Students

ACST3059 – PACE: Actuarial Modelling

2026 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Tandy Xu
jianhui.xu@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
ACST3058
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

Survival models will be used to estimate decrement rates from actual experience, compare these with standard rates, and prepare new tables. In constructing new tables, consideration will be given to risk factors; selection; data collection; graduation; and testing the graduation. The concept of actuarial modelling will be discussed. Methods for mortality projection will be described and applied. Profit testing of conventional and unit-linked contracts will also be covered. Machine learning will be introduced. The 'actuarial control cycle', a conceptual framework of the processes for developing and managing financial enterprises and products, will be studied. Students gaining a weighted average of credit across all of ACST3058, ACST3060 and the CS2-related components of the assessment in ACST3059 (minimum mark of 60% on all three components) will satisfy the requirements for exemption from the professional subject CS2 of the Actuaries Institute. Visit Employability Connect for important information on this unit.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Examine and employ a variety of exposed to risk, graduation and mortality projection techniques.
  • ULO2: Develop an understanding of aspects of the theory and practice of statistical learning methods.
  • ULO3: Model and critically analyse scenarios involving financial risks for various types of financial institutions and compare ways of managing these risks.
  • ULO4: Discuss the concept of the Actuarial Control Cycle and apply it both individually and collaboratively in teams to solve a variety of practical business problems involving financial and actuarial risks.
  • ULO5: Identify and apply the relevant statistical techniques in solving practical actuarial problems within the actuarial control cycle framework.

General Assessment Information

Delivery: Weekly lectures and tutorials. Second half of session applied to an Australian aged-care ("Support at Home") case study through two assessments, plus three Taylor Fry industry workshops (Weeks 6, 8, 10). Assessment: Individual Report 25%, Group Presentation 25%, Final Exam 50%.

Technology: R / RStudio (assessed R code in the Individual Report; R Practice Workbook on iLearn). iLearn for all materials and submissions. Recording software (e.g. Zoom) for the Group Presentation. Data files provided on iLearn.

Materials & readings: All slides, notes, tutorials, case-study briefs, workshop guides and the R Practice Workbook on iLearn. Recommended text: James et al., An Introduction to Statistical Learning. [mortality-modelling reading — add if set]. Group Presentation sources: AIHW, GEN Aged Care Data, My Aged Care, Support at Home program manual, Royal Commission; anchor paper Zhang, Shi & Huang (2023).

Other requirements: Group formation, workshop participation, and on-time submission of both case studies (with a GenAI declaration) as set out in the assessment details. 

Late Submission Penalties: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0. Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100. Example 2 (out of 30): If you score 27/30 but submit 20 hours late, you will lose 1.5 marks and receive 25.5/30.

 

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Formal examination 50% No Exam Period Individual No Observed
Professional practice: Actuarial solution to a real-world problem 25% No 01/11/2026 Individual and Group No Observed
Professional practice: Report on mortality and analytics 25% No 25/10/2026 Individual Yes Open

Formal examination

Assessment Type 1: Examination
Indicative Time on Task 2: 30 hours
Due: Exam Period
Weighting: 50%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to formally demonstrate the expertise you have gained in this unit, including both the Actuarial Control Cycle content and the technical mortality and data analytics content.

You will participate in a three-hour exam with 10 minutes reading time held during the University Examination period. Important information about the exam will be made available on the unit iLearn page. You should also review the MQ Exams website for general tips.   The exam is marked as a single integrated assessment.

Deliverable(s): Formal exam

Individual assessment
On successful completion you will be able to:
  • Examine and employ a variety of exposed to risk, graduation and mortality projection techniques.
  • Develop an understanding of aspects of the theory and practice of statistical learning methods.
  • Model and critically analyse scenarios involving financial risks for various types of financial institutions and compare ways of managing these risks.
  • Discuss the concept of the Actuarial Control Cycle and apply it both individually and collaboratively in teams to solve a variety of practical business problems involving financial and actuarial risks.
  • Identify and apply the relevant statistical techniques in solving practical actuarial problems within the actuarial control cycle framework.

Professional practice: Actuarial solution to a real-world problem

Assessment Type 1: Presentation task
Indicative Time on Task 2: 15 hours
Due: 01/11/2026
Weighting: 25%
Groupwork/Individual: Individual and Group
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to work with your peers to develop and present an actuarial solution to a real-world problem.

You will collaborate in groups to research a relevant actuarial problem, develop a proposed solution, and present your findings to peers and industry representatives.

Skills in focus:

  • Discipline knowledge
  • Critical thinking and problem solving
  • Communication skills
  • Work readiness

Deliverable(s): Group presentation [max. 15 minutes overall, 5 mins per person; combination of group mark (60%) and individual mark (40%)]

Individual and group assessment
On successful completion you will be able to:
  • Examine and employ a variety of exposed to risk, graduation and mortality projection techniques.
  • Develop an understanding of aspects of the theory and practice of statistical learning methods.
  • Model and critically analyse scenarios involving financial risks for various types of financial institutions and compare ways of managing these risks.
  • Discuss the concept of the Actuarial Control Cycle and apply it both individually and collaboratively in teams to solve a variety of practical business problems involving financial and actuarial risks.

Professional practice: Report on mortality and analytics

Assessment Type 1: Written Submission
Indicative Time on Task 2: 20 hours
Due: 25/10/2026
Weighting: 25%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

The purpose of this assessment is for you to apply actuarial data analysis techniques using R statistical software to a real-world actuarial problem.
 
 
You will analyse mortality experience and model transitions using actuarial methodologies, and produce a report that communicates the results of your data analysis and interpretation.
 
 

Skills in focus:

  • Discipline knowledge
  • Digital skills
  • Critical thinking and problem solving
  • Communication skills

 

Deliverable(s): Written report showcasing results of data analysis [max 5000 words]

 

Individual assessment
On successful completion you will be able to:
  • Examine and employ a variety of exposed to risk, graduation and mortality projection techniques.
  • Develop an understanding of aspects of the theory and practice of statistical learning methods.
  • Model and critically analyse scenarios involving financial risks for various types of financial institutions and compare ways of managing these risks.
  • Discuss the concept of the Actuarial Control Cycle and apply it both individually and collaboratively in teams to solve a variety of practical business problems involving financial and actuarial risks.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Delivery. Weekly lectures and tutorials. Second half of session applied to an Australian aged-care ("Support at Home") case study through two assessments, plus three Taylor Fry industry workshops (Weeks 6, 8, 10). Assessment: Individual Report 25%, Group Presentation 25%, Final Exam 50%.

Technology. R / RStudio (assessed R code in the Individual Report; R Practice Workbook on iLearn). iLearn for all materials and submissions. Recording software (e.g. Zoom) for the Group Presentation. Data files provided on iLearn.

Materials & readings. All slides, notes, tutorials, case-study briefs, workshop guides and the R Practice Workbook on iLearn. Recommended text: James et al., An Introduction to Statistical Learning. [mortality-modelling reading — add if set]. Group Presentation sources: AIHW, GEN Aged Care Data, My Aged Care, Support at Home program manual, Royal Commission; anchor paper Zhang, Shi & Huang (2023).

Other requirements. Group formation, workshop participation, and on-time submission of both case studies (with a GenAI declaration) as set out in the assessment details. 

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Unit information based on version 2026.06 of the Handbook