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ACSC71-300: Insurance Models


This subject is designed to develop mathematical and statistical modelling skills useful in actuarial applications, in particular, general insurance. The breadth of topics covered provides students with the principles and practical skills for a variety of general insurance risk management applications, including insurance pricing, predicting outstanding insurance liabilities, forecasting and inferring time series behaviour.

Subject details

FacultyBond Business School
Semesters offered
  • September 2019 [Standard Offering]
Study areas
  • Actuarial Science
Subject fees
  • Commencing in 2019: $4,890
  • Commencing in 2020: $5,070

Learning outcomes

1. Demonstrate the ability to calculate probabilities and moments of loss distributions under both with and without limits and risk-sharing arrangements.
2. Demonstrate the ability to construct risk models involving frequency and severity distributions.
3. Demonstrate advanced knowledge of ruin theory for a risk model.
4. Apply Bayesian statistics to calculate credibility premiums in credibility premium models.
5. Explain the fundamental concepts of a generalised linear model (GLM) and how a GLM may apply.
6. Apply the techniques for analysing a delay (or run-off) triangle and projecting the ultimate position.
7. Demonstrate advanced knowledge of basic time series models and “Monte Carlo” simulation using a series of pseudo-random numbers.
8. Demonstrate the ability to produce a written report that communicates ideas clearly, cogently and thoroughly communicates, using a professional style and format.

Enrolment requirements

Requisites: ?


Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Possess demonstratable knowledge in mathematical statistics and probability theory to the level of a unit such as ACSC71-200 Mathematical Statistics.

Restrictions: ?


Subject outlines

Subject dates

Standard Offering
Enrolment opens11/11/2018
Semester start14/01/2019
Subject start14/01/2019
Cancellation 1?28/01/2019
Cancellation 2?04/02/2019
Last enrolment27/01/2019
Withdraw – Financial?09/02/2019
Withdraw – Academic?02/03/2019
Teaching census?08/02/2019
Standard Offering
Enrolment opens14/07/2019
Semester start09/09/2019
Subject start09/09/2019
Cancellation 1?23/09/2019
Cancellation 2?30/09/2019
Last enrolment22/09/2019
Withdraw – Financial?05/10/2019
Withdraw – Academic?26/10/2019
Teaching census?04/10/2019