DESCRIPTION

This course has been conceived by actuaries for actuaries, accounting for all the specificities of insurance data instead of simply re-using standard recipes borrowed from other fields. The sessions proceed step by step, recalling the fundamental statistical concepts at the heart of tree-based methods like random forests. Their relative merits are illustrated by means of several case studies with insurance data.
The sessions aim to be interactive, alternating between methodological parts and case studies performed in front of the audience. Participants are invited to bring their own PC. Documentation including data sets and R code is made available through a supporting website. The installation of R packages prior to attendance is required.

Participants receive free copies of the reference manuals (co-authored by the trainers):

• Denuit, M., Hainaut, D., Trufin, J. (2020). Effective Statistical Learning Methods for Actuaries. Volume 2: Tree-Based Methods. Springer Actuarial Lecture Notes Series.

PROGRAM

1. TREE-BASED METHODS

• Introduction to recursive partitioning
• Classification and regression trees
• Bagging
• Random forests
• Tree-based boosting

ACQUIRED SKILLS

After completion of the training session, participants will have acquired a general knowledge of insurance analytics. They will be able to select the appropriate approach for their own data, run the R code and interpret the results. One month after the end of the training, a follow-up discussion is organized to share experience in implementing the approach that have been presented.