Master of Actuarial Science (Extended)
Course code: MC-ACTSCEX
March
Commonwealth Supported Places (CSPs) available
Access Melbourne is available
March
AUD $60,992 (2026 indicative first year fee)
IELTS 6.5: with no band less than 6.0
Course structure
Overview
The degree is designed to be completed in 2-years of full-time study or part time equivalent and requires completion of 200 points. The degree consists of 16 semester-length subjects comprising:
- 8 discipline core subjects;
- 2 capstone subjects; and
- 6 elective subjects.
Sample course plan
View some sample course plans to help you select subjects that will meet the requirements for this coursework.
Example 200 point plan
Year 1
100 pts
Year 2
100 pts
Explore this course
Explore the subjects you could choose as part of this degree.
Students complete all of the following core subjects:
| Accordion | |
|---|---|
| Mathematics of Finance I · 12.5 pts |
Topics include data analysis, the principles of actuarial modelling, the description of financial transactions; the understanding of real and nominal interest rates, the time value of money, the present value and accumulated value for a given cashflow, the term structure of interest rates; the duration, convexity and immunisation of cashflows; the equation of value and its usage to solve various practical problems, project appraisals. |
| Mathematics of Finance II · 12.5 pts |
Topics include: measures of investment risk, portfolio theory, models of asset returns, asset liability modelling, equilibrium models, the efficient markets hypothesis, stochastic models of security prices, and Brownian Motion and its application. |
| Mathematics of Finance III · 12.5 pts |
This subject aims to provide students with grounding in advanced financial mathematics, covering option pricing under the binomial model; risk‐neutral pricing of derivative securities; Brownian motion; introduction to Itô΄ formula and SDEs; stochas asset models; Black‐Scholes model; arbitrage and hedging; interest‐rate models; actuarial applications and simple models for credit risk. |
| Life Insurance Models I · 12.5 pts |
Topics include survival models concepts; estimation procedures for lifetime distributions; multiple state models; multiple decrements; binomial and Poisson model of mortality; actuarial applications of continuous‐time and discrete‐time Markov processes; exact and census methods for estimating transition intensities based on age. |
| Life Insurance Models 2 · 12.5 pts |
This subject provides the groundwork for the capstone subject Life Contingencies. It provides students with a framework for actuarial modelling. The subject also expands students’ existing knowledge of mortality modelling by introducing the important ideas of mortality variation in a population and selection effects, which have implications for the applied topic of pricing life insurance products. Building on this, models used for mortality projections and forecasting, and elementary principles of machine learning are provided. |
| Topics in Insurance and Finance · 12.5 pts |
Topics include distributions of accumulations and present values; stochastic interest rate models; time series models; an introduction to ruin theory; claim run-off triangles; stochastic simulation. |
| Probability · 12.5 pts |
This subject offers a thorough grounding in the basic concepts of mathematical probability and probabilistic modelling. Topics covered include random experiments and sample spaces, probability axioms and theorems, discrete and continuous random variables/distributions (including measures of location, spread and shape), expectations and generating functions, independence of random variables and measures of dependence (covariance and correlation), methods for deriving the distributions of transformations of random variables or approximations for them (including the central limit theorem). The probability distributions and models discussed in the subject arise frequently in real world applications. These include a number of widely used one- and two-dimensional (particularly the bivariate normal) distributions and also fundamental probability models such as Poisson processes and Markov chains. |
| Statistics · 12.5 pts |
This subject introduces the basic elements of statistical modelling, computation and data analysis. It is an entry point to further study of both mathematical and applied statistics, as well as broader data science. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised. Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing and Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests and analysis of variance. |
Students complete two capstone subjects:
| Accordion | |
|---|---|
| General Insurance Modelling · 12.5 pts |
Topics include loss distribution with and without risk sharing; collective risk model, calculation of moments and moment generating function of aggregate claims, recursion formulae, effect of reinsurance; individual risk model, recursion formulae and approximations; copulas; extreme value theorems; time series. |
| Life Contingencies · 12.5 pts |
This subject has two primary aims: To provide fundamental principles of actuarial modelling. To discuss techniques used to model and value cashflows dependent on death, survival, or other uncertain risks. |
Students complete all of the following elective subjects as well as one approved by the Program Director:
| Accordion | |
|---|---|
| Accounting and Finance for Actuaries · 12.5 pts |
This subject has two main objectives. Firstly, it is designed to enable students to have the ability to analysis and interpret the financial statements of companies and financial institutions. Secondly, to provide a basic understanding of corporate finance including a knowledge of the instrument used by companies to raise finance and manage financial risks. |
| Data Analytics in Insurance 1 · 12.5 pts |
This subject aims to provide students with basic training on modern data analytics methods, which include linear regression, classification, resampling methods, spline-based methods, generalised additive models and support vector machines. This subject focuses on applying the above methods to modelling non-life insurance claims frequency and severity. |
| Data Analytics in Insurance 2 · 12.5 pts |
Note: This subject is offered in two cohorts:
This subject aims to further develop students’ knowledge of modern analytical tools and techniques, including GLM, shrinkage techniques (e.g., LASSO and ridge regression), tree-based methods (e.g., random forests and GBM) and neural networks. It also teaches students to connect data analytics work to the actuarial control cycle and real-world business environments. Effective communication of findings to a range of business decision making audiences is also stressed. |
| Statistical Techniques in Insurance · 12.5 pts |
Topics include multiple linear regression; Spearman´s and Kendall´s measures of correlation; principal component analysis; generalised linear models; bootstrap method; Bayesian statistics; credibility theory. |
| Economics for Actuaries · 12.5 pts |
This subject introduces core economic principles and how these can be used in a business environment to help decision making and behaviour. It provides the fundamental concepts of microeconomics that explain how economic agents make decisions and how these decisions interact. It explores the principles underlying macroeconomics that explain how the economic system works, where it fails and how decisions taken by economic agents affect the economic system. |