Author: Marco AleandriPublisher:
ISBN: 9788825528657
Category : Mathematics
Languages : en
Pages : 276
Book Description
Data Science and Machine Learning in Insurance. A Gentle Introduction for Actuaries
Author: Marco AleandriPublisher:
ISBN: 9788825528657
Category : Mathematics
Languages : en
Pages : 276
Book Description
Effective Statistical Learning Methods for Actuaries III
Author: Michel DenuitPublisher: Springer Nature
ISBN: 3030258270
Category : Business & Economics
Languages : en
Pages : 250
Book Description
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.
Effective Statistical Learning Methods for Actuaries II
Author: Michel DenuitPublisher: Springer Nature
ISBN: 303057556X
Category : Business & Economics
Languages : en
Pages : 228
Book Description
This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second 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.
Insurance Data Analytics
Author: Effective Statistical Learning Methods for Actuaries I
Author: Michel DenuitPublisher: Springer Nature
ISBN: 3030258203
Category : Business & Economics
Languages : en
Pages : 441
Book Description
This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first 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.
Big Data for Insurance Companies
Author: Marine Corlosquet-HabartPublisher: John Wiley & Sons
ISBN: 1119489296
Category : Business & Economics
Languages : en
Pages : 139
Book Description
This book will be a "must" for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.
Insurance, Biases, Discrimination and Fairness
Author: Arthur CharpentierPublisher: Springer Nature
ISBN: 303149783X
Category :
Languages : en
Pages : 491
Book Description
Effective Statistical Learning Methods for Actuaries I
Author: Michel DenuitPublisher:
ISBN: 9783030258214
Category : Actuarial science
Languages : en
Pages : 441
Book Description
This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first 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.
Introduction to Actuarial Science (Classic Reprint)
Author: Harry Anson FinneyPublisher:
ISBN: 9781332733699
Category : Business & Economics
Languages : en
Pages : 114
Book Description
Excerpt from Introduction to Actuarial Science In the more comprehensive meaning Of the term, actuarial science includes an expert knowl edge Of the principles of compound interest as well as the laws Of insurance probabilities. Pub lic accountants, however, are usually interested only in the interest phases of actuarial science, leaving the application Of the laws of insurance probabilities to the actuary, who ascertains the measurement Of risks and establishes tables of rates. This discussion of actuarial science will, therefore, be -restricted to the phases thereof which deal with compound interest. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Effective Statistical Learning Methods for Actuaries
Author: Michel DenuitPublisher:
ISBN: 9783030258283
Category : Actuarial science
Languages : en
Pages :
Book Description
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.