Master of Actuarial Science (Enhanced) (MC-ACTSCEN) // Attributes, outcomes and skills

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.

Generic skills

• 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.

Graduate attributes

• 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.