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.
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.
Download or read book Insurance Biases Discrimination and Fairness written by Arthur Charpentier and published by Springer Nature. This book was released on with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Analysis and Applied Mathematics written by Allaberen Ashyralyev and published by Springer Nature. This book was released on with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pricing in General Insurance written by Pietro Parodi and published by CRC Press. This book was released on 2023-05-02 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the syllabus of the actuarial profession courses on general insurance pricing – with additional material inspired by the author’s own experience as a practitioner and lecturer – Pricing in General Insurance, Second Edition presents pricing as a formalised process that starts with collecting information about a particular policyholder or risk and ends with a commercially informed rate. The first edition of the book proved very popular among students and practitioners with its pragmatic approach, informal style, and wide-ranging selection of topics, including: Background and context for pricing Process of experience rating, ranging from traditional approaches (burning cost analysis) to more modern approaches (stochastic modelling) Exposure rating for both property and casualty products Specialised techniques for personal lines (e.g., GLMs), reinsurance, and specific products such as credit risk and weather derivatives General-purpose techniques such as credibility, multi-line pricing, and insurance optimisation The second edition is a substantial update on the first edition, including: New chapter on pricing models: their structure, development, calibration, and maintenance New chapter on rate change calculations and the pricing cycle Substantially enhanced treatment of exposure rating, increased limit factors, burning cost analysis Expanded treatment of triangle-free techniques for claim count development Improved treatment of premium building and capital allocation Expanded treatment of machine learning Enriched treatment of rating factor selection, and the inclusion of generalised additive models The book delivers a practical introduction to all aspects of general insurance pricing and is aimed at students of general insurance and actuarial science as well as practitioners in the field. It is complemented by online material, such as spreadsheets which implement the techniques described in the book, solutions to problems, a glossary, and other appendices – increasing the practical value of the book.
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.
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.
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.
Download or read book Financial and Actuarial Statistics written by Dale S. Borowiak and published by CRC Press. This book was released on 2013-11-12 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand Up-to-Date Statistical Techniques for Financial and Actuarial ApplicationsSince the first edition was published, statistical techniques, such as reliability measurement, simulation, regression, and Markov chain modeling, have become more prominent in the financial and actuarial industries. Consequently, practitioners and students must ac
Download or read book Regression Modeling with Actuarial and Financial Applications written by Edward W. Frees and published by Cambridge University Press. This book was released on 2010 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
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.
Download or read book Predictive Modeling Applications in Actuarial Science Volume 2 Case Studies in Insurance written by Edward W. Frees and published by Cambridge University Press. This book was released on 2016-07-27 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.
Download or read book Fundamentals of Actuarial Mathematics written by S. David Promislow and published by John Wiley & Sons. This book was released on 2011-01-06 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to actuarial mathematics, covering both deterministic and stochastic models of life contingencies, as well as more advanced topics such as risk theory, credibility theory and multi-state models. This new edition includes additional material on credibility theory, continuous time multi-state models, more complex types of contingent insurances, flexible contracts such as universal life, the risk measures VaR and TVaR. Key Features: Covers much of the syllabus material on the modeling examinations of the Society of Actuaries, Canadian Institute of Actuaries and the Casualty Actuarial Society. (SOA-CIA exams MLC and C, CSA exams 3L and 4.) Extensively revised and updated with new material. Orders the topics specifically to facilitate learning. Provides a streamlined approach to actuarial notation. Employs modern computational methods. Contains a variety of exercises, both computational and theoretical, together with answers, enabling use for self-study. An ideal text for students planning for a professional career as actuaries, providing a solid preparation for the modeling examinations of the major North American actuarial associations. Furthermore, this book is highly suitable reference for those wanting a sound introduction to the subject, and for those working in insurance, annuities and pensions.
Download or read book Predictive Modeling Applications in Actuarial Science Volume 2 Case Studies in Insurance written by Edward W. Frees and published by Cambridge University Press. This book was released on 2016-07-27 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.
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/
Download or read book Predictive Modeling Applications in Actuarial Science Volume 1 Predictive Modeling Techniques 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: Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.
Download or read book Fundamentals of Actuarial Mathematics written by S. David Promislow and published by Wiley. This book was released on 2006-02-10 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Actuarial work is the application of mathematics and statistics to the analysis of financial problems in life insurance, pensions, general insurance and investments. This unique introduction to the topic employs both a deterministic and stochastic treatment of the subject. It combines interest theory and life contingencies in a unified manner as well as covering basic risk theory. Fundamentals of Actuarial Mathematics presents the concepts in an original, accessible style, assuming a minimal formal background. * Provides a complete review of necessary probability theory. * Covers the Society of Actuaries' syllabus on Actuarial Models. * Orders the topics specifically to facilitate learning, beginning with the simplest case of the deterministic discrete model, and then moving to the more complicated stochastic, continuous models. * Employs modern calculation and computing techniques, such as spreadsheets. * Contains a variety of exercises, both computational and theoretical. * Supported by a website featuring exercises and further examples. * Written by a highly respected academic with over 35 years teaching experience. This book will be invaluable to senior undergraduate and graduate students, as well as actuarial professionals working in the life insurance or pension fields. Applied mathematicians and economists will also benefit greatly from the clear presentation and numerous examples.
