Methoden vergleichen

	Kredibilitätstheorie	Bonus-Malus-System	Verlustverteilungsmodell
Fachgebiet	Versicherungsmathematik	Versicherungsmathematik	Versicherungsmathematik
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1967	1995	2012
Urheber	Hans Bühlmann	Jean Lemaire	Klugman, Panjer & Willmot
Typ	Weighted linear blend of individual and collective experience	Actuarial experience-rating model	Parametric probability model
Wegweisende Quelle	Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin, 4(3), 199–207.	Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publishers. ISBN: 978-0-7923-9545-5	Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss Models: From Data to Decisions (4th ed.). Wiley. ISBN: 978-1-118-31532-3
Aliasnamen	Bühlmann Credibility, Experience Rating, Linear Credibility Estimator, Güvenilirlik Teorisi	No-Claim Discount System, Merit Rating System, Experience Rating in Automobile Insurance, Prim-Ceza Sistemi	Severity-Frequency Model, Aggregate Loss Model, Claim Size Distribution Model, Hasar Dağılımı Modeli
Verwandt	3	2	3
Zusammenfassung	Credibility Theory is an actuarial framework for estimating the pure premium of an individual risk by blending its own observed loss experience with the collective (portfolio) mean. Introduced by Hans Bühlmann in 1967, the method derives the optimal linear combination—the credibility-weighted premium—that minimises mean squared error. It extends classical experience rating to a rigorous statistical footing rooted in Bayesian and linear estimation principles.	A Bonus-Malus System (BMS) is an actuarial experience-rating mechanism used primarily in automobile insurance to adjust individual policyholders' premiums based on their personal claim history. Policyholders who remain claim-free receive premium discounts (bonus), while those who file claims are penalised with surcharges (malus). The framework was comprehensively formalised and analysed by Jean Lemaire in his landmark 1995 monograph, which remains the definitive reference for the design and evaluation of such systems worldwide.	A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998, fourth edition 2012), these models underpin premium rating, reserving, reinsurance pricing, and regulatory capital calculations across the insurance and risk-management industries.