Intended learning outcomes
On successful completion of this degree students will be able to:
- Describe, explain and apply the fundamental theories of actuarial science as they apply in life insurance, general insurance and superannuation;
- Assess the suitability of actuarial, financial and economic models in solving actuarial problems;
- Interpret and critically evaluate articles in the actuarial research literature.
- Analyse actuarial data using advanced statistical techniques;
- Calculate quantities such as premiums, reserves and superannuation contribution rates using actuarial techniques;
- Analyse real and hypothetical problems in insurance and superannuation;
- Demonstrate creativity and initiative in application of knowledge to problem solving and innovation;
- Describe the core areas of actuarial practice and relate to those areas actuarial principles, theories and models;
- Demonstrate a capacity to successfully work independently with personal accountability;
- Execute a project requiring research or a real-world application;
- Apply data analytics techniques in wider actuarial fields.
- Recognising the interrelationships and synergies which exist between the disciplines of the faculty;
- Synthesizing ideas, theories and data in developing solutions to actuarial problems;
- Critical evaluation of evidence in support of an argument or proposition;
- Problem solving in actuarial practice including general insurance, life insurance, superannuation, and investment, through the application of appropriate theories, principles and data;
- The use of software packages applicable to actuarial and statistical modelling;
- Understanding the principles, processes, impact and functions of data science;
- Written and oral communication of actuarial ideas, theories and solutions to peers and the wider community;
- Research including the retrieval of information from a variety of sources.
- Receptive to alternate ideas through a review of the literature and through class participation and assessment;
- Ethical in their approach to research and work practices;
- Advanced in their use, critical evaluation and testing of actuarial models;
- Adept in statistical reasoning through completion of core quantitative subjects in the degree;
- Advanced in problem solving through their understanding of financial, statistical and actuarial techniques;
- Skilled in working effectively with computer software for the analysis of data;
- Adept at retrieval, summary and interpretation of actuarial and financial information through class exercises and assessment;
- Able to apply and synthesise mathematical, statistical, financial and actuarial theory, models and evidence to a variety of financial and insurance issues;
- Able to apply data science in insurance;
- Independent and effective in communication of ideas;
- Able to collaborate and be effective in team work.
Last updated: 29 January 2022