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ACST357 – General Insurance Pricing and Reserving

2017 – S2 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Ken Siu
Contact via email
E4A618
Please refer to the i-Learn announcement.
Angela Chow
Credit points Credit points
3
Prerequisites Prerequisites
ACST356 and STAT271
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This unit examines the use of statistical models in general insurance. The models include those used in time series analysis, generalised linear statistical modelling and runoff triangle models. Time series models are considered for both single and multiple time series. These models are often used for forecasting and inferring the behaviour of times series. Generalised linear models are used in the pricing of insurance such as automobile or homeowner insurance. Runoff triangle models are used to predict outstanding insurance liabilities. The use of no claim discount systems as a method of experience rating is also described. A good knowledge of the material covered in STAT271 is essential. Students should understand regression analysis, and the nature and use of a statistical model. Students gaining a grade of credit or higher in both ACST356 and ACST357 are eligible for exemption from subject CT6 of the professional exams of the Institute of Actuaries of Australia.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at http://students.mq.edu.au/student_admin/enrolmentguide/academicdates/

Learning Outcomes

  1. Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  2. Manage to perform statistical analyses relevant to this unit using the statistical package R
  3. Understand basic theories and methodologies on time series model building and forecasting as well as their applications
  4. Understand some important theories and techniques of generalized linear models (GLMs)
  5. Apply deterministic and stochastic methods for calculating outstanding claims provisions in general insurance
  6. Learn how to use run-off triangles for claims reserving and prediction

General Assessment Information

 

  • Criteria and standards for grading
    • ​Numerically correct answers based on correct reasoning
    • Assessment criteria for all assessment tasks will be provided on the unit iLearn site. 
  • Submission methods
    • Assignments are submitted via iLearn 
    • The class test is in class in Week 9
  • Late assessments, extensions, penalties, resubmissions
    • Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for disruptions to studies is made and approved.
    • Tasks above 10% - No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission – 20% penalty). This penalty does not apply for cases in which an application for disruption of studies is made and approved. No submission will be accepted after solutions have been posted. 
  • Midterm and Final examination conditions.
    • You are permitted ONE A4 page of paper containing reference material printed on both sides. The material may be handwritten or typed. The page will not be returned to you at the end of the final examination.
    • It is the responsibility of students to view their marks for each within session assessment on iLearn within 20 working days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment marks (not including the final exam mark) will not be addressed.

Assessment Tasks

Name Weighting Hurdle Due
Early Assessment Quiz 5% Week 3
Class Test 35% Week 9
Final Examination 60% In the university exam period

Early Assessment Quiz

Due: Week 3
Weighting: 5%

1. This will be an online quiz with five multiple choices questions.

2. It is envisaged that the online quiz will cover all materials in Weeks 1-2. 

3. Please use the quiz as an indicator of whether you are progressing satisfactorily in the unit. If you are having difficulties, please see the Unit Convenor and consider withdrawing before the census date on Friday of week 4.

4. Late assessments, extensions, penalties, resubmissions

  • Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for disruptions to studies is made and approved.
  • Tasks above 10% - No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission – 20% penalty). This penalty does not apply for cases in which an application for disruption of studies is made and approved. No submission will be accepted after solutions have been posted. 

 


This Assessment Task relates to the following Learning Outcomes:
  • Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  • Understand basic theories and methodologies on time series model building and forecasting as well as their applications

Class Test

Due: Week 9
Weighting: 35%

1. Non-programmable calculators with no text-retrieval capacity are allowed.

2. No dictionaries permitted.

3. One A4 sheet of notes double-sized (typed or handwritten) permitted.

4. It is envisaged that the class test will cover all materials from Week 1 to Week 7.

5. The class test will be held in the lecture.

6. Class test under Midterm and Final examination conditions described in the general assessment information section.

 


This Assessment Task relates to the following Learning Outcomes:
  • Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  • Manage to perform statistical analyses relevant to this unit using the statistical package R
  • Understand basic theories and methodologies on time series model building and forecasting as well as their applications
  • Understand some important theories and techniques of generalized linear models (GLMs)

Final Examination

Due: In the university exam period
Weighting: 60%

1. Non-programmable calculators with no text-retrieval capacity are allowed.

2. No dictionaries permitted.

3. One A4 sheet of notes double-sized (typed or handwritten) permitted.

4. It is envisaged that the final examination will cover all materials in the unit.

5. Class test under Midterm and Final examination conditions described in the general assessment information section.

 


This Assessment Task relates to the following Learning Outcomes:
  • Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  • Manage to perform statistical analyses relevant to this unit using the statistical package R
  • Understand basic theories and methodologies on time series model building and forecasting as well as their applications
  • Understand some important theories and techniques of generalized linear models (GLMs)
  • Apply deterministic and stochastic methods for calculating outstanding claims provisions in general insurance
  • Learn how to use run-off triangles for claims reserving and prediction

Delivery and Resources

Classes

The timetable for classes can be found on the University web site at: https://timetables.mq.edu.au/2017/

Required and Recommended Texts and/or Materials

Required texts

A set of lecture notes and study pack including tutorial exercises and R examples are available for downloading from the ACST357/862 teaching website.

Optional ActEd material

The ActEd CT6 are not set as required or recommended reading for this unit, since the lecture notes are comprehensive and detailed.

Other useful references:

  • Generalized linear models for Insurance Data. Cambridge University Press: Cambridge.
  • Hossack, I.B., Pollard J.H, and Zehnwirth, B. (1999). Introductory statistics with applications in general insurance, second edition. Cambridge University Press: Cambridge.
  • De Jong, P. and Heller, G.Z., (2008). Generalized linear models for Insurance Data. Cambridge University Press: Cambridge.
  • Casualty Actuarial Society. (2001). Foundations of Casualty Actuarial Science, 4th edition. Casualty Actuarial Society.
  • Kaas, R., Goovaerts, M., Dhaene, J. and Denuit, M. (2009). Modern actuarial risk theory using R, 2nd edition. Springer.

Some additional references will be listed in the lecture notes.

Technology Used and Required

  • Latex, PDF, Word and Excel are used for preparing the lecture and tutorial materials. 
  • The R statistical software package will be used throughout the unit. Excel may also be used in the unit.
  • Students will be required to use a non-programmable calculator in the final examination and during the in-class test.

Unit Web Page

To access the website, go to http://ilearn.mq.edu.au and login using your usual login and password.

Teaching and Learning Strategy

  • The unit is taught using three hours of lectures and a weekly tutorial. Tutorials will commence in Week 2.
  • Concepts and examples (including computing examples in R using real datasets in finance and insurance) will be discussed in the lectures.
  • Problem sets will be discussed in tutorials. 
  • You are expected to read lecture materials in advance of the lectures and to participate actively in the tutorial classes.

What has changed since the previous offering of this unit?

Unit Guides, Lectures, Tutorials and Assessments Materials (Whenever Applicable)

 

Unit Schedule

 

Week Number

Week Beginning Monday

Topic and Notes Tutorial
1

 

Time Series: Introduction; Stationary Time Series; ACF and PACF No tutorial
2

 

Time Series: Autoregressive (AR) Models; Moving Average (MA) Models;  Autoregressive Integrated Moving Average (ARIMA) Models

Tutorial Set 1
3

 

1. Time Series Box Jenkin Algorithm I: Identification and Estimation

2. Online Quiz

Tutorial Set 2

4

 

Time Series: Box Jenkin Algorithm II: Diagnostic Checking and Prediction

 

 

Tutorial Set 3

 

5  

 

GLMs: Review of Linear Regression; Introduction to GLMs

Tutorial Set 4
6   GLMs: Exponential Family Linear Predictor; Link Function Tutorial Set 5
7  

GLMs: Model Fitting, Selection and Analysis of Residuals

 

Tutorial Set 6

STUDY

BREAK

 

 

No classes

No classes

STUDY

BREAK

8  

 

Introduction to Claim Reserving 

 

Revision
9  

 

1. Class Test

2. Introduction to Claim Reserving 

Tutorial Set 7
10  

Outstanding Claims (deterministic)

Tutorial Set 8
11   Outstanding Claims (deterministic) Tutorial Set 9
12   Outstanding Claims (stochastic) Tutorial Set 10
13

 

1. Outstanding Claims (stochastic)

2. Revision

Tutorial Set 11

 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:

Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html

Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html

Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html

Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html​

Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html

Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policies/special-consideration

In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/

Results

Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.

Supplementary exams

Information regarding supplementary exams, including dates, is available at: http://www.businessandeconomics.mq.edu.au/current_students/undergraduate/how_do_i/disruption_to_studies

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Enquiry Service

For all student enquiries, visit Student Connect at ask.mq.edu.au

Equity Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

Problem Solving and Research Capability

Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.

This graduate capability is supported by:

Learning outcomes

  • Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  • Manage to perform statistical analyses relevant to this unit using the statistical package R
  • Understand basic theories and methodologies on time series model building and forecasting as well as their applications
  • Understand some important theories and techniques of generalized linear models (GLMs)
  • Apply deterministic and stochastic methods for calculating outstanding claims provisions in general insurance
  • Learn how to use run-off triangles for claims reserving and prediction

Assessment tasks

  • Early Assessment Quiz
  • Class Test
  • Final Examination

Discipline Specific Knowledge and Skills

Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.

This graduate capability is supported by:

Learning outcomes

  • Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  • Manage to perform statistical analyses relevant to this unit using the statistical package R
  • Understand basic theories and methodologies on time series model building and forecasting as well as their applications
  • Understand some important theories and techniques of generalized linear models (GLMs)
  • Apply deterministic and stochastic methods for calculating outstanding claims provisions in general insurance
  • Learn how to use run-off triangles for claims reserving and prediction

Assessment tasks

  • Early Assessment Quiz
  • Class Test
  • Final Examination

Critical, Analytical and Integrative Thinking

We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.

This graduate capability is supported by:

Learning outcomes

  • Understand some important techniques used by actuaries to perform analysis and modelling in general insurance pricing and reserving
  • Manage to perform statistical analyses relevant to this unit using the statistical package R
  • Understand basic theories and methodologies on time series model building and forecasting as well as their applications
  • Understand some important theories and techniques of generalized linear models (GLMs)
  • Apply deterministic and stochastic methods for calculating outstanding claims provisions in general insurance
  • Learn how to use run-off triangles for claims reserving and prediction

Assessment tasks

  • Early Assessment Quiz
  • Class Test
  • Final Examination

Research and Practice

Research and Practice

The student will learn some basic theories and methodologies in time series analysis, generalized linear models and run-off triangles which are expected to be useful for researching and practising general insurance pricing and reserving. 

 

 

Changes since First Published

Date Description
25/07/2017 The class test has been changed from Week 8 to Week 9.