Aims and Objectives
Having successfully completed this module you will be able to:
- Use Bayesian approaches to credibility theory to calculate premiums in general insurance
- Demonstrate knowledge and understanding of the fundamental concepts of generalised linear models and how they may be applied
- Model the distribution of the aggregate claims for both the insurer and the re-insurer, particularly using the compound Poisson distribution
- Apply problem solving and numerical skills
- 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
- Understand how simple forms of proportional and excess of loss re-insurance are arranged
- Describe and apply basic principles of machine learning
- Understand the definitions of some basic insurance terms, particularly those relating to short-term contracts
- Understand the main concepts underlying the analysis of time series and how to apply them
Review of distribution theory; loss distributions; risk models – collective and individual; re-insurance; copulas: extreme value theory; Bayesian credibility theory; machine learning; generalised linear models; time series.
Learning and Teaching
Teaching and learning methods
|Total study time||150|
Resources & Reading list
Boland, P. J. (2007). Statistical and Probabilistic Methods in Actuarial Science. Boca Raton, Fl: Chapman and Hall/CRC..
The Faculty of Actuaries and The Institute of Actuaries (2009). Subject CT6 Core Reading: Statistical Method.
Chatfield, C. (2004). The Analysis of Time Series. Boca Raton, Fl: Chapman and Hall/CRC..
Dickson, D. C. M. (2005). Insurance Risk and Ruin. Cambridge: Cambridge University Press.
Dobson, A. J (2001). An Introduction to Generalized Linear Models. Boca Raton, Fl: Chapman and Hall/CRC.
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2004). Loss Models: From Data to Decisions. New York: Wiley.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External