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Course module: 35V5A2-B-6
35V5A2-B-6
Data Analytics for Non-Life Insurance
Course info
Course module35V5A2-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Econometrics and OR; Econometrics & Operations;
Is part of
B Econometrics and Operations Research
Lecturer(s)
Lecturer
dr.ir. G.W.P. Charlier
Other course modules lecturer
Lecturer
ir. W.J.E.C. van Eekelen
Other course modules lecturer
Lecturer
prof. dr. J.H.J. Einmahl
Other course modules lecturer
Academic year2020
Starting block
SM 1
Course mode
Full-time
RemarksCaution: this information is subject to change
Registration openfrom 25/08/2020 up to and including 20/08/2021
Aims

The goal of this course is to provide concepts and techniques to model and monitor the risk associated with an insurance portfolio.

Recommended Prerequisites

Probability and Statistics

Content
An insurance portfolio typically consists of a large number of risks that are identically distributed. For the insurer, it is of utmost importance to have accurate tools to measure the aggregate insurance risk. We will consider several techniques to model claim frequencies in combination with claim sizes to compute or approximate these measures. Valuable concepts in this context are the Capital at Risk (CaR) and the probability of ruin. Clearly, the insurer can affect the aggregate risk through reinsurance. We will consider several reinsurance strategies. Combining claim frequencies and claim sizes is then used to determine a risk-based price for insurance contracts (tariffication). The insurer will invest the premia from the policy holders. This can give rise to credit risk and we introduce the Basel III/IV framework to measure the aggregated credit risk. The insurer can also affect the aggregate insurance risk through premium setting. Particular attention will be devoted to credibility theory and IBNR techniques. Finally, we will consider the theory of extreme risks with emphasis on heavy tailed distributions; also tail dependence will be studied in this framework.
 
 
 
Type of instructions

2 hours lecture a week; 2 hours tutorial biweekly

Type of exams

written exam (90%); assignment (10%).

there is a resit for the exam (90%); the assignment (10%) has no resit.

Compulsory Reading

1. Wuthrich, Mario V., Non-Life Insurance: Mathematics & Statistics (January 7, 2020). (Parts of) Chapters 1 through 4 and 7-9. Available at SSRN: https://ssrn.com/abstract=2319328 or http://dx.doi.org/10.2139/ssrn.2319328
2. Slides 'Analysis of Extremes'.
Course available for exchange students
Conditions of admission apply
Contact person
prof. dr. J.H.J. Einmahl
Timetable information
Data Analytics for Non-Life Insurance
Required materials
-
Recommended materials
-
Tests
Assignment (10%)

Written exam (90%)

Final grade

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Kies de Nederlandse taal