Statistical Foundations of Actuarial Learning and its Applications
Statistical Foundations of Actuarial Learning and its Applications

Author: Mario V. Wüthrich

Publisher: Springer Nature

Published: 2022-11-22

Total Pages: 611

ISBN-13: 303112409X

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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.

Pricing in General Insurance
Pricing in General Insurance

Author: Pietro Parodi

Publisher: CRC Press

Published: 2023-05-02

Total Pages: 739

ISBN-13: 1000860795

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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.

Computational Actuarial Science with R
Computational Actuarial Science with R

Author: Arthur Charpentier

Publisher: CRC Press

Published: 2014-08-26

Total Pages: 652

ISBN-13: 1498759823

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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/

Fundamentals of Actuarial Mathematics
Fundamentals of Actuarial Mathematics

Author: S. David Promislow

Publisher: John Wiley & Sons

Published: 2011-01-06

Total Pages: 390

ISBN-13: 0470978074

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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.

Predictive Modeling Applications in Actuarial Science
Predictive Modeling Applications in Actuarial Science

Author: Edward W. Frees

Publisher: Cambridge University Press

Published: 2014-07-28

Total Pages: 565

ISBN-13: 1107029872

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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.

Market-Consistent Actuarial Valuation
Market-Consistent Actuarial Valuation

Author: Mario V. Wüthrich

Publisher: Springer Science & Business Media

Published: 2010-09-02

Total Pages: 164

ISBN-13: 3642148522

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It is a challenging task to read the balance sheet of an insurance company. This derives from the fact that different positions are often measured by different yardsticks. Assets, for example, are mostly valued at market prices whereas liabilities are often measured by established actuarial methods. However, there is a general agreement that the balance sheet of an insurance company should be measured in a consistent way. Market-Consistent Actuarial Valuation presents powerful methods to measure liabilities and assets in a consistent way. The mathematical framework that leads to market-consistent values for insurance liabilities is explained in detail by the authors. Topics covered are stochastic discounting with deflators, valuation portfolio in life and non-life insurance, probability distortions, asset and liability management, financial risks, insurance technical risks, and solvency.

Effective Statistical Learning Methods for Actuaries II
Effective Statistical Learning Methods for Actuaries II

Author: Michel Denuit

Publisher: Springer Nature

Published: 2020-11-16

Total Pages: 228

ISBN-13: 303057556X

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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.