Dr Xiaolin Zhu

This course is available on the BSc in Actuarial Science and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

Students must have completed Probability, Distribution Theory and Inference (ST202) and Stochastic Processes (ST302).

This course is an introduction to actuarial work in non-life insurance. The course covers a general overview of the industry, history of general insurance and risk-sharing arrangements. Loss distributions suitable for modelling individual and aggregate losses; statistical inference. Moment generating functions of the distributions: gamma, exponential, Pareto, generalized Pareto, normal, lognormal, Weibull, and others. The collective model: risk models involving frequency and severity distributions. Moments and moment generating functions of Compound distributions. Stochastic risk models: Compound Poisson processes. Reinsurance treaties: proportional, excess of loss, stop-loss, deriving the distribution, moment generating functions and other properties of the losses to the insurer and reinsurer under all the models above. Ruin theory, Lundberg theorem and an integral approach for the ruin probability. Fundamental concepts of Bayesian statistics, prior distributions, posterior distributions, loss functions, Bayesian estimators. Credibility theory; Bayesian models. Experience rating models and applications. Claims reserving: run-off triangles. Programming applications using R

This course will be delivered through a combination of lectures and classes totalling a minimum of 30 hours across Winter Term.This course includes a reading week in Week 6 of Winter Term.

A set of exercises which are similar to problems appearing in the exam will be assigned.

Notes are given out in the lectures. 

Exam (90%, duration: 3 hours) in the spring exam period.
Online assessment (10%) in the WT Week 11.