Effective Statistical Learning Methods for Actuaries III

Neural Networks and Extensions

By: Michel Denuit, Donatien Hainaut, Julien Trufin

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Paperback | 13 November 2019

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266 Pages

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23.5 x 15.88 x 1.27

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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.

Industry Reviews

"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)

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