ST818: Insurance Statistics

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N370019102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N370019101
ECTS value: 5

Date of Approval: 11-10-2021

Duration: 1 semester

Version: Approved - active

Entry requirements

A Bachelor’s degree in Mathematics, Applied Mathematics, Mathematics-Economy

Academic preconditions

Students taking the course are expected to have knowledge of

  • measure and integration theory 
  • probability theory
  • mathematical statistics

Course introduction

The aim of the course is to enable the student to work in a rigorous way with probability models for insurance. It is advantageous to combine the course with ST803 Extreme Value Statistics.

The course builds on the knowledge acquired in the courses ST521, MM548, MM544 (or MM835) and gives an academic basis for a master thesis project in insurance mathematics. 

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • Give the competence to handle model building and model calculations
  • Give skills to apply arguments and concepts from the basic disciplines in mathematics
  • Give knowledge about fundamental mathematical knowledge building, theory and methods

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:

  • reproduce definitions in insurance statistics within the scope of the course's syllabus
  • reproduce results in insurance statistics, together with their proofs, within the scope of the course's syllabus
  • apply the theory to solve problems in insurance
  • relate the results within the scope of the course's syllabus to each other
  • perform programming relevant to the content of the course in the statistical package used in the course.


The following main topics are contained in the course:
  • models for claim numbers: Poisson process (homogeneous and inhomogeneous), renewal process
  • claim size distributions: exploratory tools, heavy and light tailed distributions, regular variation
  • the distribution of the total claim amount


See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)


Mid April


Oral exam




Second examiner: Internal


7-point grading scale


Student Identification Card


Normally, the same as teaching language

Examination aids

Allowed in the preparation time before the oral exam. Not allowed during the oral exam.
A closer description of the exam rules will be posted i itslearning.

ECTS value


Indicative number of lessons

32 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 24 hours
  • Skills training phase:  8 hours of tutorials
Activities during the study phase:
  • Solution of assignments in order to discuss these in the exercise sections
  • Self study of the course material.
  • Reflection upon the intro and training sections

Teacher responsible

Name E-mail Department
Yuri Goegebeur Data Science


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.