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The author of Placebo: The Belief Effect draws on real-life examples and detailed research findings to counsel readers on how to make appropriate decisions based on accurate assessments of risk, revealing common flaws in human thinking processes that compromise personal judgment. 40,000 first printing.
Too many executives think risk management is strictly for technical specialists. In Risk Intelligence: Learning to Manage What We Don’t Know, David Apgar challenges this misconception. The author explains how to raise the quality of your risk analysis—-thus enhancing your “risk IQ”—-by applying four simple rules: 1) Recognize which risks are learnable—and reduce their uncertainty by discovering more about them. 2) Identify risks you can learn about the fastest. The higher your learning speed, the more a project is worth pursuing. 3) Take on risky projects one at a time—learning about the risks underlying each before moving to the next. 4) Build networks of business partners, suppliers, and customers who can collectively manage new ventures’ risks by playing distinct roles. The book provides two tools for improving your risk IQ—the Risk Intelligence Audit and the Risk Scorecard—and concludes with a 10-step action plan for systematically raising your managerial and organizational risk IQ. Your reward? Smarter business decisions over time.
This handbook provides a detailed analysis of threats and risk in the international system and of how governments and their intelligence services must adapt and function in order to manage the evolving security environment. This environment, now and for the foreseeable future, is characterised by complexity. The development of disruptive digital technologies; the vulnerability of critical national infrastructure; asymmetric threats such as terrorism; the privatisation of national intelligence capabilities: all have far reaching implications for security and risk management. The leading academics and practitioners who have contributed to this handbook have all done so with the objective of cutting through the complexity, and providing insight on the most pressing security, intelligence, and risk factors today. They explore the changing nature of conflict and crises; interaction of the global with the local; the impact of technological; the proliferation of hostile ideologies and the challenge this poses to traditional models of intelligence; and the impact of all these factors on governance and ethical frameworks. The handbook is an invaluable resource for students and professionals concerned with contemporary security and how national intelligence must adapt to remain effective.
Artificial Intelligence (AI) Design and Solutions for Risk and Security targets readers to understand, learn, define problems, and architect AI projects. Starting from current business architectures and business processes to futuristic architectures. Introduction to data analytics and life cycle includes data discovery, data preparation, data processing steps, model building, and operationalization are explained in detail. The authors examine the AI and ML algorithms in detail, which enables the readers to choose appropriate algorithms during designing solutions. Functional domains and industrial domains are also explained in detail. The takeaways are learning and applying designs and solutions to AI projects with risk and security implementation and knowledge about futuristic AI in five to ten years.
If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. The book covers the latest research on the risks and future impacts of AI. It starts with an introduction to the problem of risk and the future of artificial intelligence, followed by a discussion (Armstrong/Sokala/ÓhÉigeartaigh) on how predictions of its future have fared to date. Omohundro makes the point that even an innocuous artificial agent can easily turn into a serious threat for humans. T. Goertzel explains how to succeed in the design of artificial agents. But will these be a threat for humanity, or a useful tool? Ways to assure beneficial outcomes through ‘machine ethics’ and ‘utility functions’ are discussed by Brundage and Yampolskiy. B. Goertzel and Potapov/Rodionov propose ‘learning’ and ‘empathy’ as paths towards safer AI while Kornai explains how the impact of AI may be bounded. Sandberg explains the implications of human-like AI via the technique of brain emulation. Dewey discusses strategies to deal with the ‘fast takeoff’ of artificial intelligence and, finally, Bishop explains why there is no need to worry because computers will remain in a state of ‘artificial stupidity’. Sharing insights from leading thinkers in artificial intelligence, this book provides you with an expert-level perspective of what is on the horizon for AI, whether it will be a threat for humanity, and how we might counteract this threat.
Praise for Fair Lending ComplianceIntelligence and Implications for Credit Risk Management "Brilliant and informative. An in-depth look at innovative approaches to credit risk management written by industry practitioners. This publication will serve as an essential reference text for those who wish to make credit accessible to underserved consumers. It is comprehensive and clearly written." --The Honorable Rodney E. Hood "Abrahams and Zhang's timely treatise is a must-read for all those interested in the critical role of credit in the economy. They ably explore the intersection of credit access and credit risk, suggesting a hybrid approach of human judgment and computer models as the necessary path to balanced and fair lending. In an environment of rapidly changing consumer demographics, as well as regulatory reform initiatives, this book suggests new analytical models by which to provide credit to ensure compliance and to manage enterprise risk." --Frank A. Hirsch Jr., Nelson Mullins Riley & Scarborough LLP Financial Services Attorney and former general counsel for Centura Banks, Inc. "This book tackles head on the market failures that our current risk management systems need to address. Not only do Abrahams and Zhang adeptly articulate why we can and should improve our systems, they provide the analytic evidence, and the steps toward implementations. Fair Lending Compliance fills a much-needed gap in the field. If implemented systematically, this thought leadership will lead to improvements in fair lending practices for all Americans." --Alyssa Stewart Lee, Deputy Director, Urban Markets Initiative The Brookings Institution "[Fair Lending Compliance]...provides a unique blend of qualitative and quantitative guidance to two kinds of financial institutions: those that just need a little help in staying on the right side of complex fair housing regulations; and those that aspire to industry leadership in profitably and responsibly serving the unmet credit needs of diverse businesses and consumers in America's emerging domestic markets." --Michael A. Stegman, PhD, The John D. and Catherine T. MacArthur Foundation, Duncan MacRae '09 and Rebecca Kyle MacRae Professor of Public Policy Emeritus, University of North Carolina at Chapel Hill
A new book to help senior executives and boards get smart about risk management The ability of businesses to survive and thrive often requires unconventional thinking and calculated risk taking. The key is to make the right decisions—even under the most risky, uncertain, and turbulent conditions. In the new book, Surviving and Thriving in Uncertainty: Creating the Risk Intelligent Enterprise, authors Rick Funston and Steve Wagner suggest that effective risk taking is needed in order to innovate, stay competitive, and drive value creation. Based on their combined decades of experience as practitioners, consultants, and advisors to numerous business professionals throughout the world, Funston and Wagner discuss the adoption of 10 essential and practical skills, which will improve agility, resilience, and realize benefits: Challenging basic business assumptions can help identify "Black Swans" and provide first-mover advantage Defining the corporate risk appetite and risk tolerances can help reduce the risk of ruin. Anticipating potential causes of failure can improve chances of survival and success through improved preparedness. Factoring in velocity and momentum can improve speed of response and recovery. Verifying sources and the reliability of information can improve insights for decision making and thus decision quality. Taking a longer-term perspective can aid in identifying the potential unintended consequences of short-term decisions.
As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.
In this book, the study of strategic risk is not only for its control and mitigation using analytics and digital transformation in organizations, but also it is about the strategic risks that digital transformation can bring to organizations. Strategic risk control is one of the goals in creating intelligent organizations and at the same time it is part of the appetite for creating smarter organizations to support organizations' development. Knowledge that is created by data analytics and the capacity to operationalize that knowledge through digital transformation can produce potential sustainable competitive advantages.The core of the volume is connecting data analytics and artificial intelligence, risk management and digitalization to create strategic intelligence as the capacity of adaptation that organizations need to compete and to succeed. Strategic intelligence is a symbiotic work of artificial intelligence, business intelligence and competitive intelligence. Strategic risk is represented by the probability of having variations in the performance results of the organizations that can limit their capacity to maintain sustainable competitive advantages. There is an emphasis in the book about the conversion of models that support data analytics into actions to mitigate strategic risk based on digital transformation.This book reviews the steps that organizations have taken in using technology that connects the data analytics modeling process and digital operations, such as the shift from the use of statistical learning and machine learning for data analytics to the improvement and use of new technologies. The digitalization process is a potential opportunity for organizations however the results are not necessarily good for everyone. Hence, organizations implement strategic risk control in cloud computing, blockchain, artificial intelligence and create digital networks that are connected internally and externally to deal with internal and external customers, with suppliers and buyers, and with competitors and substitutes. The new risks appear once new knowledge emerges and is in use, but at the same time the new knowledge supports the initiatives to deal with risks arising from novel ways of competing and collaborating.
