Module Content

Throughout the module, students will use Python and/or R for statistical analysis.

Economics of insurance: Utility Theory, Insurance and Utility, Elements of Insurance, Optimal Insurance.
Risk Models: Models for individual claim random variable, sums of independent random variable, approximation for the distribution of sum, application to insurance.
Survival Analysis: Survival functions, time until death, curtate future lifetimes, force of mortality, life tables and relation to survival function, deterministic survivorship group, life table characteristics and recursion formulas, fractional age assumptions, other laws of mortality, select and ultimate tables.
Life Insurance: Insurance payable at moment of death (MOD): level benefit, endowment, deferred, varying benefit. Insurance payable at end of year of death (EOY), relationships between MOD and EOY.

Teaching Methods

The module will be delivered on campus, with weekly lecture and tutorial sessions.

Printed notes will be given ahead of time for each section of the course, to support and enhance students’ preparation and engagement during class sessions. Lectures will follow the notes, with discussions of the main theoretical topics, and study of examples of the applications of the theory. There will be a strong emphasis on student involvement in discussions in lectures, to encourage a more active approach to learning the material, and to allow the delivery to be tailored to build on the students’ current understanding. Throughout the module, students will use Python and/or R for statistical analysis.

Regular formative work in tutorial sessions will allow students to internalise the mathematical ideas and methods developed in the lectures, and lead to the development of problem-solving skills. This formative work will also feed back into the delivery of lectures and tutorials.

Assessment Methods

The module is assessed through a Written Exam.