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Insurance data analytics : some case studies of advanced algorithms and applications
The use of algorithms exploiting heterogeneous and often high-volume data has developed very rapidly in recent years, taking advantage of increasing computing capacity and data collected by GAFA. These techniques first appeared in the insurance world to meet management or marketing needs : dimension...
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| Auteurs principaux : | , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Insurance data analytics : some case studies of advanced algorithms and applications / Frédéric Planchet, Christian Y. Robert (editors) |
| Publié : |
Paris :
Economica
, DL 2020 |
| Description matérielle : | 1 volume (407 pages) |
| Collection : | Assurance, audit, actuariat |
| Sujets : |
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| 200 | 1 | |a Insurance data analytics |e some case studies of advanced algorithms and applications |f Frédéric Planchet, Christian Y. Robert (editors) | |
| 214 | 0 | |a Paris |c Economica |d DL 2020 | |
| 215 | |a 1 volume (407 pages) |c llustrations en noir et blanc, couverture illustrée en couleurs |d 24 cm | ||
| 225 | |a Assurance. Audit. Actuariat | ||
| 339 | |a Des études de cas illustrant les techniques d'analyse et de quantification des risques issues de la science des données dans le domaine des assurances. ©Electre 2020 | ||
| 320 | |a Notes bibliographiques | ||
| 330 | |a The use of algorithms exploiting heterogeneous and often high-volume data has developed very rapidly in recent years, taking advantage of increasing computing capacity and data collected by GAFA. These techniques first appeared in the insurance world to meet management or marketing needs : dimensioning of call centers, customer selection, automated analysis of contractual clauses, automation of underwriting processes, etc. Even if a few old attempts can be spotted, it is only recently that actuaries have started to integrate data science techniques more systematically into their toolbox and have started to identify issues where these approaches could prove to be more efficient than the usual approaches. In this book, at the crossroads of actuarial and data science, you will find a state of the art of the use of these techniques for risk analysis and quantification. Intended for students, academics and practitioners, it aims to provide a working basis and lines of thought for an enlightened use of data science in actuarial science |2 4e de couverture | ||
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