Statistical Methods in Insurance

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Apply problem solving and numerical skills
• Describe and apply basic principles of machine learning
• Understand the definitions of some basic insurance terms, particular those relating to short-term contracts
• Understand how simple forms of proportional and excess of loss re-insurance are arranged
• 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 the main concepts underlying the analysis of time series and how to apply them
• 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

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.

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Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.