BSc Actuarial Science and Mathematics / Course details

Following up on the course unit Actuarial Insurance this unit introduces further concepts and models relevant to the non-life industry, in particular the important concept of ruin in a risk model is discussed.

This unit aims at providing students with a further grounding in the important statistical and probabilistic techniques and models relevant to the non-life insurance industry.

On completion of this unit, successful students will be able to: 
1. compare and contrast the Bayesian approach and the frequentist approach to Statistics. 
2. discuss the Bayesian approach to decision theory and inference problems, and determine the Bayesian solution for such problems. 
3. discuss the assumptions and goals of credibility theory, and compute the types of credibility estimates seen in the course. 
4. discuss the main categories making up the field of machine learning and categorise given situations accordingly. 
5. execute and evaluate principal component analysis and clustering on appropriate data sets. 
6. discuss and evaluate dependence between random variables using copulas and related concepts.

(i) Bayesian Statistics: decision theory and inference  
(ii) Credibility theory: Bayes, Buhlmann and Buhlmann-Straub  
(iii) Machine Learning: overview of the field & main categories, Principal Components Analysis & Clustering  
(iv) Copulas: examples & properties, generators, Kendall's tau and correlation

• Coursework 20% (one piece of take home coursework).
• Examination at the end of the semester, 80%.

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.

Insurance Risk and Ruin. Dickinson, D.C.M. (ISBN: 9780521176750)

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.  

Please see Blackboard / course unit related emails for any further updates