Michael Koller – författare

Designed as a jumping-off point for engineers and decision-makers, this book provides a broad view of machine learning safety. It gives a validation road-map for developers and users of safety-critical systems where human lives are at stake. It addresses the limitations of machine learning systems at every stage of their lifecycle, and provides an overview of techniques to mitigate risks.Chapters are structured to ease understanding of concepts: For each stage in the machine learning lifecycle, the reader is given a brief overview, list of terms, example methods, application methods, and resources for further investigation.•             Specifically addresses safety-critical systems where human users are at risk, physical property can be damaged, or financial losses are possible•             Covers datasets, robustness, reliability, interpretability, explainability, verification, validation, and operational safety•             Covers relevant safety standards•             Easy-to-digest overview of critical and fast-moving field•             Extensive bibliography Those working in safety-critical areas, such as autonomous transportation, medical diagnostics and treatments, robotics and manufacturing, and financial systems, will find this book valuable.

Im vorliegenden Buch werden neue Erkenntnisse der Lebensversicherungsmathematik aus dem Gebiet der Markovmodelle und der stochastischen Zinsen behandelt. Besonderes Gewicht wird auf die Anwendbarkeit der Modelle in der Praxis gelegt, so dass die Aussagen direkt angewendet werden können. Die dargestellten Modelle sind in besonderer Weise geeignet, eine schnelle Tarifierung neuer Lebensversicherungsprodukte zu ermöglichen. Gleichzeitig geben diese Modelle einen tieferen Einblick in das Wesen der Lebensversicherungsmathematik. Der besondere Nutzen dieses Buches liegt einerseits in der parallelen Behandlung der Theorie in stetiger und in diskreter Zeit. Zusätzlich wird das für die Behandlung der Theorie nötige Vorwissen im Buch dargestellt. Durch die vielen Beispiele können die entsprechenden Aussagen direkt in die Praxis umgesetzt werden. Die zweiten Auflage wurde an die aktuellen Entwicklungen, insbesondere in Bezug auf Solvency 2, angepasst.

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.