NMAA06068U Topics in Non-Life Insurance (Skade2)
MSc Programme in Actuarial Mathematics
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.
Knowledge:
- 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.
Skills:
Proficiency in estimating and predicting risks, even when dealing with heavy-tailed data, censoring, truncation, covariates; utilizing both parametric and non-parametric methodologies.
Competences:
- 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.
Lecture notes
Academic qualifications equivalent to a BSc degree is recommended.
- Category
- Hours
- Lectures
- 37
- Preparation
- 114
- Theory exercises
- 14
- Project work
- 40
- Exam
- 1
- Total
- 206
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 minutes
- Type of assessment details
- 30 minutes oral exam. No preparation time and no aids.
- Aid
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
Same as the ordinary exam.
Criteria for exam assesment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under intended learning outcome.
Course information
- Language
- English
- Course code
- NMAA06068U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C
- Course capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Jostein Paulsen (7-74797d7e6f73784a776b7e7238757f386e75)