This textbook provides a broad overview of the present state of insurance mathematics and some related topics in risk management, financial mathematics and probability. Both non-life and life aspects are covered. The emphasis is on probability and modeling rather than statistics and practical implementation. Aimed at the graduate level, pointing in part to current research topics, it can potentially replace other textbooks on basic non-life insurance mathematics and advanced risk management methods in non-life insurance. Based on chapters selected according to the particular topics in mind, the book may serve as a source for introductory courses to insurance mathematics for non-specialists, advanced courses for actuarial students, or courses on probabilistic aspects of risk. It will also be useful for practitioners and students/researchers in related areas such as finance and statistics who wish to get an overview of the general area of mathematical modeling and analysis in insurance.

The aim of the book is to provide an overview of risk management in life insurance companies. The focus is twofold: (1) to provide a broad view of the different topics needed for risk management and (2) to provide the necessary tools and techniques to concretely apply them in practice. Much emphasis has been put into the presentation of the book so that it presents the theory in a simple but sound manner. The first chapters deal with valuation concepts which are defined and analysed, the emphasis is on understanding the risks in corresponding assets and liabilities such as bonds, shares and also insurance liabilities. In the following chapters risk appetite and key insurance processes and their risks are presented and analysed. This more general treatment is followed by chapters describing asset risks, insurance risks and operational risks - the application of models and reporting of the corresponding risks is central. Next, the risks of insurance companies and of special insurance products are looked at. The aim is to show the intrinsic risks in some particular products and the way they can be analysed. The book finishes with emerging risks and risk management from a regulatory point of view, the standard model of Solvency II and the Swiss Solvency Test are analysed and explained. The book has several mathematical appendices which deal with the basic mathematical tools, e.g. probability theory, stochastic processes, Markov chains and a stochastic life insurance model based on Markov chains. Moreover, the appendices look at the mathematical formulation of abstract valuation concepts such as replicating portfolios, state space deflators, arbitrage free pricing and the valuation of unit linked products with guarantees. The various concepts in the book are supported by tables and figures.

This classic, comprehensive book is divided into three sections. The first section examines the concept of risk, the nature of the insurance device, and the principles of risk management. This section also provides an overview of the insurance industry. The second section examines the traditional fields of life and health insurance as solutions to the risks connected with the loss of income. The Social Security system, workers compensation, and other social insurance coverages are discussed. The final section deals with the risks associated with the ownership of property and legal liability. Updated to reflect the changes in the field of insurance since 1996, and a listing of Web sites of interest.

This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.

Orio Giarini The "Geneva Association" (International Association for the Study of Risk and Insurance Economics) was founded in 1973. The main goal was to stimulate and organize objective research in the field of risk, uncertainty, and insurance, in a world in which such issues were clearly becoming of greater and greater relevance for all economic actors. This was a pioneer ing effort, especially as economic theory and the teaching of economics were still anchored to the key notion of general equilibrium under an assumption of certainty. Thus, we had to start our work almost from scratch. One of the first initiatives was to bring together in Geneva, in June of 1973, all the academics in Europe already involved in risk and insurance economics. We found eight from five different countries who never had met before. This seminar chaired by Raymond Barre, the first president of The Geneva Association, was the first of an annual series that became known as the seminar of "The European Group of Risk and Insurance Economists." Since then more than 100 economists from most European countries as well as participants from two other continents and in particular from the United States have taken part in this seminar.

An updated review of the theories and applications of corporate risk management After the financial crisis of 2008, issues concerning corporate risk management arose that demand new levels of oversight. Corporate Risk Management is an important guide to the topic that puts the focus on the corporate finance dimension of risk management. The author—a noted expert on the topic—presents several theoretical models appropriate for various industries and empirically verifies theoretical propositions. The book also proposes statistical modeling that can evaluate the importance of different risks and their variations according to economic cycles. The book provides an analysis of default, liquidity, and operational risks as well as the failures of LTCM, ENRON, and financial institutions that occurred during the financial crisis. The author also explores Conditional Value at Risk (CVaR), which is central to the debate on the measurement of market risk under Basel III. This important book: Includes a comprehensive review of the aspects of corporate risk management Presents statistical modeling that addresses recent risk management issues Contains an analysis of risk management failures that lead to the 2008 financial crisis Offers a must-have resource from author Georges Dionne the former editor of The Journal of Risk and Insurance Corporate Risk Management provides a modern empirical analysis of corporate risk management across industries. It is designed for use by risk management professionals, academics, and graduate students.