Actuarial methods for segmentation

Teacher(s)

Denuit Michel;

Language

French

Prerequisites

Mastery of basic concepts in statistics and probability calculation, at the level of courses in the FSA1BA, INGE1BA, MATH1BA programs or the access minor in statistics, actuarial sciences and data science.

Main themes

Insurance risk classification : techniques, regulation and ethics. Adverse selection and actuarial fairness. Method of marginal totals and calibration. Performance measures. Credibility and bonus-malus.

Learning outcomes

At the end of this learning unit, the student is able to :

With regard to the A.A. reference framework (A.A. of the Master of Science in Actuarial Science program), this activity enables students to master the following A.A.s - As a priority, the following AA: 1.1, 1.2, 1.3, 1.4, 1.7, 1.8, 2.1, 2.3 - Secondary: 1.6, 2.2, 3.1, 3.3 At the end of this course, students will be able to Understand the principles of risk segmentation in insurance, premium setting and revision based on past claims experience. Analyze segmentation systems from a technical, regulatory and ethical perspective. Evaluate the performance of a segmentation system.

Bibliography

Matériel disponible en ligne, complété si nécessaire par
Denuit, M., Charpentier, A. (2005). Mathématiques de l'Assurance Non-Vie. Tome II: Tarification et Provisionnement. Collection Economie et Statistique Avancées, Economica, Paris.
Denuit, M., Maréchal, X., Pitrebois, S., Walhin, J.-F. (2007). Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems. Wiley, New York.
Denuit, M., Hainaut, D., Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries Volume 1: GLM and Extensions. Springer Actuarial Lecture Notes Series.
 

Teaching materials

• Syllabus, transparents, etc. sur moodle

Faculty or entity

> LSBA