Overview
- Provides an exhaustive and self-contained presentation of tree-based methods applied to insurance
- Gives a rigorous statistical analysis of tree-based methods
- Fills a gap in the literature on artificial intelligence techniques applied to insurance
- Written by actuaries for actuaries
- Based on more than a decade of lectures and consulting projects on the topic, by the three authors
- Offers several case studies in P&C insurance
Part of the book series: Springer Actuarial (SPACT)
Part of the book sub series: Springer Actuarial Lecture Notes (SPACLN)
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Table of contents (6 chapters)
Keywords
About this book
This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities.
The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful.
This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurancedata analytics with applications to P&C, life and health insurance.
Authors and Affiliations
About the authors
Michel Denuit holds masters degrees in mathematics and actuarial science as well as a PhD in statistics from ULB (Brussels). Since 1999, he has been professor of actuarial mathematics at UCLouvain (Louvain-la-Neuve, Belgium), where he serves as Director of the masters program in Actuarial Science. He has also held several visiting appointments, including at Lausanne (Switzerland) and Lyon (France). He has published extensively and has conducted many R&D projects with major (re)insurance companies over the past 20 years.
Donatien Hainaut is a civil engineer in applied mathematics and an actuary. He also holds a masters in financial risk management and a PhD in actuarial science from UCLouvain (Louvain-La-Neuve, Belgium). After a few years in the financial industry, he joined Rennes School of Business (France) and was visiting lecturer at ENSAE (Paris, France). Since 2016, he has been professor at UCLouvain, in the Institute of Statistics, Biostatistics and Actuarial Science. He serves as Director of the UCLouvain Masters in Data Science.
Julien Trufin holds master's degrees in physics and actuarial science as well as a Ph.D. in actuarial science from UCLouvain (Louvain-la-Neuve, Belgium). After a few years in the insurance industry, he joined the actuarial school at Laval University (Quebec, Canada). Since 2014, he has been Professor in actuarial science at the department of mathematics, ULB (Brussels, Belgium). He also holds visiting appointments in Lausanne (Switzerland) and in Louvain-la-Neuve (Belgium). He is an Associate Editor for the Journals “Astin Bulletin” and “Methodology and Computing in Applied Probability” and a qualified actuary of the Institute of Actuaries in Belgium (IA|BE).
Bibliographic Information
Book Title: Effective Statistical Learning Methods for Actuaries II
Book Subtitle: Tree-Based Methods and Extensions
Authors: Michel Denuit, Donatien Hainaut, Julien Trufin
Series Title: Springer Actuarial
DOI: https://doi.org/10.1007/978-3-030-57556-4
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-57555-7Published: 17 November 2020
eBook ISBN: 978-3-030-57556-4Published: 16 November 2020
Series ISSN: 2523-3262
Series E-ISSN: 2523-3270
Edition Number: 1
Number of Pages: X, 228
Number of Illustrations: 62 b/w illustrations, 6 illustrations in colour
Topics: Actuarial Sciences, Mathematical Models of Cognitive Processes and Neural Networks, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Business, Management, Economics, Finance, Insurance