Peter C. Young – författare

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 casebook extends Strategic Risk Leadership: Engaging a World of Risk, Uncertainty and the Unknown, bringing theory and practice grounded in the first book to life with an array of applicable, real-world examples. The book enables critical thinking about the current state of risk management and ERM, demonstrating contemporary shortcomings and challenges from real-life cases drawn from a global selection of well-known organizations. It confronts modern risk management practices and discusses what leaders should do to deal with unpredictable environments. Providing a basis for developing more effective risk management approaches, the book identifies shortcomings of contemporary approaches to risk management and specifies how to deal with the major risks we face today, illuminated by a variety of comprehensive global examples. It also provides valuable insights on these approaches for managers and leaders in general—including risk executives and chief risk officers—as well as advanced risk management students. End-of-chapter cases illustrate both good and bad risk management approaches as useful inspiration for reflective risk leaders.This book will be a hugely valuable resource for those studying or teaching risk management.

This casebook extends Strategic Risk Leadership: Engaging a World of Risk, Uncertainty and the Unknown, bringing theory and practice grounded in the first book to life with an array of applicable, real-world examples. The book enables critical thinking about the current state of risk management and ERM, demonstrating contemporary shortcomings and challenges from real-life cases drawn from a global selection of well-known organizations. It confronts modern risk management practices and discusses what leaders should do to deal with unpredictable environments. Providing a basis for developing more effective risk management approaches, the book identifies shortcomings of contemporary approaches to risk management and specifies how to deal with the major risks we face today, illuminated by a variety of comprehensive global examples. It also provides valuable insights on these approaches for managers and leaders in general—including risk executives and chief risk officers—as well as advanced risk management students. End-of-chapter cases illustrate both good and bad risk management approaches as useful inspiration for reflective risk leaders.This book will be a hugely valuable resource for those studying or teaching risk management.

True Digital Control: Statistical Modelling and Non–Minimal State Space Designdevelops a true digital control design philosophy that encompasses data–based model identification, through to control algorithm design, robustness evaluation and implementation. With a heritage from both classical and modern control system synthesis, this book is supported by detailed practical examples based on the authors’ research into environmental, mechatronic and robotic systems. Treatment of both statistical modelling and control design under one cover is unusual and highlights the important connections between these disciplines.Starting from the ubiquitous proportional–integral controller, and with essential concepts such as pole assignment introduced using straightforward algebra and block diagrams, this book addresses the needs of those students, researchers and engineers, who would like to advance their knowledge of control theory and practice into the state space domain; and academics who are interested to learn more about non–minimal state variable feedback control systems. Such non–minimal state feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner.Key features: Covers both system identification and control system design in a unified mannerIncludes practical design case studies and simulation examplesConsiders recent research into time–variable and state–dependent parameter modelling and control, essential elements of adaptive and nonlinear control system design, and the delta–operator (the discrete–time equivalent of the differential operator) systemsAccompanied by a website hosting MATLAB examplesTrue Digital Control: Statistical Modelling and Non–Minimal State Space Design is a comprehensive and practical guide for students and professionals who wish to further their knowledge in the areas of modern control and system identification.

The Impact of CLIMATE CHANGE and SUSTAINABILITY STANDARDS on the INSURANCE MARKET The book explores the role of the insurance industry in contributing and responding to the harms that climate change has brought. This book delves into the physical and logical impacts, both direct and indirect, on the insurance industry. Subjects discussed include new technology such as big data, artificial intelligence, machine learning, the growth of sustainable economics with foreign direct investments (FDIs), trustworthiness, and ethics. Related use cases of data science for claim processing, fraud detection and prevention, policy administration, pricing, and underwriting are discussed along with cyber security issues, data protection, and big data regulatory reforms. To promote ESG sustainability, the insurance industry plays a critical and significant role. Climate-related risks are being factored into underwriting and investing strategies. Through their own operations and business activities, insurers may promote the ESG agenda and move towards sustainability. Also discussed are promoting diversity and inclusion, lowering greenhouse gas (GHG) emissions, resolving gender inequality, and helping communities through charitable work, which all improve a company’s brand, reputation, and ESG credentials. Audience The book is specially designed for administrators, lecturers, researchers, students of insurance and sustainability, students in financial services, insurance practitioners, actuaries, loss adjusters, underwriters, regulators, facilities management, utility companies, voluntary organizations, government departments, business leaders, policymakers, decision-makers, investors, risk managers, compliance managers, and audit managers amongst many others.

Public Sector Leadership in Assessing and Addressing Risk explores risk management in practice, taking a specific focus on the identification of risks in the European public sector while contextualising its Eurocentric analysis within a global setting.The volume lays important groundwork for understanding the main philosophical premises of risk management. Navigating the text's philosophical underpinnings such as 'Risk Management is a misnomer', the editors provide deep insight into global, strategic, and operational risk management that will prove invaluable for any practitioner.Providing high quality academic research, ESFIRM provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale.

This book builds on the methods introduced in the author’s previous Springer book Recursive Estimation and Time-Series Analysis to deliver a powerful and practical framework for Data-Based Mechanistic (DBM) modelling directly from time-series data. DBM modelling produces interpretable continuous-time transfer function models, allowing the underlying linear or nonlinear differential equations to be understood in clear physical terms closely tied to the real dynamics behind the data. All modelling tools are freely available in dedicated toolboxes for MATLAB, including advanced modules for forecasting and control based on DBM models. The four application chapters provide an in-depth perspective on DBM modelling, while a separate tutorial appendix offers a guided, step-by-step introduction to the complete DBM workflow using accessible hydrological examples. These methodological foundations are then illustrated by their application in three major areas of topical importance: global climate dynamics, the COVID-19 epidemic, and investment–unemployment interactions in the USA. This book also shows how large-scale simulation models can be distilled into compact, transparent DBM representations, and how these small 'emulation’ models can deliver physical insight, reliable forecasting, and effective control (management) strategies. Blending rigorous estimation theory, real-world relevance, and fully reproducible tools, this book offers a unique bridge between advanced data-based modelling and today’s most pressing dynamical problems.

This is a revised version of the 1984 book of the same name but considerably modified and enlarged to accommodate the developments in recursive estimation and time series analysis that have occurred over the last quarter century.