This book will be a "must" for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.

The 2008 financial collapse, the expansion of corporate and private wealth, the influence of money in politics-many of Wall Street's contemporary trends can be traced back to the work of fourteen critical figures who wrote, and occasionally broke, the rules of American finance. Edward Morris plots in absorbing detail Wall Street's transformation from a clubby enclave of financiers to a symbol of vast economic power. His book begins with J. Pierpont Morgan, who ruled the American banking system at the turn of the twentieth century, and ends with Sandy Weill, whose collapsing Citigroup required the largest taxpayer bailout in history. In between, Wall Streeters relates the triumphs and missteps of twelve other financial visionaries. From Charles Merrill, who founded Merrill Lynch and introduced the small investor to the American stock market; to Michael Milken, the so-called junk bond king; to Jack Bogle, whose index funds redefined the mutual fund business; to Myron Scholes, who laid the groundwork for derivative securities; and to Benjamin Graham, who wrote the book on securities analysis. Anyone interested in the modern institution of American finance will devour this history of some of its most important players.

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.

In recent years, the damage caused by natural disasters has increased worldwide; this trend will only continue with the impact of climate change. Despite this, the role for the most common mechanism for managing risk - insurance - has received little attention. This book considers the contribution that insurance arrangements can make to society's management of the risks of natural hazards in a changing climate. It also looks at the potential impacts of climate change on the insurance sector, and insurers' responses to climate change. The author combines theory with evidence from the rich experiences of the Netherlands together with examples from around the world. He recognises the role of the individual in preparing for disasters, as well as the difficulties individuals have in understanding and dealing with infrequent risks. Written in plain language, this book will appeal to researchers and policy-makers alike.

Mortality improvements, uncertainty in future mortality trends and the relevant impact on life annuities and pension plans constitute important topics in the field of actuarial mathematics and life insurance techniques. In particular, actuarial calculations concerning pensions, life annuities and other living benefits (provided, for example, by long-term care insurance products and whole life sickness covers) are based on survival probabilities which necessarily extend over a long time horizon. In order to avoid underestimation of the related liabilities, the insurance company (or the pension plan) must adopt an appropriate forecast of future mortality.
Great attention is currently being devoted to the management of life annuity portfolios, both from a theoretical and a practical point of view, because of the growing importance of annuity benefits paid by private pension schemes. In particular, the progressive shift from defined benefit to defined contribution pension schemes has increased the interest in life annuities with a guaranteed annual amount.
This book provides a comprehensive and detailed description of methods for projecting mortality, and an extensive introduction to some important issues concerning longevity risk in the area of life annuities and pension benefits. It relies on research work carried out by the authors, as well as on a wide teaching experience and in CPD (Continuing Professional Development) initiatives. The following topics are dealt with: life annuities in the framework of post-retirement income strategies; the basic mortality model; recent mortality trends that have been experienced; general features of projection models; discussion of stochastic projection models, with numerical illustrations; measuring and managing longevity risk.

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.

This book, the second one of three volumes, gives practical examples by a number of use cases showing how to take first steps in the digital journey of banks and insurance companies. The angle shifts over the volumes from a business-driven approach in "Disruption and DNA" to a strong technical focus in "Data Storage, Processing and Analysis", leaving "Digitalization and Machine Learning Applications" with the business and technical aspects in-between. This second volume mainly emphasizes use cases as well as the methods and technologies applied to drive digital transformation (such as processes, leveraging computational power and machine learning models).

This book sets out in a clear and concise manner the central principles of insurance law in the Caribbean, guiding students through the complexities of the subject. This book features, among several other key themes, extensive coverage of: insurance regulation; life insurance; property insurance; contract formation; intermediaries; the claims procedure; and analysis of the substantive laws of several jurisdictions. Commonwealth Caribbean Insurance Law is essential reading for LLB students in Caribbean universities, students in CAPE Law courses, and practitioners.

This book encourages insurance companies and regulators to explore offering Islamic insurance to boost the insurance industry in India. The distinctive features of Takaful also make it appealing even to non-Muslims. According to the 2012 World Takaful Report, India has immense potential for Takaful is based on the size of its Muslim population and the growth of its economy. However, it is surprising that Takaful has yet to be introduced in India since it has been offered in non-majority Muslim countries, such as Singapore, Thailand, and Sri Lanka. When the concept and practice of Takaful are examined, it is free from interest, uncertainty, and gambling. These are the main elements prohibited in Islam. However, it has been evidenced that these elements are also banned in teaching other religions believed by the Indians. Given this landscape, this book fills the gap in research on the viability of Takaful in India, focusing on its empirical aspects by examining the perception of Indian insurance operators toward Takaful.

Insurance Economics brings together the economic analysis of decision making under risk, risk management and demand for insurance among individuals and corporations, objectives pursued and management tools used by insurance companies, the regulation of insurance, and the division of labor between private and social insurance. Appropriate both for advanced undergraduate and graduate students of economics, management, and finance, this text provides the background required to understand current research. Predictions derived from theoretical arguments are not merely stated, but also related to empirical evidence. Throughout the book, conclusions summarize key results, helping readers to check their knowledge and comprehension. Issues discussed include paradoxes in decision making under risk and attempts at their resolution, moral hazard and adverse selection including the possibility of a "death spiral", and future challenges to both private and social insurance such as globalization and the availability of genetic information. This second edition has been extensively revised. Most importantly, substantial content has been added to represent the evolution of risk-related research. A new chapter, Insurance Demand II: Nontraditional Approaches, provides a timely addition in view of recent developments in risk theory and insurance. Previous discussions of Enterprise Risk Management, long-term care insurance, adverse selection, and moral hazard have all been updated. In an effort to expand the global reach of the text, evidence and research from the U.S. and China have also been added.

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 practicing analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. 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 by analysts to gain a competitive advantage in situations with complex data."

Statistical and Probabilistic Methods in Actuarial Science covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. The book builds on students' existing knowledge of probability and statistics by establishing a solid and thorough understanding of these methods. It also emphasizes the wide variety of practical situations in insurance and actuarial science where these techniques may be used.

Although some chapters are linked, several can be studied independently from the others. The first chapter introduces claims reserving via the deterministic chain ladder technique. The next few chapters survey loss distributions, risk models in a fixed period of time, and surplus processes, followed by an examination of credibility theory in which collateral and sample information are brought together to provide reasonable methods of estimation. In the subsequent chapter, experience rating via no claim discount schemes for motor insurance provides an interesting application of Markov chain methods. The final chapters discuss generalized linear models and decision and game theory.

Developed by an author with many years of teaching experience, this text presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.

The market is like the sea: it gives, and it takes away. That became apparent once again when the economy and society went into "lockdown" due to the coronavirus outbreak. Organizations will either sink or swim, and only the pros will be able to keep their heads above water. This is a self-help book for managers, supervisors and administrators who see themselves as skippers at the helm of an organization in times of turbulence, uncertainty and complexity. It provides a number of the latest handy management models, such as the Three-Phase Model, Governance Model and Management Matrix, which help leaders and managers arrive at well thought-out risk management decisions. In addition, the practical cases and discussion questions in each chapter help readers implement these models in their organizations. The book is an English translation of the Dutch book 'Varen in de mist', which was nominated for the Dutch Management Book of the Year.

Insurance is an extraordinarily useful tool to manage risk. When it works as intended, it provides financial protection to individuals and a profitable business model for insurance firms and their investors. But it is broadly misunderstood by consumers, regulators, and insurance executives. This book looks at the behavior of individuals at risk, insurance industry decision makers, and policy makers at the local, state, and federal level involved in the selling, buying, and regulating of insurance. It compares their actions to those predicted by benchmark models of choice derived from classical economic theory. When actual choices stray from predictions, the behavior is considered to be anomalous. With considerable sums of money at stake, both in consumer premiums and insurance company payouts, it is important to understand the reasons for anomalous behavior. Howard Kunreuther, Mark Pauly, and Stacey McMorrow examine these anomalies through the lens of behavioral economics, which takes into account emotions, biases, and simplified decision rules. The authors then consider if and how such behavioral anomalies could be modified to improve individual and social welfare. This book is neither a defense of the insurance industry nor an attack on it. Neither is it a consumer guide to purchasing insurance, although the authors believe that consumers will benefit from the insights it contains. Rather, this book describes situations in which both public policy and the insurance industry s collective posture need to change. This may require incentives, rules, and institutions to help reduce both inefficient and anomalous behavior, thereby encouraging behavior that will improve individual and social welfare."

This book examines the challenges for the life insurance sector in Europe arising from new technologies, socio-cultural and demographic trends, and the financial crisis. It presents theoretical and applied research in all areas related to life insurance products and markets, and explores future determinants of the insurance industry's development by highlighting novel solutions in insurance supervision and trends in consumer protection. Drawing on their academic and practical expertise, the contributors identify problems relating to risk analysis and evaluation, demographic challenges, consumer protection, product distribution, mortality risk modeling, applications of life insurance in contemporary pension systems, financial stability and solvency of life insurers. They also examine the impact of population aging on life insurance markets and the role of digitalization. Lastly, based on an analysis of early experiences with the implementation of the Solvency II system, the book provides policy recommendations for the development of life insurance in Europe.

This book begins with the fundamental large sample theory, estimating ruin probability, and ends by dealing with the latest issues of estimating the Gerber-Shiu function. This book is the first to introduce the recent development of statistical methodologies in risk theory (ruin theory) as well as their mathematical validities. Asymptotic theory of parametric and nonparametric inference for the ruin-related quantities is discussed under the setting of not only classical compound Poisson risk processes (Cramer-Lundberg model) but also more general Levy insurance risk processes. The recent development of risk theory can deal with many kinds of ruin-related quantities: the probability of ruin as well as Gerber-Shiu's discounted penalty function, both of which are useful in insurance risk management and in financial credit risk analysis. In those areas, the common stochastic models are used in the context of the structural approach of companies' default. So far, the probabilistic point of view has been the main concern for academic researchers. However, this book emphasizes the statistical point of view because identifying the risk model is always necessary and is crucial in the final step of practical risk management.

Modern risk management as practiced today faces significant obstacles-we argue-primarily due to the fundamental premise of the concept itself. It asserts that we are mainly dealing with measurable, quantifiable risks and that we can manage the uncontrollable by relying on formal control-based systems, which has produced a general view that (enterprise) risk management is a technical-scientific discipline. Strategic Risk Leadership offers a critique of the status quo, and encourages leaders, executives, and chief risk officers to find fresh approaches that can help them deal more proactively with what the future may hold. The book provides an overview of the history of risk management and current risk governance approaches as prescribed by leading risk management standards, such as COSO and ISO31000. This enables practitioners to challenge the frameworks and improve their adoption in practice introducing sustainable resilience as a (more) meaningful response to uncertain and unknowable conditions. The book shows how traditional thinking downplays the significance of human behavior and judgmental biases as key elements of major organizational exposures illustrated and explained through numerous case examples and studies. This book is essential reading for strategic risk managers to understand the requirements for effective risk governance practices in the contemporary and rapidly changing global risk landscape. Indeed, it is a valuable resource for all risk executives, leaders, and chief risk officers, as well as advanced students of risk management.

This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing in the presence of low probability events, group testing, compositional data analysis for detecting data inconsistencies, behaviouristic aspects in fighting a pandemic, and insurers' legal problems, amongst others. Concluding with an essay by a practicing actuary on the applicability of the methods proposed, this interdisciplinary book is aimed at actuaries as well as readers with a background in mathematics, economics, statistics, finance, epidemiology, or sociology.

This book presents a consistent and complete framework for studying the risk management of a pension fund. It gives the reader the opportunity to understand, replicate and widen the analysis. To this aim, the book provides all the tools for computing the optimal asset allocation in a dynamic framework where the financial horizon is stochastic (longevity risk) and the investor's wealth is not self-financed. This tutorial enables the reader to replicate all the results presented. The R codes are provided alongside the presentation of the theoretical framework. The book explains and discusses the problem of hedging longevity risk even in an incomplete market, though strong theoretical results about an incomplete framework are still lacking and the problem is still being discussed in most recent literature.

This Volume of the AIDA Europe Research Series on Insurance Law and Regulation focuses on transparency as the guiding principle of modern insurance law. It consists of chapters written by leaders in the respective field, who address transparency in a range of civil and common law jurisdictions, along with overview chapters. Each chapter reviews the transparency principles applicable in the jurisdiction discussed. Whether expressly or impliedly, all jurisdictions recognize a duty on the part of the insured to make a fair presentation of the risk when submitting a proposal for cover to the insurers, although there is little consensus on the scope of that duty. Disputed matters in this regard include: whether it is satisfied by honest answers to express questions, or whether there is a spontaneous duty of disclosure; whether facts relating to the insured's character, as opposed to the nature of the risk itself, are to be presented to the insurers; the role of insurance intermediaries in the placement process; and the remedy for breach of duty. Transparency is, however, a much wider concept. Potential policyholders are in principle entitled to be made aware of the key terms of coverage and to be warned of hidden traps (such as conditions precedent, average clauses and excess provisions), but there are a range of different approaches. Some jurisdictions have adopted a "soft law" approach, using codes of practice for pre-contract disclosure, while other jurisdictions employ the rather nebulous duty of (utmost) good faith. Leaving aside placement, transparency is also demanded after the policy has been incepted. The insured is required to be transparent during the claims process. There is less consistency in national legislation regarding the implementation of transparency by insurers in the context of handling claims.

This book is devoted to the mathematical methods of metamodeling that can be used to speed up the valuation of large portfolios of variable annuities. It is suitable for advanced undergraduate students, graduate students, and practitioners. It is the goal of this book to describe the computational problems and present the metamodeling approaches in a way that can be accessible to advanced undergraduate students and practitioners. To that end, the book will not only describe the theory of these mathematical approaches, but also present the implementations.

Whether man-made or naturally occurring, large-scale disasters can cause fatalities and injuries, devastate property and communities, savage the environment, impose significant financial burdens on individuals and firms, and test political leadership. Moreover, global challenges such as climate change and terrorism reveal the interdependent and interconnected nature of our current moment: what occurs in one nation or geographical region is likely to have effects across the globe. Our information age creates new and more integrated forms of communication that incur risks that are difficult to evaluate, let alone anticipate. All of this makes clear that innovative approaches to assessing and managing risk are urgently required. When catastrophic risk management was in its inception thirty years ago, scientists and engineers would provide estimates of the probability of specific types of accidents and their potential consequences. Economists would then propose risk management policies based on those experts' estimates with little thought as to how this data would be used by interested parties. Today, however, the disciplines of finance, geography, history, insurance, marketing, political science, sociology, and the decision sciences combine scientific knowledge on risk assessment with a better appreciation for the importance of improving individual and collective decision-making processes. The essays in this volume highlight past research, recent discoveries, and open questions written by leading thinkers in risk management and behavioral sciences. The Future of Risk Management provides scholars, businesses, civil servants, and the concerned public tools for making more informed decisions and developing long-term strategies for reducing future losses from potentially catastrophic events. Contributors: Mona Ahmadiani, Joshua D. Baker, W. J. Wouter Botzen, Cary Coglianese, Gregory Colson, Jeffrey Czajkowski, Nate Dieckmann, Robin Dillon, Baruch Fischhoff, Jeffrey A. Friedman, Robin Gregory, Robert W. Klein, Carolyn Kousky, Howard Kunreuther, Craig E. Landry, Barbara Mellers, Robert J. Meyer, Erwann Michel-Kerjan, Robert Muir-Wood, Mark Pauly, Lisa Robinson, Adam Rose, Paul J. H. Schoemaker, Paul Slovic, Phil Tetlock, Daniel Vastfjall, W. Kip Viscusi, Elke U. Weber, Richard Zeckhauser.

You no longer need a traditional employer plan to get good, affordable health insurance. The New Health Insurance Solution can help you cut your health insurance costs in half if: You're self-employed, an independent contractor, or your employer doesn't provide health insurance (you can probably get coverage on your own for about $94/month--a fraction of what an employer would have to pay for the same coverage)You are employed and pay extra to cover your spouse or children under your employer-sponsored plan--you may save 50% by taking them off your employer planYou own a small business and are getting killed by double-digit premium increases--you can now give employees tax-free money to buy their own plans and get your company out of the health insurance business

The book also explains in detail the best solutions for you if: You can't find affordable health insurance because you or a child have an expensive preexisting medical problem (your state has a program to provide you with guaranteed coverage )You're currently putting money into an IRA or a 401(k)--because you don't realize that an HSA is always a better optionYou're unsure how you or your parents will be able to afford health insurance during retirement, or how to maximize benefits from Medicare--including the new Part D prescription drug plan

The New Health Insurance Solution is the definitive guide to the new ways every American can now get affordable health care--without an employer.

PAUL ZANE PILZER is a world-renowned economist, a former advisor in two White House administrations, an entrepreneur/employer, an award-winning adjunct professor at NYU, and a New York Times bestselling author.

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.