Pre-requisite: ECON2006 OR MATH2011
Aims and Objectives
Having successfully completed this module you will be able to:
- Apply problem solving and numerical skills
- Describe and apply basic principles of machine learning
- Use Bayesian approaches to credibility theory to calculate premiums in general insurance
- Understand 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
- Understand how simple forms of proportional and excess of loss re-insurance are arranged
- 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 the main concepts underlying the analysis of time series and how to apply them
- Understand the definitions of some basic insurance terms, particular those relating to short-term contracts
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
|Total study time||150|
Resources & Reading list
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2008). Loss Models: From Data to Decisions. New York: Wiley.
Dickson, D. C. M. (2005). Insurance Risk and Ruin. Cambridge: Cambridge University Press.
Boland, P. J. (2007). Statistical and Probabilistic Methods in Actuarial Science. Boca Raton, Fl: Chapman and Hall/CRC.
Dobson, A. J. (2001). An Introduction to Generalized Linear Models. 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.
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