Master of Commerce (Actuarial Science)
Course code: MC-COMACTS
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 one and a half years of full-time study or three years part time and requires completion of 150 points, comprised of:
- 7 core actuarial subjects and
- 5 elective subjects.
Student can elect to follow one of two pathways; the practice pathway or the research pathway.
Note: Students interested in progressing to the PhD program will need to follow the research pathway.
Sample course plan
View some sample course plans to help you select subjects that will meet the requirements for this coursework.
Practice pathway
Year 1
100 pts
Explore this course
Explore the subjects you could choose as part of this degree.
Practice pathway
Students complete all of the following subjects:
| Accordion | |
|---|---|
| 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. |
| Insurance Risk Models · 12.5 pts |
Topics considered in this subject include premium principles, including variance principle, Esscher principle, risk adjusted principle; applications of utility theory, premium calculation and optimal reinsurance retention levels; reinsurance problems; stochastic ordering; comparisons of random losses in terms of risk measures; ruin theory, explicit solutions for the probability of ultimate ruin, the effect of reinsurance on ruin probabilities. |
| Actuarial Practice and Control I · 12.5 pts |
Note: This subject is offered in two cohorts:
Topics include insurance markets and products; underwriting and risk assessment; policy design; actuarial modelling; actuarial assumptions and feedback; reserving methods. |
Students must take 25 points of actuarial subjects, selected from the following subjects:
| Accordion | |
|---|---|
| Actuarial Studies Projects - Part 1 · 12.5 pts |
This subject provides students with the experience of carrying out research independently on each of three topics chosen by the subject’s lecturers. For each topic, the student is required over eight weeks to conduct and present as an extended essay the results of an independent piece of actuarial science research. This subject involves a two-semester program of study. Students must enrol in two consecutive semesters, Actuarial Studies Projects - Part 1, semester 1 and Actuarial Studies Projects - Part 2, semester 2. |
| Insurance Risk Models II · 12.5 pts |
Topics considered in this subject include premium principles, including variance principle, Esscher principle, risk adjusted principle; applications of utility theory, premium calculation and optimal reinsurance retention levels; reinsurance problems; ruin theory, Lundberg's inequality, explicit solutions for the probability of ultimate ruin, application of Panjer's recursion formula, the probability and severity of ruin, the effect of reinsurance on ruin probabilities. |
| Mathematics of Finance IV · 12.5 pts |
This subject will consider the following topics: No-arbitrage pricing in continuous-time models. Completeness. Fundamental Theorem of Asset Pricing. Applications of martingales. Multidimensional Brownian motion in asset price models. Other asset price models. Pricing of path-dependent options. Computation methods. |
| Actuarial Science Research Report Part 1 · 12.5 pts |
A research essay not exceeding 10,000 words on a topic approved by the Head of Department. The word count includes bibliography, footnotes, appendices and the number of words which would take up space used for tables, formulae and charts. |
| Actuarial Science Research Report Part 2 · 12.5 pts |
Refer to ACTL90016 Actuarial Science Research Report Part 1 for details. |
Students complete both of the following subjects
| Accordion | |
|---|---|
| Actuarial Practice and Control III · 12.5 pts |
Analysis of investment portfolios and asset classes from the perspective of an appointed actuary, with a view to identifying assets that suit the requirements of a variety of general insurance, life insurance, superannuation and other defined benefit liabilities. |
| Actuarial Practice and Control II · 12.5 pts |
Note: This subject is offered in two cohorts:
Topics include assessment of solvency; analysis of experience; analysis of surplus; actuarial techniques in the wider fields; and an introduction to professionalism. |
Students complete 62.5 points of electives selected from the following list; core actuarial subjects; or other masters-level subjects in actuarial studies, economics, finance or mathematics, as approved by the Academic Program Director.
| Accordion | |
|---|---|
| Time Series Analysis and Forecasting · 12.5 pts |
Normally topics will include current techniques used in forecasting in finance, accounting and economics such as regression models, Box-Jenkins, ARIMA models, vector autoregression, causality analysis, cointegration and forecast evaluation, ARCH models. |
| Bayesian Econometrics · 12.5 pts |
The overall aim of this subject is to introduce students to the essential concepts and techniques/tools used in Bayesian inference and to apply Bayesian inference |
| Numerical Techniques in Finance · 12.5 pts |
Numerical techniques focuses on the theory and application of numerical methods for solving financial problems. The applications may include option valuation, value at risk, term structure modelling, portfolio simulation and optimisation and capital budgeting. These applications motivate the study of matrix methods, the solutions of linear and nonlinear equations, interpolation and approximation methods, numerical integration and Monte Carlo methods. No prior programming experience is required as the principles of programming are covered. |
| Systems Modelling and Simulation · 12.5 pts |
Modern science and business makes extensive use of computers for simulation, because complex real-world systems often cannot be analysed exactly, but can be simulated. Using simulation we can perform virtual experiments with the system, to see how it responds when we change parameters, which thus allows us to optimise its performance. We use the language R, which is one of the most popular modern languages for data analysis. |
| Mathematical Statistics · 12.5 pts |
The theory of statistical inference is important for applied statistics and as a discipline in its own right. After reviewing random samples and related probability techniques including inequalities and convergence concepts the theory of statistical inference is developed. The principles of data reduction are discussed and related to model development. Methods of finding estimators are given, with an emphasis on multi-parameter models, along with the theory of hypothesis testing and interval estimation. Both finite and large sample properties of estimators are considered. Applications may include robust and distribution free methods, quasi-likelihood and generalized estimating equations. It is expected that students completing this course will have the tools to be able to develop inference procedures in novel settings. |
| Business Risk Management · 12.5 pts |
Risk management is a key business activity that impacts the full range of organisational activities and functional areas across the enterprise. This subject surveys a spectrum of business risks from operational to strategic risks. It provides a foundation in enterprise risk management principles, tools and techniques such as risk scenario planning. |
| General Insurance Practice · 12.5 pts |
Note: This subject is offered in two cohorts:
Topics include the Australian General Insurance industry and products, actuarial estimation of claims cost, general insurance liabilities, general insurance pricing, capital management, accounting and regulatory reporting. |
| Computational Statistics & Data Science · 12.5 pts |
Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where "Big Data" problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, and generalised additive models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include unsupervised learning of meaningful components, bootstrap resampling and inference, cross-validation, the Expectation-Maximisation (EM) algorithm and variational approximation, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs samplers and the Metropolis--Hastings algorithm. |
| Computational Statistics & Data Science · 12.5 pts |
Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where "Big Data" problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, and generalised additive models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include unsupervised learning of meaningful components, bootstrap resampling and inference, cross-validation, the Expectation-Maximisation (EM) algorithm and variational approximation, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs samplers and the Metropolis--Hastings algorithm. |
Research pathway
Students complete all of the following subjects:
| Accordion | |
|---|---|
| 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. |
| Insurance Risk Models · 12.5 pts |
Topics considered in this subject include premium principles, including variance principle, Esscher principle, risk adjusted principle; applications of utility theory, premium calculation and optimal reinsurance retention levels; reinsurance problems; stochastic ordering; comparisons of random losses in terms of risk measures; ruin theory, explicit solutions for the probability of ultimate ruin, the effect of reinsurance on ruin probabilities. |
| Actuarial Practice and Control I · 12.5 pts |
Note: This subject is offered in two cohorts:
Topics include insurance markets and products; underwriting and risk assessment; policy design; actuarial modelling; actuarial assumptions and feedback; reserving methods. |
Students complete 25 points of actuarial subjects, selected from the following:
| Accordion | |
|---|---|
| Actuarial Practice and Control III · 12.5 pts |
Analysis of investment portfolios and asset classes from the perspective of an appointed actuary, with a view to identifying assets that suit the requirements of a variety of general insurance, life insurance, superannuation and other defined benefit liabilities. |
| Actuarial Practice and Control II · 12.5 pts |
Note: This subject is offered in two cohorts:
Topics include assessment of solvency; analysis of experience; analysis of surplus; actuarial techniques in the wider fields; and an introduction to professionalism. |
| Actuarial Studies Projects - Part 1 · 12.5 pts |
This subject provides students with the experience of carrying out research independently on each of three topics chosen by the subject’s lecturers. For each topic, the student is required over eight weeks to conduct and present as an extended essay the results of an independent piece of actuarial science research. This subject involves a two-semester program of study. Students must enrol in two consecutive semesters, Actuarial Studies Projects - Part 1, semester 1 and Actuarial Studies Projects - Part 2, semester 2. |
| Insurance Risk Models II · 12.5 pts |
Topics considered in this subject include premium principles, including variance principle, Esscher principle, risk adjusted principle; applications of utility theory, premium calculation and optimal reinsurance retention levels; reinsurance problems; ruin theory, Lundberg's inequality, explicit solutions for the probability of ultimate ruin, application of Panjer's recursion formula, the probability and severity of ruin, the effect of reinsurance on ruin probabilities. |
| Mathematics of Finance IV · 12.5 pts |
This subject will consider the following topics: No-arbitrage pricing in continuous-time models. Completeness. Fundamental Theorem of Asset Pricing. Applications of martingales. Multidimensional Brownian motion in asset price models. Other asset price models. Pricing of path-dependent options. Computation methods. |
| Accordion | |
|---|---|
| Actuarial Science Research Report Part 1 · 12.5 pts |
A research essay not exceeding 10,000 words on a topic approved by the Head of Department. The word count includes bibliography, footnotes, appendices and the number of words which would take up space used for tables, formulae and charts. |
| Actuarial Science Research Report Part 2 · 12.5 pts |
Refer to ACTL90016 Actuarial Science Research Report Part 1 for details. |
Students complete 62.5 points of electives selected from the following list; core actuarial subjects; or other masters-level subjects in actuarial studies, economics, finance or mathematics, as approved by the Academic Program Director.
| Accordion | |
|---|---|
| Time Series Analysis and Forecasting · 12.5 pts |
Normally topics will include current techniques used in forecasting in finance, accounting and economics such as regression models, Box-Jenkins, ARIMA models, vector autoregression, causality analysis, cointegration and forecast evaluation, ARCH models. |
| Bayesian Econometrics · 12.5 pts |
The overall aim of this subject is to introduce students to the essential concepts and techniques/tools used in Bayesian inference and to apply Bayesian inference |
| Numerical Techniques in Finance · 12.5 pts |
Numerical techniques focuses on the theory and application of numerical methods for solving financial problems. The applications may include option valuation, value at risk, term structure modelling, portfolio simulation and optimisation and capital budgeting. These applications motivate the study of matrix methods, the solutions of linear and nonlinear equations, interpolation and approximation methods, numerical integration and Monte Carlo methods. No prior programming experience is required as the principles of programming are covered. |
| Systems Modelling and Simulation · 12.5 pts |
Modern science and business makes extensive use of computers for simulation, because complex real-world systems often cannot be analysed exactly, but can be simulated. Using simulation we can perform virtual experiments with the system, to see how it responds when we change parameters, which thus allows us to optimise its performance. We use the language R, which is one of the most popular modern languages for data analysis. |
| Mathematical Statistics · 12.5 pts |
The theory of statistical inference is important for applied statistics and as a discipline in its own right. After reviewing random samples and related probability techniques including inequalities and convergence concepts the theory of statistical inference is developed. The principles of data reduction are discussed and related to model development. Methods of finding estimators are given, with an emphasis on multi-parameter models, along with the theory of hypothesis testing and interval estimation. Both finite and large sample properties of estimators are considered. Applications may include robust and distribution free methods, quasi-likelihood and generalized estimating equations. It is expected that students completing this course will have the tools to be able to develop inference procedures in novel settings. |
| Business Risk Management · 12.5 pts |
Risk management is a key business activity that impacts the full range of organisational activities and functional areas across the enterprise. This subject surveys a spectrum of business risks from operational to strategic risks. It provides a foundation in enterprise risk management principles, tools and techniques such as risk scenario planning. |
| General Insurance Practice · 12.5 pts |
Note: This subject is offered in two cohorts:
Topics include the Australian General Insurance industry and products, actuarial estimation of claims cost, general insurance liabilities, general insurance pricing, capital management, accounting and regulatory reporting. |
| Computational Statistics & Data Science · 12.5 pts |
Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where "Big Data" problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, and generalised additive models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include unsupervised learning of meaningful components, bootstrap resampling and inference, cross-validation, the Expectation-Maximisation (EM) algorithm and variational approximation, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs samplers and the Metropolis--Hastings algorithm. |