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Actuary Course
From 4 to 360h of flexible workload

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

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Course content

8 Chapters40 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

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 2See details

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 3See details

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 4See details

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 5See details

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 6See details

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 7See details

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 8See details

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.

Certification
Certification

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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