Big Data for Insurance Companies-Wiley-ISTE(2018).pdf

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This book presents an overview of big data methods applied to insurance problems. Specifically, it is a multi-author book that gives a fairly complete view of five important aspects, each of which is presented by authors well known in the fields covered, who have complementary profiles and expertise (data scientists, actuaries, statisticians, engineers). These range from classical data analysis methods (including learning methods like machine learning) to the impact of big data on the present and future insurance market. Big data, megadata or massive data apply to datasets that are so vast that not only the popular data management methods but also the classical methods of statistics (for example, inference) lose their meaning or cannot apply. The exponential development of the power of computers linked to the crossroads of this data analysis with artificial intelligence helps us to initiate new analysis methods for gigantic databases that are mostly found in the insurance sector as presented in this book. The first chapter, written by Romain Billot, Cécile Bothorel and Philippe Lenca (IMT Atlantique, Brest), presents a sound introduction to big data and its application to insurance. This chapter focuses on the impact of megadata, showing that hundreds of millions of people generate billions of bytes of data each day. The classical characterization of big data by 5Vs is well illustrated and enriched by other Vs such as variability and validity. Introduction written by Marine CORLOSQUET-HABART and Jacques JANSSEN. xiv Big Data for Insurance Companies In order to remedy the insufficiency of classical data management techniques, the authors develop parallelization methods for data as well as possible tasks thanks to the development of computing via the parallelism of several computers. The main IT tools, including Hadoop, are presented as well as their relationship with platforms specialized in decision-making solutions and the problem of migrating to a given orien

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