Module overview
Linked modules
Pre-requisite: MATH2011 or ECON2006 or ECON2041
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Model the distribution of the aggregate claims for both the insurer and the re-insurer, particularly using the compound Poisson distribution
- Understand the definitions of some basic insurance terms, particular those relating to short-term contracts
- Use Bayesian approaches to credibility theory to calculate premiums in general insurance
- Understand the properties of some loss distributions: gamma, exponential, Pareto, generalised Pareto, normal, log-normal, Weibull and Burr, and how to fit them to complete claim size data
- Describe and apply basic principles of machine learning
- Understand how simple forms of proportional and excess of loss re-insurance are arranged
- Understand the fundamental concepts of generalised linear models and how they may be applied
- Apply problem solving and numerical skills
- Understand the main concepts underlying the analysis of time series and how to apply them
Syllabus
Review of distribution theory; loss distributions; risk models – collective and individual; reinsurance; copulas; extreme value theory; Bayesian credibility theory; machine learning; generalised linear models; time series.
Learning and Teaching
Teaching and learning methods
.
Type | Hours |
---|---|
Teaching | 45 |
Independent Study | 105 |
Total study time | 150 |
Resources & Reading list
Textbooks
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2008). Loss Models: From Data to Decisions. New York: Wiley.
Chatfield, C. (2004). The Analysis of Time Series. Boca Raton, Fl: Chapman and Hall/CRC.
Boland, P. J. (2007). Statistical and Probabilistic Methods in Actuarial Science. Boca Raton, Fl: Chapman and Hall/CRC.
Dickson, D. C. M. (2005). Insurance Risk and Ruin. Cambridge: Cambridge University Press.
The Faculty of Actuaries and The Institute of Actuaries (2009). Subject CT6 Core Reading: Statistical Method.
Dobson, A. J. (2001). An Introduction to Generalized Linear Models. Boca Raton, Fl: Chapman and Hall/CRC.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Assignment | 10% |
Class Test | 10% |
Assignment | 10% |
Exam | 70% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Exam | 100% |
Repeat Information
Repeat type: Internal & External