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Effective Statistical Learning Methods for Actuaries III

Neural Networks and Extensions

  • Michel Denuit
  • Donatien Hainaut
  • Julien Trufin
Textbook

Part of the Springer Actuarial book series (SPACT)

Also part of the Springer Actuarial Lecture Notes book sub series (SPACLN)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 1-41
  3. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 43-61
  4. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 63-82
  5. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 83-109
  6. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 111-145
  7. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 147-166
  8. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 167-192
  9. Michel Denuit, Donatien Hainaut, Julien Trufin
    Pages 193-248
  10. Back Matter
    Pages 249-250

About this book

Introduction

Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance.

The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics.

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.

This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.

 

 

Keywords

62P05, 62-XX, 68-XX, 62M45 deep learing for insurance neural networks machine learning actuarial modeling insurance risk classification

Authors and affiliations

  • Michel Denuit
    • 1
  • Donatien Hainaut
    • 2
  • Julien Trufin
    • 3
  1. 1.Université Catholique LouvainLouvain-la-NeuveBelgium
  2. 2.Université Catholique de LouvainLouvain-la-NeuveFrance
  3. 3.Université Libre de BruxellesBrusselsBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-25827-6
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-25826-9
  • Online ISBN 978-3-030-25827-6
  • Series Print ISSN 2523-3262
  • Series Online ISSN 2523-3270
  • Buy this book on publisher's site
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