How to apply operational risk theory to real-life banking data Modelling Operational and Reputational Risks shows practitioners the best models to use in a given situation, according to the type of risk an organization is facing. Based on extensive applied research on operational risk models using real bank datasets, it offers a wide range of various testing models and fitting techniques for financial practitioners. With this book, professionals will have a foundation for measuring and predicting these important intangibles. Aldo Soprano (Madrid, Spain) is Group Head of operational risk management at UniCredit Group.

A one-stop guide for the theories, applications, and statistical methodologies essential to operational risk Providing a complete overview of operational risk modeling and relevant insurance analytics, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk offers a systematic approach that covers the wide range of topics in this area. Written by a team of leading experts in the field, the handbook presents detailed coverage of the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements. Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk begins with coverage on the four data elements used in operational risk framework as well as processing risk taxonomy. The book then goes further in-depth into the key topics in operational risk measurement and insurance, for example diverse methods to estimate frequency and severity models. Finally, the book ends with sections on specific topics, such as scenario analysis; multifactor modeling; and dependence modeling. A unique companion with Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk, the handbook also features: Discussions on internal loss data and key risk indicators, which are both fundamental for developing a risk-sensitive framework Guidelines for how operational risk can be inserted into a firm’s strategic decisions A model for stress tests of operational risk under the United States Comprehensive Capital Analysis and Review (CCAR) program A valuable reference for financial engineers, quantitative analysts, risk managers, and large-scale consultancy groups advising banks on their internal systems, the handbook is also useful for academics teaching postgraduate courses on the methodology of operational risk.

Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. Explores integration of semantic, unstructured textual data, in Operational Risk Management. Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management. Looks at case studies in the financial and industrial sector. Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.

Operational risk can substantially impact an insurer's risk situation and is now increasingly in the focus of insurance companies, especially due to new European risk-based regulatory framework Solvency II. The aim of this paper is to model and examine the effects of operational risk on fair premiums and solvency capital requirements under Solvency II. In particular, three different approaches of deriving solvency capital requirements are analyzed: the Solvency II standard model, a partial internal model, and a full internal model. This analysis is not only of relevance for Solvency II, but also regarding an insurer's Own Risk and Solvency Assessment (ORSA) that is not only planned in Solvency II, but also by the NAIC in the United States. The analysis emphasizes that diversification plays a central role and that operational risk measurement and management is highly relevant for insurers and should be integrated in an enterprise risk management framework.

This book covers Operational Risk Management (ORM), in the current context, and its new role in the risk management field. The concept of operational risk is subject to a wide discussion also in the field of ORM’s literature, which has increased throughout the years. By analyzing different methodologies that try to integrate qualitative and quantitative data or different measurement approaches, the authors explore the methodological framework, the assumptions, statistical tool, and the main results of an operational risk model projected by intermediaries. A guide for academics and students, the book also discusses the avenue of mitigation acts, suggested by the main results of the methodologies applied. The book will appeal to students, academics, and financial supervisory and regulatory authorities.

A step-by-step, real world guide to the use of Value at Risk (VaR) models, this text applies the VaR approach to the measurement of market risk, credit risk and operational risk. The book describes and critiques proprietary models, illustrating them with practical examples drawn from actual case studies. Explaining the logic behind the economics and statistics, this technically sophisticated yet intuitive text should be an essential resource for all readers operating in a world of risk. Applies the Value at Risk approach to market, credit, and operational risk measurement. Illustrates models with real-world case studies. Features coverage of BIS bank capital requirements.

The book presents arguments that are critical of the Basel II Accord, particularly the advanced measurement approach to operational risk. It is argued that the advanced measurement approach is not viable in terms of costs and benefits and is likely to distract financial institutions from the real task of managing operational risk.

Investment Risk Management provides an overview of developments in risk management and a synthesis of research on the subject. The chapters examine ways to alter exposures through measuring and managing risk exposures and provide an understanding of the latest strategies and trends within risk management.

TEN LAWS OF OPERATIONAL RISK Unlike credit and market risk, operational risk currently lacks an overarching theory to explain how and why losses occur. As a result, operational risk managers have been forced to use unsatisfactory tools and processes that fail to add sufficient commercial value. In Ten Laws of Operational Risk: Understanding its Behaviours to Improve its Management, Michael Grimwade delivers an insightful discussion of the nature of operational risk and a groundbreaking redesign of the profession???s existing tools. The author???s Ten Laws are grounded on the business profiles of firms and the human and institutional behaviours that drive operational risk. They are underpinned by taxonomies for the causes; the inadequacies or failures that constitute both control failures and events; and the impacts of operational risks. Drawing on twenty-five years of first-hand experience and research, this book explains the patterns and trends that are apparent in the historical data and offers solutions to the persistent problems inherent in risk appetite, RCSAs, scenario analysis, reputational risk, stress testing, capital modeling, and insurance. It also provides fresh insights into the everyday activities of risk managers with respect to predictive key risk and control indicators, root cause analysis, why controls fail, the risks posed by change, and product risk profiles. Ten Laws of Operational Risk presents a structured and evidence-based approach to identifying emerging risks and predicting future behaviours related to pandemics, climate change, cybercrime, artificial intelligence, and machine learning. It includes revealing industry data, in-depth case studies, and real-world examples that shed light on recurring and obstinate problems in operational risk management. A must-read resource for Chief Risk Officers and other risk professionals, as well as regulators, management consultants, and students and scholars of operational risk, Ten Laws of Operational Risk provides an invaluable new, systematic, and rigorous approach to operational risk management. PRAISE FOR TEN LAWS OF OPERATIONAL RISK ???Operational Risk can no longer be described as a new concept, but as a discipline few attempts have been made to really understand its behaviour. In his book Michael does this very successfully, blending extensive practical experience with analytical thought leadership to propose a set of laws that explain why and how Operational Risks arise, and what can be done to manage them. Assertions are evidence based, with numerous real examples used to underpin his hypotheses. This is a valuable addition to Operational Risk thinking and is recommended for experienced professionals and novices alike.??? ??? Dr Luke Carrivick, Director of Research & Information, ORX ???Michael has established himself as one of Operational Risk???s foremost thinkers. His ability to use historical data to analyse events is unrivalled. In this must-read book, he identifies ten fundamental laws that provide every Operational Risk practitioner with a clear set of rules they can use to understand current events and predict their impacts.??? ??? Andrew Sheen, former Head of the FSA???s Operational Risk Review team ???Michael is one of the most prominent thinkers in Operational Risk. He combines a long career in Operational Risk management and measurement with a deep, long-standing reflection on the fundamental causes, dynamics and patterns in the manifestation of Operational Risk events. He produces, with this book, a remarkable synthesis of his insightful and innovative work.??? ??? Dr Ariane Chapelle, Honorary Reader, University College London; Managing Partner, Chapelle Consulting ???Michael is a highly respected expert in the field of Operational Risk, who has developed some ground-breaking frameworks for analysing this risk and guiding better risk management decisions. As a working practitioner in the field he brings many insights that will appeal to other practitioners as well as regulators, students and scholars.??? ??? Professor Elizabeth Sheedy, Macquarie Business School ???Michael???s views and analysis challenge the traditional Basel II views of Operational Risk and are genuinely thought-provoking. His book on the Ten Laws of Operational Risk will give financial services clarity and a practical view, where it has been previously lacking, on how best to manage such risks.??? ??? Tin Lau, Group Head of Financial and Strategic Risk, TP ICAP