
Actuary Course
Master the full actuarial toolkit — from probability distributions and life contingencies to loss reserving and enterprise risk management. This course prepares you for professional actuarial exams and real-world practice across insurance, pensions, and finance. Build the quantitative and strategic skills that define a credentialed actuary.
What you will learn:
You will develop a rigorous foundation in probability, financial mathematics, and statistical modelling as applied to insurance and risk. The course covers life contingencies, mortality models, and annuity valuation for pricing life insurance products. You will master property-casualty ratemaking, credibility theory, and loss development methods used daily in practice. Advanced topics include stochastic interest rate models, copula-based dependence modelling, and economic capital frameworks. Supplementary modules address predictive modelling with GLMs and machine learning, data science tools in Python and R, and actuarial communication skills for executive audiences.
How you study in practice Actuary Course
How you practise Actuary Course
For companies looking to train their teams
With Elevify for businesses, the course includes exercises and examples tailored to your company and its specific needs.
Course content
8 Chapters • 40 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsFoundations of Actuarial Science
Foundations of Actuarial Science
Lesson 1 • Descriptive Statistics for Actuaries
Covers measures of central tendency, dispersion, and data visualisation relevant to actuarial datasets. Prepares learners for statistical inference in later chapters.
Lesson 2 • Core Probability Concepts
Introduces sample spaces, events, and probability axioms as the foundation for risk modelling. Directly supports later chapters on loss distributions.
Lesson 3 • Introduction to Financial Mathematics
Establishes time value of money, interest rates, and present value as prerequisites for life and casualty pricing. Links mathematics to financial decision-making.
Lesson 4 • The Actuarial Profession Overview
Defines the actuarial role across insurance, pensions, and finance. Establishes professional context before technical content begins.
Lesson 5 • Essential Mathematical Prerequisites
Reviews calculus, linear algebra, and summation notation required for actuarial models. Ensures uniform mathematical readiness across learners.
Chapter 2HideHide detailsSee detailsProbability Distributions and Risk Models
Probability Distributions and Risk Models
Lesson 1 • Discrete Probability Distributions
Covers Poisson, binomial, and negative binomial distributions for modelling claim counts. Connects frequency modelling to aggregate loss calculations.
Lesson 2 • Aggregate Loss Models
Combines frequency and severity distributions into aggregate loss models using convolution and Panjer recursion. Directly enables premium and reserve calculations.
Lesson 3 • Moments and Generating Functions
Derives moments, moment generating functions, and probability generating functions for key distributions. Supports analytical computation of aggregate loss statistics.
Lesson 4 • Continuous Loss Distributions
Examines exponential, gamma, Pareto, and lognormal distributions for severity modelling. Provides tools for selecting distributions based on tail behaviour.
Lesson 5 • Parameter Estimation Methods
Applies maximum likelihood estimation and method of moments to fit distributions to loss data. Prepares learners for credibility and ratemaking chapters.
Chapter 3HideHide detailsSee detailsInterest Rate Theory and Financial Models
Interest Rate Theory and Financial Models
Lesson 1 • Stochastic Interest Rate Models
Introduces short-rate models including Vasicek and Cox-Ingersoll-Ross for stochastic valuation. Prepares learners for option pricing and embedded guarantee valuation.
Lesson 2 • Bond Pricing and Yield Measures
Prices coupon bonds, computes yield to maturity, and analyses price-yield relationships. Provides tools for fixed-income portfolio management.
Lesson 3 • Term Structure of Interest Rates
Analyses spot rates, forward rates, and yield curves as tools for valuing fixed-income cash flows. Connects interest theory to bond pricing and liability valuation.
Lesson 4 • Immunisation and Asset-Liability Matching
Applies duration matching and full immunisation to protect surplus against interest rate shifts. Connects interest theory to actuarial balance sheet management.
Lesson 5 • Duration and Convexity
Derives Macaulay, modified, and dollar duration alongside convexity for interest rate sensitivity analysis. Directly supports immunisation and hedging strategies.
Chapter 4HideHide detailsSee detailsLife Contingencies and Mortality Models
Life Contingencies and Mortality Models
Lesson 1 • Survival Models and Life Tables
Introduces the survival function, hazard rate, and curtate future lifetime as core mortality modelling tools. Establishes notation used throughout life contingencies.
Lesson 2 • Multiple Decrement Models
Extends single-decrement life tables to multiple causes of exit such as death, disability, and withdrawal. Enables pricing of products with competing risks.
Lesson 3 • Life Annuity Valuation
Values life-contingent annuities including whole life, temporary, and deferred annuities. Supports pension and retirement product pricing in later applied chapters.
Lesson 4 • Net Premiums and Benefit Reserves
Derives net premiums using the equivalence principle and computes prospective and retrospective reserves. Provides the basis for statutory reserve calculations.
Lesson 5 • Actuarial Present Value of Benefits
Computes actuarial present values for whole life, term, and endowment insurance contracts. Connects mortality models to product pricing.
Chapter 5HideHide detailsSee detailsCredibility Theory and Ratemaking
Credibility Theory and Ratemaking
Lesson 1 • Bühlmann and Bühlmann-Straub Models
Derives the Bühlmann credibility formula and extends it to the Bühlmann-Straub model for varying exposure. Provides the industry-standard linear credibility estimator.
Lesson 2 • Trend and Development Adjustments
Applies loss trend factors and loss development triangles to project ultimate losses for ratemaking. Ensures premiums reflect future expected costs.
Lesson 3 • Insurance Ratemaking Fundamentals
Covers the pure premium and loss ratio methods for computing indicated rate changes. Connects credibility estimates to practical premium adjustments.
Lesson 4 • Classification Ratemaking and GLMs
Uses generalised linear models to estimate relativities for rating variables such as age and territory. Extends manual ratemaking to multivariate classification systems.
Lesson 5 • Foundations of Credibility Theory
Introduces limited fluctuation and Bayesian credibility as frameworks for blending observed and prior data. Establishes the conceptual basis for experience rating.
Chapter 6HideHide detailsSee detailsLoss Reserving Methods
Loss Reserving Methods
Lesson 1 • Reserve Adequacy and Validation
Evaluates reserve estimates using hindsight analysis, development diagnostics, and reasonableness tests. Ensures reserve selections meet professional and regulatory standards.
Lesson 2 • Loss Development Triangle Analysis
Constructs paid and incurred loss triangles and applies age-to-age factors to project ultimate losses. Establishes the data structure underlying all reserving methods.
Lesson 3 • Expected Loss and Cape Cod Methods
Derives the expected loss method and Cape Cod method as alternatives when data is sparse. Broadens the reserving toolkit for immature or volatile lines.
Lesson 4 • Chain-Ladder and Bornhuetter-Ferguson
Applies the chain-ladder method and the Bornhuetter-Ferguson method to estimate IBNR reserves. Compares the strengths of development-based and a priori loss ratio approaches.
Lesson 5 • Stochastic Reserving Models
Introduces Mack's model and overdispersed Poisson models to quantify reserve uncertainty. Supports risk capital allocation and solvency assessment.
Chapter 7HideHide detailsSee detailsRisk Measures and Enterprise Risk Management
Risk Measures and Enterprise Risk Management
Lesson 1 • Quantitative Risk Measures
Defines Value at Risk, Tail Value at Risk, and coherent risk measures for quantifying loss exposure. Provides the mathematical foundation for capital modelling.
Lesson 2 • Enterprise Risk Management Frameworks
Applies ERM frameworks to identify, assess, and monitor strategic, operational, and financial risks. Integrates quantitative risk measures into organisational governance.
Lesson 3 • Copulas and Dependence Modelling
Models dependence between risk sources using copulas beyond linear correlation. Enables realistic aggregate loss distributions for multi-line insurers.
Lesson 4 • Reinsurance and Risk Transfer
Analyses proportional and non-proportional reinsurance structures and their effect on retained risk. Supports optimal reinsurance purchasing decisions.
Lesson 5 • Economic Capital and Solvency Frameworks
Computes economic capital using internal models and connects results to regulatory solvency requirements. Links risk quantification to capital adequacy decisions.
Chapter 8HideHide detailsSee detailsAdvanced Actuarial Applications and Strategy
Advanced Actuarial Applications and Strategy
Lesson 1 • Predictive Modelling in Actuarial Practice
Applies regression, decision trees, and gradient boosting to actuarial pricing and fraud detection. Extends classical methods with modern machine learning tools.
Lesson 2 • Actuarial Communication and Reporting
Structures actuarial reports, opinions, and presentations for technical and non-technical audiences. Ensures professional standards are met in all deliverables.
Lesson 3 • Strategic Actuarial Leadership
Positions the actuary as a strategic adviser in pricing, capital allocation, and product development decisions. Develops skills to lead cross-functional actuarial initiatives.
Lesson 4 • Pension and Retirement Plan Valuation
Values defined benefit pension obligations using projected unit credit and entry age normal methods. Connects life contingency skills to long-term liability management.
Lesson 5 • Embedded Options and Guarantee Valuation
Values guaranteed minimum benefits and policyholder options using risk-neutral pricing and simulation. Addresses the growing complexity of life insurance product design.

Your valid completion certificate
This course is for you:
Maths or statistics graduate: seeking a structured entry point into actuarial practice.
Insurance analyst: wanting to formalise technical skills towards a professional designation.
Finance professional: looking to pivot into risk quantification and insurance pricing roles.
Pension or benefits administrator: aiming to deepen actuarial knowledge behind liability valuations.
Career changer from engineering: applying quantitative strengths to a new risk-focused profession.
Data scientist in insurance: building the actuarial domain knowledge their models currently lack.
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