Topics in Non-Life Insurance (Skade2)

Course content

Various distributions that are useful in non-life insurance
The impact of deductibles on the insurance premium calculations
Maximum likelihood methods, including the adjustment for deductibles. Also use of the delta method.
Asymptotic properties of the estimators
Model selection methods
Some linear and generalized linear model theory

Course description: Distributions in insurance, deductibles, maximum likelihood estimation, model choice, non parametric method, linear and generalized linear models.


MSc Programme in Actuarial Mathematics


Learning outcome


  • Thorough understanding of distributions in non-life insurance.
  • Thorough understand of maximum likelihood methods in non-life insurance
  • Basic understanding of the specificities of non-life regression models, and their practical ramifications.
  • Thorough understanding mod model selection methods (Prediction based and goodness-of-fit based).
  • Familiarity with insurance data analysis and machine learning techniques, including implementation in R.


Proficiency in estimating and predicting risks, even when dealing with heavy-tailed data, censoring, truncation, covariates; utilizing both parametric and non-parametric methodologies.


  • Capacity to discern the appropriate technique for specific scenarios, as well as having an understanding of the strengths and limitations inherent in different modeling approaches.
  • Effective communication of model outputs and diagnostic findings, both in technical and practical terms.

5 hours of lectures and 2 hours of exercises each week for 7 weeks. These are given in the first 7 weeks in the course "Statistics in Insurance".

Lecture notes

Bachelor in insurance mathematics or similar.

Academic qualifications equivalent to a BSc degree is recommended.

This course is only available to students enrolled in the MSc Programme in Actuarial Mathematics in the study year 2023/24 and earlier.

7,5 ECTS
Type of assessment
Oral examination, 30 minutes
Type of assessment details
30 minutes oral exam. No preparation time and no aids.
Without aids
Marking scale
7-point grading scale
Censorship form
External censorship

Same as the ordinary exam.


Criteria for exam assessment

The student should convincingly and accurately demonstrate the knowledge, skills and competences described under intended learning outcome.

  • Category
  • Hours
  • Lectures
  • 37
  • Preparation
  • 114
  • Theory exercises
  • 14
  • Project work
  • 40
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Jostein Paulsen   (7-6c717576676b70426f63766a306d7730666d)
Phone+45 35 32 07 37 office: 04.3.19
Saved on the 29-04-2024

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