Artificial Intelligence And Actuarial Science

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Artificial Intelligence and Actuarial Science

Author : Sonal Trivedi
Publisher :
ISBN 13 : 9781032751337
Total Pages : 0 pages
Book Rating : 4.7/5 (513 download)

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Book Synopsis Artificial Intelligence and Actuarial Science by : Sonal Trivedi

Download or read book Artificial Intelligence and Actuarial Science written by Sonal Trivedi and published by . This book was released on 2024-12-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 solvency, governance, compliance, data processes, etc. It also explores the usage of Machine learning, telematics system, AI- AI-enabled claim processing software, Big Data and Algorithms, Explainable AI, AI-enabled risk management tools in various actuarial processes. - 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.

Effective Statistical Learning Methods for Actuaries III

Author : Michel Denuit
Publisher : Springer Nature
ISBN 13 : 3030258270
Total Pages : 250 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Effective Statistical Learning Methods for Actuaries III by : Michel Denuit

Download or read book Effective Statistical Learning Methods for Actuaries III written by Michel Denuit and published by Springer Nature. This book was released on 2019-10-31 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

AI in Actuarial Science

Author : Ronald Richman
Publisher :
ISBN 13 :
Total Pages : 53 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis AI in Actuarial Science by : Ronald Richman

Download or read book AI in Actuarial Science written by Ronald Richman and published by . This book was released on 2018 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid advances in Artificial Intelligence and Machine Learning are creating products and services with the potential not only to change the environment in which actuaries operate, but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning”. This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. After providing some background on machine learning and deep learning, and providing a heuristic for where actuaries might benefit from applying these techniques, the paper surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. The paper concludes with an outlook on the potential for actuaries to integrate deep learning into their activities.

Predictive Modeling Applications in Actuarial Science

Author : Edward W. Frees
Publisher : Cambridge University Press
ISBN 13 : 1107029872
Total Pages : 565 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Predictive Modeling Applications in Actuarial Science by : Edward W. Frees

Download or read book Predictive Modeling Applications in Actuarial Science written by Edward W. Frees and published by Cambridge University Press. This book was released on 2014-07-28 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

Machine Learning in Insurance

Author : Jens Perch Nielsen
Publisher : MDPI
ISBN 13 : 3039364472
Total Pages : 260 pages
Book Rating : 4.0/5 (393 download)

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Book Synopsis Machine Learning in Insurance by : Jens Perch Nielsen

Download or read book Machine Learning in Insurance written by Jens Perch Nielsen and published by MDPI. This book was released on 2020-12-02 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Hire Purpose

Author : Deanna Mulligan
Publisher : Columbia University Press
ISBN 13 : 0231553129
Total Pages : 307 pages
Book Rating : 4.2/5 (315 download)

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Book Synopsis Hire Purpose by : Deanna Mulligan

Download or read book Hire Purpose written by Deanna Mulligan and published by Columbia University Press. This book was released on 2020-10-13 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: A WALL STREET JOURNAL BUSINESS BESTSELLER The future of work is already here, and what this future looks like must be a pressing concern for the current generation of leaders in both the private and public sectors. In the next ten to fifteen years, rapid change in a post-pandemic world and emerging technology will revolutionize nearly every job, eliminate some, and create new forms of work that we have yet to imagine. How can we survive and thrive in the face of such drastic change? Deanna Mulligan offers a practical, broad-minded look at the effects of workplace evolution and automation and why the private sector needs to lead the charge in shaping a values-based response. With a focus on the power of education, Mulligan proposes that the solutions to workforce upheaval lie in reskilling and retraining for individuals and companies adapting to rapid change. By creating lifelong learning opportunities that break down boundaries between the classroom and the workplace, businesses can foster personal and career well-being and growth for their employees. Drawing on her own experiences, historical examples, and reports from the frontiers where these issues are unfolding, Mulligan details how business leaders can prepare for and respond to technological disruption. Providing a framework for concrete and meaningful action, Hire Purpose is an essential read about the transformations that will shape the next decade and beyond.

Effective Statistical Learning Methods for Actuaries II

Author : Michel Denuit
Publisher : Springer Nature
ISBN 13 : 303057556X
Total Pages : 228 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Effective Statistical Learning Methods for Actuaries II by : Michel Denuit

Download or read book Effective Statistical Learning Methods for Actuaries II written by Michel Denuit and published by Springer Nature. This book was released on 2020-11-16 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Foundations of Actuarial Learning and its Applications

Author : Mario V. Wüthrich
Publisher : Springer Nature
ISBN 13 : 303112409X
Total Pages : 611 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Statistical Foundations of Actuarial Learning and its Applications by : Mario V. Wüthrich

Download or read book Statistical Foundations of Actuarial Learning and its Applications written by Mario V. Wüthrich and published by Springer Nature. This book was released on 2022-11-22 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Effective Statistical Learning Methods for Actuaries I

Author : Michel Denuit
Publisher :
ISBN 13 : 9783030258214
Total Pages : 441 pages
Book Rating : 4.2/5 (582 download)

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Book Synopsis Effective Statistical Learning Methods for Actuaries I by : Michel Denuit

Download or read book Effective Statistical Learning Methods for Actuaries I written by Michel Denuit and published by . This book was released on 2019 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Michel Denuit
Publisher :
ISBN 13 : 9783030258283
Total Pages : pages
Book Rating : 4.2/5 (582 download)

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Book Synopsis Effective Statistical Learning Methods for Actuaries by : Michel Denuit

Download or read book Effective Statistical Learning Methods for Actuaries written by Michel Denuit and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence in Education

Author : Rosemary Luckin
Publisher : IOS Press
ISBN 13 : 1586037641
Total Pages : 764 pages
Book Rating : 4.5/5 (86 download)

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Book Synopsis Artificial Intelligence in Education by : Rosemary Luckin

Download or read book Artificial Intelligence in Education written by Rosemary Luckin and published by IOS Press. This book was released on 2007 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nature of technology has changed since Artificial Intelligence in Education (AIED) was conceptualized as a research community and Interactive Learning Environments were initially developed.

Computational Actuarial Science with R

Author : Arthur Charpentier
Publisher : CRC Press
ISBN 13 : 1498759823
Total Pages : 652 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Actuarial Science with R by : Arthur Charpentier

Download or read book Computational Actuarial Science with R written by Arthur Charpentier and published by CRC Press. This book was released on 2014-08-26 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Hands-On Approach to Understanding and Using Actuarial ModelsComputational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/

Effective Statistical Learning Methods for Actuaries I

Author : Michel Denuit
Publisher : Springer Nature
ISBN 13 : 3030258203
Total Pages : 441 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Effective Statistical Learning Methods for Actuaries I by : Michel Denuit

Download or read book Effective Statistical Learning Methods for Actuaries I written by Michel Denuit and published by Springer Nature. This book was released on 2019-09-03 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author : Marco Corazza
Publisher : Springer Nature
ISBN 13 : 3030996387
Total Pages : 456 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Mathematical and Statistical Methods for Actuarial Sciences and Finance by : Marco Corazza

Download or read book Mathematical and Statistical Methods for Actuarial Sciences and Finance written by Marco Corazza and published by Springer Nature. This book was released on 2022-04-11 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The cooperation and contamination among mathematicians, statisticians and econometricians working in actuarial sciences and finance are improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas in the form of four- to six-page papers presented at the International Conference MAF2022 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the COVID-19 pandemic, the conference, to which this book is related, was organized in a hybrid form by the Department of Economics and Statistics of the University of Salerno, with the partnership of the Department of Economics of Cà Foscari University of Venice, and was held from 20 to 22 April 2022 in Salerno (Italy) MAF2022 is the tenth edition of an international biennial series of scientific meetings, started in 2004 on the initiative of the Department of Economics and Statistics of the University of Salerno. It has established itself internationally with gradual and continuous growth and scientific enrichment. The effectiveness of this idea has been proven by the wide participation in all the editions, which have been held in Salerno (2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020 online), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioural finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.

Healthcare Risk Adjustment and Predictive Modeling

Author : Ian G. Duncan
Publisher : ACTEX Publications
ISBN 13 : 1566987695
Total Pages : 350 pages
Book Rating : 4.5/5 (669 download)

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Book Synopsis Healthcare Risk Adjustment and Predictive Modeling by : Ian G. Duncan

Download or read book Healthcare Risk Adjustment and Predictive Modeling written by Ian G. Duncan and published by ACTEX Publications. This book was released on 2011 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.

Data Science and Machine Learning in Insurance. A Gentle Introduction for Actuaries

Author : Marco Aleandri
Publisher :
ISBN 13 : 9788825528657
Total Pages : 276 pages
Book Rating : 4.5/5 (286 download)

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Book Synopsis Data Science and Machine Learning in Insurance. A Gentle Introduction for Actuaries by : Marco Aleandri

Download or read book Data Science and Machine Learning in Insurance. A Gentle Introduction for Actuaries written by Marco Aleandri and published by . This book was released on 2019 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author : Marco Corazza
Publisher : Springer Nature
ISBN 13 : 3030789659
Total Pages : 389 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Mathematical and Statistical Methods for Actuarial Sciences and Finance by : Marco Corazza

Download or read book Mathematical and Statistical Methods for Actuarial Sciences and Finance written by Marco Corazza and published by Springer Nature. This book was released on 2021-12-13 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page papers, presented at the International Conference eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca’ Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.