserving for health and disability insurance, incorporating morbidity trends, claims-cost inflation, and (in the US) regulatory frameworks specific to health coverage.
• Pension and retirement actuarial science. Valuing defined-benefit pension obligations, setting employer contribution rates, and assessing the funded status of retirement plans and social-insurance systems over multi-decade horizons.
• Property and casualty (general insurance) actuarial science. Pricing and reserving for auto, homeowners, liability, and commercial-property coverage, centered on claims-frequency/severity modeling and loss-reserve development — the domain of the Casualty Actuarial Society specifically.
• Investment and financial actuarial science. Applying actuarial and quantitative-finance methods to asset-liability management, hedging, and investment strategy for insurers and pension funds.
• Enterprise risk management (ERM). Aggregating risk across an entire institution — underwriting, market, credit, operational — to model overall capital adequacy and solvency, often using stochastic simulation across correlated risk types.
• Catastrophe and climate-risk modeling. A growing subfield applying actuarial and statistical methods to price and reserve for extreme, correlated events — hurricanes, wildfires, floods — increasingly informed by climate science.

Who Funds Actuarial Science Research

Actuarial science’s funding landscape looks different from most academic quantitative fields: because the discipline is tightly organized around professional credentialing bodies with their own research mandates, a meaningful share of actuarial research funding flows through the profession itself rather than federal science agencies:

• Society of Actuaries (SOA) Research Institute. The SOA’s research arm funds and publishes applied actuarial research — mortality studies, experience studies, and emerging-risk reports — used directly by practicing actuaries. Its Committee on Knowledge Extension Research (CKER) specifically funds academic actuarial research, including joint grant competitions run with the Casualty Actuarial Society.
• Casualty Actuarial Society (CAS). Through its Research Grants Program and periodic Requests for Proposals, the CAS funds property-casualty-focused actuarial research (recent CAS-funded work has included climate-related and machine-learning topics), administered under its Publications & Research function and partly coordinated with the SOA’s CKER for individual grant competitions.
• The Actuarial Foundation. A nonprofit affiliated with the actuarial profession that funds actuarial and mathematics education initiatives and supports research and outreach connecting the profession to academic training.
• National Science Foundation (NSF). Actuarial science does not have its own NSF program, but the underlying mathematics and statistics — stochastic processes, probability, applied statistics — are eligible for funding through NSF’s Division of Mathematical Sciences (DMS), the same division that funds core statistics research described in CASRAI’s guide to statistics.
• Longevity and mortality research adjacent to NIH. Actuarial mortality and longevity modeling overlaps with population-aging research funded by the National Institute on Aging (NIA), part of NIH, though NIA funds this work for public-health and demographic reasons rather than as actuarial science specifically.

As with any funding landscape, program scope, budgets, and eligibility shift — research offices should confirm current guidelines directly on each funder’s own site before an application, rather than relying on a general overview like this one.

Research Methods and Tools in Actuarial Science

Actuarial research and practice share a common technical toolkit, applied at different levels of formality:

• Mortality and morbidity table construction. Building and graduating (smoothing) tables of death, illness, or disability rates by age and other risk factors from large claims or population datasets.
• Survival analysis. Modeling time-to-event data (time to death, lapse, or claim) using hazard-rate and life-table methods shared with biostatistics and demography.
• Generalized linear models (GLMs) and machine learning. The standard toolkit for pricing property-casualty and health insurance, modeling claims frequency and severity as a function of risk factors; increasingly supplemented by gradient-boosting and other machine-learning methods for rating and underwriting.
• Credibility theory. An actuarial-specific Bayesian shrinkage framework for blending an individual risk’s own limited claims experience with a broader class average.
• Stochastic simulation. Monte Carlo methods for projecting portfolios of correlated risks forward under thousands of simulated economic and claims scenarios, used heavily in reserving, capital modeling, and enterprise risk management.
• Loss-reserving methods. Techniques such as the chain-ladder method and stochastic reserving models for estimating claims that have occurred but are not yet fully reported or settled.
• Specialized actuarial and general statistical software. Purpose-built actuarial modeling platforms alongside general-purpose statistical and programming tools (R, Python, SAS) and spreadsheet-based modeling, which remains common for smaller-scale pricing and reserving work.

Career and Training Pathways

Actuarial science is unusual among research-adjacent quantitative fields in running on a professional-examination system rather than graduate credentialing alone:

• Undergraduate preparation. Most practicing actuaries hold an undergraduate degree in actuarial science, mathematics, statistics, or a related quantitative field, often from a program the profession has recognized for strong exam-pass outcomes.
• Validation by Educational Experience (VEE). The SOA requires candidates to demonstrate coverage of specific topics — economics, corporate finance, and applied statistics — through accredited coursework, separate from the exam sequence itself.
• Professional examinations. Credentialing runs through a multi-year sequence of rigorous, standardized exams administered by the profession’s own bodies rather than a university. In the US and Canada, the two principal credentialing bodies are the Society of Actuaries (SOA) — awarding the Associate (ASA) and Fellow (FSA) designations, primarily for life, health, retirement, and investment actuaries — and the Casualty Actuarial Society (CAS) — awarding the Associate (ACAS) and Fellow (FCAS) designations, primarily for property-casualty actuaries.
• Graduate study. A master’s degree in actuarial science, applied statistics, or quantitative finance is common for candidates aiming at research-heavy or highly technical roles (pricing science, enterprise risk management, catastrophe modeling), though it is not a substitute for the professional exams.
• Academic infrastructure. The SOA designates select university programs as Centers of Actuarial Excellence, recognizing strong curricula, faculty research, and exam outcomes — the closest actuarial-science analog to a research center in other quantitative fields.
• Employment context. The large majority of actuaries work inside insurers, reinsurers, pension consultancies, and government bodies (such as a national Social Security system’s office of the chief actuary) rather than in university research positions — a structural difference from most of the other disciplines in this series.

Frequently Asked Questions

What is the difference between an actuary and a statistician?

A statistician’s training is general-purpose — the theory and methods of inference from data across any domain. An actuary applies a subset of that toolkit specifically to pricing and reserving for insurance and pension risk, and is credentialed through profession-run exams rather than (or in addition to) a graduate statistics degree. In practice the two fields overlap heavily in method, particularly in claims modeling and survival analysis.

Do you need a PhD to work as an actuary?

No. Most practicing actuaries qualify through the SOA’s or CAS’s professional exam sequence combined with a bachelor’s degree, not a doctorate. A PhD is more relevant to actuarial-adjacent academic research roles or highly specialized quantitative-finance and risk-modeling positions than to standard actuarial practice.

Is actuarial science part of mathematics or business?

Both, depending on the program. Actuarial science is grounded in mathematics and statistics but is applied to a business and regulatory problem — pricing risk and certifying financial solvency — so university programs house it variously in mathematics, statistics, business, or dedicated actuarial science departments.

How long does it take to become a fully qualified actuary?

Reaching Fellowship (FSA or FCAS) typically takes several years of exam study alongside full-time work, since candidates generally sit for exams while employed as actuarial analysts rather than studying full-time. The exact timeline varies significantly by individual pace and specialty track.

What is the difference between actuarial science and econometrics?

Econometrics develops and applies statistical methods to test economic theory and estimate economic relationships in general. Actuarial science borrows some of the same regression and time-series toolkit but directs it narrowly at pricing and reserving for insurance and pension risk, within a profession-specific certification and regulatory framework econometrics does not have. See CASRAI’s guide to econometrics for more detail.

Related CASRAI Resources

This guide is part of CASRAI’s Branches of Science series, which organizes deep-dive guides to individual academic and scientific disciplines by category, with cross-links to every published guide in the series. For the mathematical and statistical foundations actuarial science builds on, see CASRAI’s guides to mathematics and statistics. For closely related quantitative fields, see CASRAI’s guides to econometrics and game theory, plus CASRAI’s broader research-methods resources for study design, measurement, and quantitative-analysis content relevant to any empirical discipline.