Michel Denuit, Donatien Hainaut, Julien Trufin

Effective Statistical Learning Methods for Actuaries III

Neural Networks and Extensions

• Broschiertes Buch

Produktbeschreibung

This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible.

Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting.
Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.

This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

Produktdetails

• Springer Actuarial
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-25826-9
• 1st ed. 2019
• Seitenzahl: 264
• Erscheinungstermin: 13. November 2019
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 416g
• ISBN-13: 9783030258269
• ISBN-10: 3030258262
• Artikelnr.: 56956250

Autorenporträt

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 masters degrees in physics and actuarial science as well as a PhD 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 associate editor for the Journals "Astin Bulletin" and "Methodology and Computing in Applied Probability" and qualified actuary of the Institute of Actuaries in Belgium (IA BE).   

Inhaltsangabe

Preface. - Feed-forward Neural Networks. - Byesian Neural Networks and GLM. - Deep Neural Networks.- Dimension-Reduction with Forward Neural Nets Applied to Mortality. - Self-organizing Maps and k-means clusterin in non Life Insurance. - Ensemble of Neural Networks.- Gradient Boosting with Neural Networks. - Time Series Modelling with Neural Networks.- References.

Rezensionen

"Intended for students and practicing actuaries, this book follows its presentations of neural network methods with detailed case studies using insurance data. ... The unified approach lays a solid foundation for understanding non-likelihood methods readers may later encounter." (David R. Bickel, Mathematical Reviews, May, 2021)