Non-life Insurance
- Credits: 5
- Ending: Examination
- Range: 2P + 2C
- Semester: summer
- Year: 1
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Students will gain theoretical and practical knowledge in the field of Non-life insurance, they will be able to define and analyze insurance industries, within P&C products. The students will expand their knowledge of mathematical statistics and probability and in addition to the theoretical basis of statistical analysis or predictive modeling they will learn modern practical applications in non-life insurance, such as tariff analysis or claim reserving, using statistical software R or Python.
Knowledge
1. Identification and analysis of the insurance industry, within retail and commercial non-life insurance products
2. Understand the basic concepts, terminology and principles in non-life insurance
3. Acquisition of a solid mathematical basis and knowledge of procedures and mathematical-statistical methods applied in non-life insurance in the valuation of products and the estimation of technical provisions
4. Orientation in the basics of legislation in the field of non-life insurance
Competences
Based on the acquired knowledge, students are able to understand the statistical, mathematical, financial and IT procedures in insurance companies, administrative and regulatory bodies responsible for the supervision of P&C insurance. They will gain the ability to understand the practice and development of the insurance market not only in Slovakia but also the single market within the European Union, including its legal basis. The acquired theoretical and practical skills are to the extent recommended by the International Society of Actuaries for Non-life Insurance.
Skills
After completing the course, students can:
• use theoretical knowledge in data analysis in basic statistical models
• be familiar with the issue and apply appropriate procedures and models
• develop actuarial models and demonstrate an understanding of practical considerations and constraints related to tariff analysis and claim reserving
• use computational technology and statistical softwares (R/Python)
• clearly interpretation and presentation of the achieved results
Indicative content
1. Non-life insurance classification, KPI in P&C industry.
2. Generalized linear methods (GLMs) - model structure, multiplicative model, parameter estimation, testing the significance of the model and the significance of individual parameters.
3. Diagnostics and model quality, model selection, deviance.
4. Predictive modeling using GLMs in tariff analysis, segmented risk model.
5. Bayesian statistics and Credibility Theory.
6. Empirical Bayesian Credibility Theory.
7. Bonus-Malus systems (BMS) and No-Claim Discount (NCD) systems.
8. Markov analysis and Poisson process.
9. Calculation of relative premium rate in BMS system, BMS efficiency.
10. Claim reserving, Deterministic claim reserving methods: Chain-ladder, Inflation-adjusted Chain-ladder.
11. Deterministic methods for calculating technical provisions for claims: Arithmetic separation method, geometric separation method, Bornhuetter-Ferguson, Cape-Code.
12. Stochastic claim reserving methods.
13. Non-life insurance in Solvency II.
Support literature
1. Denuit, Michel, et al.: Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems. West Sussex : John Wiley & Sons, Inc., 2007.
2. Klugman, Stuart A., Panjer, Harry H., Willmot, Gordon E.: Loss Models: From Data to Decisions. 4th Edition. New Jersey: John Wiley & Sons, Inc., 2012.
3. Boland, P. J.: Statistical and Probabilistic methods in Actuarial Science, 2007.
4. Ohlsson, E., Johansson, B.: Non-Life Insurance Pricing with Generalized Linear Models. Berlín: Springer Nature Switzerland AG, 2010.
5. Pacáková, V.: Aplikovaná poistná štatistika. Bratislava: Elita, 2004
6. Cipra, T.: Riziko ve financích a pojišťovnictví: Basel III a Solvency II, 2015.
7. Zákon č. 39/2015 Z. z. (Zákon o poisťovníctve).
8. Charpentier, A.: Computation actuarial science with R. Taylor & Francis Group. 2015.
9. Strežo, M., Mucha, V., Šoltés, E., Páleš, M. Risk Premium Prediction of Motor Hull Insurance Using Generalized Linear Models. In Statistika : Statistics and Economy Journal. - Praha : Český statistický úřad, 2019, vol. 99, no. 4
Syllabus
1. Non-life insurance classification, KPI in P&C industry. 2. Generalized linear methods (GLMs) - model structure, multiplicative model, parameter estimation, testing the significance of the model and the significance of individual parameters. 3. Diagnostics and model quality, model selection, deviance. 4. Predictive modeling using GLMs in tariff analysis, segmented risk model. 5. Bayesian statistics and Credibility Theory. 6. Empirical Bayesian Credibility Theory. 7. Bonus-Malus systems (BMS) and No-Claim Discount (NCD) systems. 8. Markov analysis and Poisson process. 9. Calculation of relative premium rate in BMS system, BMS efficiency. 10. Claim reserving, Deterministic claim reserving methods: Chain-ladder, Inflation-adjusted Chain-ladder. 11. Deterministic methods for calculating technical provisions for claims: Arithmetic separation method, geometric separation method, Bornhuetter-Ferguson, Cape-Code. 12. Stochastic claim reserving methods. 13. Non-life insurance in Solvency II.
Requirements to complete the course
30% exams (using software support)
20% oral final exam
50% written final exam (using software support)
Student workload
Total study load (in hours): 130 hours
26 hours - participation in lectures,
26hours - participation in exercises,
13 hours - preparation for exercises, homeworks,
13 hours - preparation for written work,
52 hours - self-study in preparation for the exam.
Language whose command is required to complete the course
slovak
Date of approval: 11.03.2024
Date of the latest change: 15.05.2022