Effective Statistical Learning Methods For Actuaries I
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Effective Statistical Learning Methods For Actuaries Iii
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2019-10-31
Effective Statistical Learning Methods For Actuaries Iii written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Business & Economics categories.
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
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2020-11-16
Effective Statistical Learning Methods For Actuaries Ii written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-16 with Business & Economics categories.
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.
Effective Statistical Learning Methods For Actuaries I
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Author : Michel Denuit
language : en
Publisher:
Release Date : 2019
Effective Statistical Learning Methods For Actuaries I written by Michel Denuit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Actuarial science categories.
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.
Effective Statistical Learning Methods For Actuaries I
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2019-09-03
Effective Statistical Learning Methods For Actuaries I written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-03 with Business & Economics categories.
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.
Effective Statistical Learning Methods For Actuaries
DOWNLOAD
Author : Michel Denuit
language : en
Publisher:
Release Date : 2019
Effective Statistical Learning Methods For Actuaries written by Michel Denuit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Actuarial science categories.
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.
Statistical Foundations Of Actuarial Learning And Its Applications
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Author : Mario V. Wüthrich
language : en
Publisher: Springer Nature
Release Date : 2022-11-22
Statistical Foundations Of Actuarial Learning And Its Applications written by Mario V. Wüthrich and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-22 with Mathematics categories.
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
Artificial Intelligence And Actuarial Science
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Author : Sonal Trivedi
language : en
Publisher: CRC Press
Release Date : 2024-12-27
Artificial Intelligence And Actuarial Science written by Sonal Trivedi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
This book aims to explore how to automate, innovate, design, and deploy emerging technologies in actuarial work transformations for the insurance and finance sector. It examines the role of artificial intelligence with process automation in daily monitoring of solvency, governance, compliance, data processes, etc. It also explores the usage of machine learning, telematics system, AI-enabled claim processing software, Big Data and Algorithms, Explainable AI, and AI-enabled risk management tools in various actuarial processes. This book: • Presents case studies and best practices with real-world examples of successful and unsuccessful actuarial work transformation initiatives and transformation with emerging technologies • Offers deployment solutions for different applications of AI in actuarial work • Discusses how organizations can effectively incorporate AI into their current practices of actuarial work • Covers diverse emerging technologies, practices, and processes of actuaries from around the globe • Elaborates upon a framework for comprehending how big data and AI developments may affect insurance offers and their supervision • Explains how insurance companies may review and modify their current Risk Management Framework (RMF) to take into account some of the significant differences while implementing AI use cases This reference book is for scholars, researchers and professionals interested in Artificial Intelligence and Actuarial Science.
Regression Modeling With Actuarial And Financial Applications
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Author : Edward W. Frees
language : en
Publisher: Cambridge University Press
Release Date : 2010
Regression Modeling With Actuarial And Financial Applications written by Edward W. Frees and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Machine Learning In Insurance
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Author : Jens Perch Nielsen
language : en
Publisher: MDPI
Release Date : 2020-12-02
Machine Learning In Insurance written by Jens Perch Nielsen and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-02 with Business & Economics categories.
Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.