Course co-ordinator(s): Dr Seva Shneer (Edinburgh), Marjan Qazvini (Malaysia).
Aims:
This course aims:
- to introduce and apply the statistical techniques used in the analysis of general insurance processes, including models for aggregate claims and simple reinsurance arrangements;
- to examine the assessment of premiums for short term insurance contracts;
- to introduce experience rating (a) using Bayesian credibility theory, and (b) in the context of no claims discount systems;
- to introduce classical ruin theory;
- to examine claims reserving through the use of run-off triangles;
- to cover the basics of simulation.
Summary:
- Conditional expectation and compound distributions
- Loss distributions
- Aggregate risk model and Individual risk model
- Risk sharing – simple reinsurance and deductibles
- Premium calculation principles
- Bayesian estimation and Credibility Theory
- Experience rating – No Claims Discount systems
- Ruin theory
- Claims reserving – run-off triangles
- Simulation
Detailed Information
Pre-requisite course(s): F79MA Statistical Models A (Project Course) .
Location: Edinburgh.
Semester: 2.
Syllabus:
The detailed syllabus is given in the workbook. There is also a link to it on the course webpage.
Learning Outcomes: Subject Mastery
A detailed list of the learning outcomes is given on the course webpage.
Learning Outcomes: Personal Abilities
At the end of the course, students should be able to:
-
- Demonstrate the ability to learn independently
- Manage time work to deadlines and prioritise workloads
- Use an appropriate computer package to process data
- Present results in a way which demonstrates that they have understood the technical and broader issues of risk theory
Reading list:
A substantial spiral-bound workbook is given to each student – it contains core lecture material, worked examples, illustrations, and all tutorials.
Assessment Methods:
2 hour examination at the summer diet.
SCQF Level: 10.
Credits: 15.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the lecturer
VISION: further information and course materials are available on VISION