Basic Non-Life Insurance Mathematics (Skade1)

Course content

The course will give an overview of some important elements of non-life insurance and reinsurance:

Models for claim numbers: the Poisson, mixed Poisson and renewal process.
Stochastic models for non-life insurance risks, in particular the compound Poisson, compound mixed Poisson and renewal models.
Large and small claims distributions.
Premium calculation principles for the total claim amount of a portfolio.
Experience rating: calculation of the premium in a policy.
Bayes estimation and credibility theory.

Education

BSc Programme in Actuarial Mathematics

Learning outcome

At the end of the course, the students are expected to have the following knowledge:

Definition and properties of claim number processes; in particular Poisson processes, mixed Poisson processes and renewal processes.
Definition and properties of total claim amount processes in a portfolio.
The Cramer-Lundberg and the renewal model as basic risk models.
Methods for approximating the distribution of risk models.
Small  and large claim distributions and their properties.
Premium calculation principles and their properties.
Reinsurance treaties and their properties.
Bayesian methods in a non-life insurance context, in particular the
Bayes and linear Bayes estimators for calculating the premium in a policy.

The student will gain the following skills:

-Calculation of distributional characteristics of
the claim number and total claim amount processes, in particular their moments.
-Calculation of premiums for a non-life (re)insurance portfolio  and a non-life individual policy.
-Statistical skills for analysizing  small and large claim data. 
-Risk analyses  in a non-life portfolio.
-Proficiency in Bayesian methods in a non-life insurance context.

Competences:
 
At the end of the course, the student  will be able to
relate and illustrate theory and practice in a non-life insurance company.
He/she will be able to read the actuarial non-life literature and be operational in premium calculation and risk analysis.

5 hours of lectures and 3 hours of exercises per week for 7 weeks.

Examples on course literature:

T. Mikosch. Non-Life Insurance Mathematics.

An Introduction with the Poisson Process.

2nd edition, Springer 2009

Basic knowledge of probability theory, statistics and stochastic processes:

Stokastiske processer (Stok),
Sandsynlighedsteori (Sand) - alternatively Mål- og integralteori (MI) from previous years.
Forsikring og jura 1 (Forsik&Jura1)
Stochastic processes 2 - no later than at the same time,

or similar courses.

Oral
Feedback by final exam (In addition to the grade)

Oral feedback will be given on students’ presentations in class.

Feedback by final exam (in addition to the grade): In connection with written exam (oral reexam).

ECTS
7,5 ECTS
Type of assessment
Written examination, 3 hours under invigilation
Type of assessment details
...
Aid
Written aids allowed

No electronic aids are allowed.

 

Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 35
  • Preparation
  • 147
  • Theory exercises
  • 21
  • Exam
  • 3
  • English
  • 206

Kursusinformation

Language
English
Course number
NMAA05070U
ECTS
7,5 ECTS
Programme level
Bachelor
Duration

1 block

Placement
Block 2 And Block 4
Schedulegroup
B And C
The course is placed in 2B and 4C in 2022/23.
Capacity
No limit
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Thomas Valentin Mikosch   (7-736f717579696e4673677a6e34717b346a71)
Saved on the 28-02-2022

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