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| artificial intelligence and risk management: Artificial Intelligence for Risk Management Archie Addo, Srini Centhala, Muthu Shanmugam, 2020-03-13 Artificial Intelligence (AI) for Risk Management is about using AI to manage risk in the corporate environment. The content of this work focuses on concepts, principles, and practical applications that are relevant to the corporate and technology environments. The authors introduce AI and discuss the different types, capabilities, and purposes–including challenges. With AI also comes risk. This book defines risk, provides examples, and includes information on the risk-management process. Having a solid knowledge base for an AI project is key and this book will help readers define the knowledge base needed for an AI project by developing and identifying objectives of the risk-knowledge base and knowledge acquisition for risk. This book will help you become a contributor on an AI team and learn how to tell a compelling story with AI to drive business action on risk. |
| artificial intelligence and risk management: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry. |
| artificial intelligence and risk management: Machine Learning for Financial Risk Management with Python Abdullah Karasan, 2021-12-07 Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models |
| artificial intelligence and risk management: Artificial Intelligence in Financial Markets Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous, Konstantinos Theofilatos, 2016-11-21 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. |
| artificial intelligence and risk management: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| artificial intelligence and risk management: Artificial Intelligence Design and Solution for Risk and Security Archie Addo, Srini Centhala, Muthu Shanmugam, 2020-03-13 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. |
| artificial intelligence and risk management: Artificial Intelligence and Islamic Finance Adel M. Sarea, Ahmed H. Elsayed, Saeed A. Bin-Nashwan, 2021-12-31 This book provides a systematic overview of the current trends in research relating to the use of artificial intelligence in Islamic financial institutions (IFIs), across all organization of Islamic cooperation (OIC) countries. Artificial Intelligence and Islamic Finance discusses current and potential applications of artificial intelligence (AI) for risk management in Islamic finance. It covers various techniques of risk management, encompassing asset and liability management risk, credit, market, operational, liquidity risk, as well as regulatory and Shariah risk compliance within the financial industry. The authors highlight AI’s ability to combat financial crime such as monitoring trader recklessness, anti-fraud and anti-money laundering, and assert that the capacity of machine learning (ML) to examine large amounts of data allows for greater granular and profound analyses across a variety of Islamic financial products and services. The book concludes with practical limitations around data management policies, transparency, and lack of necessary skill sets within financial institutions. By adopting new methodological approaches steeped in an Islamic economic framework (e.g., analysing FinTech in the context of Shariah principles and Islamic values), it devises practical solutions and generates insightful knowledge, helping readers to understand and explore the role of technological enablers in the Islamic finance industry, such as RegTech and artificial intelligence, in providing better and Shariah-compliant services to customers through digital platforms. The book will attract a wide readership spanning Shariah scholars, academicians, and researchers as well as Islamic financial practitioners and policy makers. |
| artificial intelligence and risk management: Risk Modeling Terisa Roberts, Stephen J. Tonna, 2022-09-20 A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management. |
| artificial intelligence and risk management: Economics and Law of Artificial Intelligence Georgios I. Zekos, 2021-01-11 This book presents a comprehensive analysis of the alterations and problems caused by new technologies in all fields of the global digital economy. The impact of artificial intelligence (AI) not only on law but also on economics is examined. In the first part, the economics of AI are explored, including topics such as e-globalization and digital economy, corporate governance, risk management, and risk development, followed by a quantitative econometric analysis which utilizes regressions stipulating the scale of the impact. In the second part, the author presents the law of AI, covering topics such as the law of electronic technology, legal issues, AI and intellectual property rights, and legalizing AI. Case studies from different countries are presented, as well as a specific analysis of international law and common law. This book is a must-read for scholars and students of law, economics, and business, as well as policy-makers and practitioners, interested in a better understanding of legal and economic aspects and issues of AI and how to deal with them. |
| artificial intelligence and risk management: Patient Safety Ethics John D. Banja, 2019-06-25 Developing best practices and ethical systems to protect and enhance patient safety. Human errors occur all too frequently in medical practice settings. One sobering recent report claimed that medical errors are the third leading cause of death in the United States. Hoping to reverse this disturbing trend but wondering why it is that things usually go well despite errors, John D. Banja's Patient Safety Ethics lays out a model that advocates vigilance, mindfulness, compliance, and humility as core ethical principles of patient safety. Arguing that the safe provision of healthcare is one of the most fundamental moral obligations of clinicians, Banja surveys the research literature on harm-causing medical errors to explore the ethical foundations of patient safety and to reduce the severity and frequency of medical error. Drawing on contemporary scholarship on quality improvement, risk management, and medical decision making, Banja also relies on a novel source of information to illustrate patient safety ethics: medical malpractice suits. Providing professional perspective with insights from prominent patient safety experts, Patient Safety Ethics identifies hazard pitfalls and suggests concrete ways for clinicians and regulators to improve patient safety through an ethically cultivated program of hazard awareness. |
| artificial intelligence and risk management: The Future of Risk Management Howard Kunreuther, Robert J. Meyer, Erwann O. Michel-Kerjan, 2019-07-26 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 Västfjäll, W. Kip Viscusi, Elke U. Weber, Richard Zeckhauser. |
| artificial intelligence and risk management: Artificial Intelligence for Managers Malay A. Upadhyay, 2020-09-17 Understand how to adopt and implement AI in your organization Key Features _ 7 Principles of an AI Journey _ The TUSCANE Approach to Become Data Ready _ The FAB-4 Model to Choose the Right AI Solution _ Major AI Techniques & their Applications: - CART & Ensemble Learning - Clustering, Association Rules & Search - Reinforcement Learning - Natural Language Processing - Image Recognition Description Most AI initiatives in organizations fail today not because of a lack of good AI solutions, but because of a lack of understanding of AI among its end users, decision makers and investors. Today, organizations need managers who can leverage AI to solve business problems and provide a competitive advantage. This book is designed to enable you to fill that need, and create an edge for your career. The chapters offer unique managerial frameworks to guide an organization's AI journey. The first section looks at what AI is; and how you can prepare for it, decide when to use it, and avoid pitfalls on the way. The second section dives into the different AI techniques and shows you where to apply them in business. The final section then prepares you from a strategic AI leadership perspective to lead the future of organizations. By the end of the book, you will be ready to offer any organization the capability to use AI successfully and responsibly - a need that is fast becoming a necessity. What will you learn _ Understand the major AI techniques & how they are used in business. _ Determine which AI technique(s) can solve your business problem. _ Decide whether to build or buy an AI solution. _ Estimate the financial value of an AI solution or company. _ Frame a robust policy to guide the responsible use of AI. Who this book is for This book is for Executives, Managers and Students on both Business and Technical teams who would like to use Artificial Intelligence effectively to solve business problems or get an edge in their careers. Table of Contents 1.Preface 2.Acknowledgement 3.About the Author 4.Section 1: Beginning an AI Journey a. AI Fundamentals b. 7 Principles of an AI Journey c. Getting Ready to Use AI 5.Section 2: Choosing the Right AI Techniques a. Inside the AI Laboratory b. How AI Predicts Values & Categories c. How AI Understands and Predicts Behaviors & Scenarios d. How AI Communicates & Learns from Mistakes e. How AI Starts to Think Like Humans 6.Section 3: Using AI Successfully & Responsibly a. AI Adoption & Valuation b. AI Strategy, Policy & Risk Management 7.Epilogue |
| artificial intelligence and risk management: The Master Algorithm Pedro Domingos, 2015-09-22 Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible. |
| artificial intelligence and risk management: Machine Learning for High-Risk Applications Patrick Hall, James Curtis, Parul Pandey, 2023-04-17 The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab |
| artificial intelligence and risk management: Revisiting Supply Chain Risk George A. Zsidisin, Michael Henke, 2018-12-18 This book offers a bridge between our current understanding of supply chain risk in practice and theory, and the monumental shifts caused by the emergence of the fourth industrial revolution. Supply chain risk and its management have experienced significant attention in scholarship and practice over the past twenty years. Our understanding of supply chain risk and its many facets, such as uncertainty and vulnerability, has expanded beyond utilizing approaches such as deploying inventory to buffer the initial effects of disruptions. Even with our increased knowledge of supply chain risk, being in the era of lean supply chain practices, digitally managed global supply chains, and closely interconnected networks, firms are exposed as ever to supply chain uncertainties that can damage, or even destroy, their ability to compete in the marketplace. The book acknowledges the criticality of big data analytics in Supply Chain Risk Management (SCRM) processes and provides appropriate tools and approaches for creating robust SCRM processes. Revisiting Supply Chain Risk presents a state-of-the-art look at SCRM through current research and philosophical thought. It is divided into six sections that highlight established themes, as well as provide new insights to developing areas of inquiry and contexts on the topic. Section 1 examines the first step in managing supply chain risk, risk assessment. The chapters in Section 2 encompass resiliency in supply chains, while Section 3 looks at relational and behavioral perspectives from varying units of analysis including consortiums, teams and decision makers. Section 4 focuses on examining supply chain risk in the contexts of sustainability and innovation. Section 5 provides insight on emerging typologies and taxonomies for classifying supply chain risk. The book concludes with Section 6, featuring illustrative case studies as real-world examples in assessing and managing supply chain risk. |
| artificial intelligence and risk management: Artificial Intelligence and Big Data for Financial Risk Management Noura Metawa, M. Kabir Hassan, Saad Metawa, 2022-08-26 This book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment. |
| artificial intelligence and risk management: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| artificial intelligence and risk management: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| artificial intelligence and risk management: Artificial Intelligence in Asset Management Söhnke M. Bartram, Jürgen Branke, Mehrshad Motahari, 2020-08-28 Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity. |
| artificial intelligence and risk management: An Intelligence in Our Image Osonde A. Osoba, William Welser IV, William Welser, 2017-04-05 Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches for combating these problems. |
| artificial intelligence and risk management: Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies Wynn, Martin George, 2021-10-15 Companies from various sectors of the economy are confronted with the new phenomenon of digital transformation and are faced with the challenge of formulating and implementing a company-wide strategy to incorporate what are often viewed as “disruptive” technologies. These technologies are sometimes associated with significant and extremely rapid change, in some cases with even the replacement of established business models. Many of these technologies have been deployed in unison by leading-edge companies acting as the catalyst for significant process change and people skills enhancement. The Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies examines the phenomenon of digital transformation and the impact of disruptive technologies through the lens of industry case studies where different combinations of these new technologies have been deployed and incorporated into enterprise IT and business strategies. Covering topics including chatbot implementation, multinational companies, cloud computing, internet of things, artificial intelligence, big data and analytics, immersive technologies, and social media, this book is essential for senior management, IT managers, technologists, computer scientists, cybersecurity analysts, academicians, researchers, IT consultancies, professors, and students. |
| artificial intelligence and risk management: Artificial Intelligence and Global Security Yvonne R. Masakowski, 2020-07-15 Artificial Intelligence and Global Security: Future Trends, Threats and Considerations brings a much-needed perspective on the impact of the integration of Artificial Intelligence (AI) technologies in military affairs. Experts forecast that AI will shape future military operations in ways that will revolutionize warfare. |
| artificial intelligence and risk management: Trustworthy AI Beena Ammanath, 2022-03-15 An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI. |
| artificial intelligence and risk management: Artificial Intelligence in Construction Engineering and Management Limao Zhang, Yue Pan, Xianguo Wu, Mirosław J. Skibniewski, 2021-06-18 This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally. |
| artificial intelligence and risk management: The AI Book Ivana Bartoletti, Anne Leslie, Shân M. Millie, 2020-06-29 Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important |
| artificial intelligence and risk management: Systemic Banking Crises Revisited Mr.Luc Laeven, Mr.Fabian Valencia, 2018-09-14 This paper updates the database on systemic banking crises presented in Laeven and Valencia (2008, 2013). Drawing on 151 systemic banking crises episodes around the globe during 1970-2017, the database includes information on crisis dates, policy responses to resolve banking crises, and the fiscal and output costs of crises. We provide new evidence that crises in high-income countries tend to last longer and be associated with higher output losses, lower fiscal costs, and more extensive use of bank guarantees and expansionary macro policies than crises in low- and middle-income countries. We complement the banking crises dates with sovereign debt and currency crises dates to find that sovereign debt and currency crises tend to coincide or follow banking crises. |
| artificial intelligence and risk management: Artificial Intelligence and Machine Learning in Business Management Sandeep Kumar Panda, Vaibhav Mishra, R. Balamurali, Ahmed A. Elngar, 2021-11-04 Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines. |
| artificial intelligence and risk management: A Human's Guide to Machine Intelligence Kartik Hosanagar, 2020-03-10 A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence. |
| artificial intelligence and risk management: Artificial Intelligence for Risk Mitigation in the Financial Industry Ambrish Kumar Mishra, Shweta Anand, Narayan C. Debnath, Purvi Pokhariyal, Archana Patel, 2024-05-29 Artificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come. |
| artificial intelligence and risk management: The Essentials of Risk Management, Second Edition Michel Crouhy, Dan Galai, Robert Mark, 2013-12-06 The essential guide to quantifying risk vs. return has been updated to reveal the newest, most effective innovations in financial risk management Written for risk professionals and non-risk professionals alike, this easy-to-understand guide helps readers meet the increasingly insistent demand to make sophisticated assessments of their company’s risk exposure Provides the latest methods for measuring and transferring credit risk, increase risk-management transparency, and implement an organization-wide Enterprise risk Management (ERM) approach The authors are renowned figures in risk management: Crouhy heads research and development at NATIXIS; Galai is the Abe Gray Professor of Finance and Business Asdministration at Hebrew University; and Mark is the founding CEO of Black Diamond Risk |
| artificial intelligence and risk management: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
| artificial intelligence and risk management: Enterprise Risk Management in Finance David L. Olson, Desheng Dash Wu, 2015-05-26 Enterprise Risk Management in Finance is a guide to measuring and managing Enterprise-wide risks in financial institutions. Financial institutions operate in a unique manner when compared to other businesses. They are, by the nature of their business, highly exposed to risk at every level, and indeed employ their own risk management functions to manage many of these risks. However, financial firms are also highly exposed at enterprise level. Traditional approaches and frameworks for ERM are flawed when applied to banks, asset managers or insurance houses, and a different approach is needed. This new book provides a comprehensive, technical guide to ERM for financial institutions. Split into three parts, it first sets the scene, putting ERM in the context of finance houses. It will examine the financial risks already inherent in banking, and then insurance operations, and how these need to be accounted for at a floor and enterprise level. The book then provides the necessary tools to implement ERM in these environments, including performance analysis, credit analysis and forecasting applications. Finally, the book provides real life cases of successful and not so successful ERM in financial institutions. Technical and rigorous, this book will be a welcome addition to the literature in this area, and will appeal to risk managers, actuaries, regulators and senior managers in banks and financial institutions. |
| artificial intelligence and risk management: Regulating Artificial Intelligence in Industry Damian M. Bielicki, 2021-12-24 Artificial Intelligence (AI) has augmented human activities and unlocked opportunities for many sectors of the economy. It is used for data management and analysis, decision making, and many other aspects. As with most rapidly advancing technologies, law is often playing a catch up role so the study of how law interacts with AI is more critical now than ever before. This book provides a detailed qualitative exploration into regulatory aspects of AI in industry. Offering a unique focus on current practice and existing trends in a wide range of industries where AI plays an increasingly important role, the work contains legal and technical analysis performed by 15 researchers and practitioners from different institutions around the world to provide an overview of how AI is being used and regulated across a wide range of sectors, including aviation, energy, government, healthcare, legal, maritime, military, music, and others. It addresses the broad range of aspects, including privacy, liability, transparency, justice, and others, from the perspective of different jurisdictions. Including a discussion of the role of AI in industry during the Covid-19 pandemic, the chapters also offer a set of recommendations for optimal regulatory interventions. Therefore, this book will be of interest to academics, students and practitioners interested in technological and regulatory aspects of AI. |
| artificial intelligence and risk management: Alternative Data and Artificial Intelligence Techniques Qingquan Tony Zhang, Beibei Li, Danxia Xie, 2022-10-31 This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation. |
| artificial intelligence and risk management: Regulating Artificial Intelligence Thomas Wischmeyer, Timo Rademacher, 2019-11-29 This book assesses the normative and practical challenges for artificial intelligence (AI) regulation, offers comprehensive information on the laws that currently shape or restrict the design or use of AI, and develops policy recommendations for those areas in which regulation is most urgently needed. By gathering contributions from scholars who are experts in their respective fields of legal research, it demonstrates that AI regulation is not a specialized sub-discipline, but affects the entire legal system and thus concerns all lawyers. Machine learning-based technology, which lies at the heart of what is commonly referred to as AI, is increasingly being employed to make policy and business decisions with broad social impacts, and therefore runs the risk of causing wide-scale damage. At the same time, AI technology is becoming more and more complex and difficult to understand, making it harder to determine whether or not it is being used in accordance with the law. In light of this situation, even tech enthusiasts are calling for stricter regulation of AI. Legislators, too, are stepping in and have begun to pass AI laws, including the prohibition of automated decision-making systems in Article 22 of the General Data Protection Regulation, the New York City AI transparency bill, and the 2017 amendments to the German Cartel Act and German Administrative Procedure Act. While the belief that something needs to be done is widely shared, there is far less clarity about what exactly can or should be done, or what effective regulation might look like. The book is divided into two major parts, the first of which focuses on features common to most AI systems, and explores how they relate to the legal framework for data-driven technologies, which already exists in the form of (national and supra-national) constitutional law, EU data protection and competition law, and anti-discrimination law. In the second part, the book examines in detail a number of relevant sectors in which AI is increasingly shaping decision-making processes, ranging from the notorious social media and the legal, financial and healthcare industries, to fields like law enforcement and tax law, in which we can observe how regulation by AI is becoming a reality. |
| artificial intelligence and risk management: Enterprise Artificial Intelligence Transformation Rashed Haq, 2020-06-10 Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation. |
| artificial intelligence and risk management: Carbon Finance: A Risk Management View Martin Hellmich, Rudiger Kiesel, 2021-11-24 Mastering climate change has been recognised as a major challenge for the current decade. Besides the physical risks of climate change, the accompanying economic risks are substantial. Carbon Finance: A Risk Management View provides an in-depth analysis of how climate change will affect all aspects of financial markets and how mathematical and statistical methods can be used to analyse, model and manage the ensuing financial risks. There is a focus on the transition risk (termed carbon risk), but also a discussion of the impact of physical risks (as these risks are closely entangled) on the way to low carbon economies. This is a valuable overview for readers seeking an analysis of carbon risks from the perspective of financial risk management, utilising quantitative risk management tools. |
| artificial intelligence and risk management: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| artificial intelligence and risk management: Handbook of Research on Applied AI for International Business and Marketing Applications Christiansen, Bryan, Škrinjari?, Tihana, 2020-09-25 Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students. |
| artificial intelligence and risk management: Robot-Proof, revised and updated edition Joseph E. Aoun, 2024-10-15 A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human. |
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The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
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ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
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Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
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Uses, Opportunities, and Risks of Artificial Intelligence in Financial Services 2. Staff Acknowledgements* The Office of Financial Institutions, including Acting Assistant Secretary …
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Jan 23, 2024 · Engaging with Artificial Intelligence (AI) 4 Introduction The purpose of this publication is to provide organisations with guidance on how to use AI systems securely. The …
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5536 L.-W.WONGETAL. (2009)definedagilityasamulti-dimensionalconstructof demandresponse,jointplanning,customerresponsive-nessandvisibility. However,afirm ...
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Artificial Intelligence Risk Management Framework (AI RMF …
The Artificial Intelligence Risk Management Framework (AI RMF) is intended to be a living document. NIST will review the content and usefulness of the Framework regularly to …
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2 1 These use cases are distinguished from other applications that may raise unique policy and regulatory issues, including AI/ML models used in the context of consumer finance …
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Artificial Intelligence Risk Management Framework
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NIST Seeks Comments on AI Risk Management Framework Guidance, Workshop Date Set (https://www.nist.gov/news-events/news/2022/08/nist-seeks-comments-ai-risk-
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grounded and risk-informed awareness of its potential and pitfalls. As directed by Executive Order 14110, Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the U.S. …
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2023, the AI RMF is intended to build on, align with, and support AI risk management efforts by others. • A NIST AI RMF Playbook suggests ways to navigate and use the AI RMF to …
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SUBJECT: Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence Artificial intelligence (AI) is one of the most powerful technologies of our time, and …
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existing literature and research on the utilisation of Artificial Intelligence-based risk management. We also incorporate case studies and examples from the application of AI-based risk …
Artificial Intelligence in Finance: Transforming Decision …
May 11, 2024 · Artificial Intelligence in Finance: Transforming Decision-Making and Risk Management for Enhanced Financial Outcomes Bryce Layn University of Arkansas, …
Generative Artificial Intelligence Risk Assessment
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Regulating AI in the financial sector: recent developments and …
Keywords: artificial intelligence, machine learning, corporate governance, risk management, risk modelling . FSI Insights are written by members of the Financial Stability Institute (FSI) of the …
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Keywords: Artificial Intelligence, Risk Management, Fraud Detection, Banking Sector, Regulatory Compliance Introduction Risk management in the banking sector is a comprehensive …
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Artificial Intelligence Risk Management Framework (AI RMF …
The Artificial Intelligence Risk Management Framework (AI RMF) is intended to be a living document. NIST will review the content and usefulness of the Framework regularly to …
Enterprise Use of Artificial Intelligence and Machine Learning
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US Department of Transportation (DOT) Compliance Plan for
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Approaching Emergent Risks: An Exploratory Study into the …
Sep 19, 2023 · Keywords ± Artificial Intelligence, Risk Management, Model Risk Management, Financial Services. 1. Introduction 1.1. Background The implementation of Artificial Intelligence …
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Keywords: Artificial Intelligence, Risk Management, Profitability, Investment Firms Introduction The financial services industry acts as a catalyst in the economic system, driving advancement …
Artificial Intelligence Risk Management Framework (AI RMF …
Artificial Intelligence Risk Management Framework (AI RMF 1.0) NIST AI 100-1. WHAT IS THE AI RMF? Voluntary resource for organizations designing, developing, deploying, or using AI …
(Original Signature of Member) CONGRESS S H. R. ll - Ted Lieu
To require Federal agencies to use the Artificial Intelligence Risk Management Framework developed by the National Institute of Standards and Technology with respect to the use of …
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Artificial Intelligence Risk Management The risk-based approach in the Artificial Intelligence Act Candidate number: 631 ... The topic of this thesis is the relationship between high-risk Artificial …
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DRAFT FOR PUBLIC REVIEW EXECUTIVE OFFICE OF THE …
Artificial Intelligence Ethics for the Intelligence Community, as well as policies governing specific high-risk national security applications of AI, such as DoD Directive 3000.09, Autonomy in ...
29 Implementation of artificial intelligence into risk …
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Risk management in the AI Act - arXiv.org
Risk management in the Artificial Intelligence Act Jonas Schuett* The proposed EU AI Act is the first comprehensive attempt to regulate AI in a major jurisdiction. This article analyses Article 9, …
