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| data and risk management: Data Analytics for Engineering and Construction Project Risk Management Ivan Damnjanovic, Kenneth Reinschmidt, 2019-05-23 This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution. |
| data and risk management: Operational Risk Management Ron S. Kenett, Yossi Raanan, 2011-06-20 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. |
| data and risk management: Total Information Risk Management Alexander Borek, Ajith Kumar Parlikad, Jela Webb, Philip Woodall, 2013-08-30 How well does your organization manage the risks associated with information quality? Managing information risk is becoming a top priority on the organizational agenda. The increasing sophistication of IT capabilities along with the constantly changing dynamics of global competition are forcing businesses to make use of their information more effectively. Information is becoming a core resource and asset for all organizations; however, it also brings many potential risks to an organization, from strategic, operational, financial, compliance, and environmental to societal. If you continue to struggle to understand and measure how information and its quality affects your business, this book is for you. This reference is in direct response to the new challenges that all managers have to face. Our process helps your organization to understand the pain points regarding poor data and information quality so you can concentrate on problems that have a high impact on core business objectives. This book provides you with all the fundamental concepts, guidelines and tools to ensure core business information is identified, protected and used effectively, and written in a language that is clear and easy to understand for non-technical managers. - Shows how to manage information risk using a holistic approach by examining information from all sources - Offers varied perspectives of an author team that brings together academics, practitioners and researchers (both technical and managerial) to provide a comprehensive guide - Provides real-life case studies with practical insight into the management of information risk and offers a basis for broader discussion among managers and practitioners |
| data and risk management: Security Risk Management for the Internet of Things John Soldatos, 2020-06-15 In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot. |
| data and risk management: The Book of Alternative Data Alexander Denev, Saeed Amen, 2020-07-21 The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users. |
| data and risk management: Data Science and Risk Analytics in Finance and Insurance Tze Leung Lai, Haipeng Xing, 2024-10-02 This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension. |
| data and risk management: Data Protection David G. Hill, 2016-04-19 Failure to appreciate the full dimensions of data protection can lead to poor data protection management, costly resource allocation issues, and exposure to unnecessary risks. Data Protection: Governance, Risk Management, and Compliance explains how to gain a handle on the vital aspects of data protection.The author begins by building the foundatio |
| data and risk management: Financial Analysis and Risk Management Victoria Lemieux, 2012-10-20 The Global Financial Crisis and the Eurozone crisis that has followed have drawn attention to weaknesses in financial records, information and data. These weaknesses have led to operational risks in financial institutions, flawed bankruptcy and foreclosure proceedings following the Crisis, and inadequacies in financial supervisors’ access to records and information for the purposes of a prudential response. Research is needed to identify the practices that will provide the records, information and data needed to support more effective financial analysis and risk management. The unique contribution of this volume is in bringing together researchers in distinct domains that seldom interact to identify theoretical, technological, policy and practical issues related to the management of financial records, information and data. The book will, therefore, appeal to researchers or advanced practitioners in the field of finance and those with an interest in risk management, computer science, cognitive science, sociology, management information systems, information science, and archival science as applied to the financial domain. |
| data 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. |
| data and risk management: Data Protection Beyond Borders Federico Fabbrini, Edoardo Celeste, John Quinn, 2021-02-11 This timely book examines crucial developments in the field of privacy law, efforts by legal systems to impose their data protection standards beyond their borders and claims by states to assert sovereignty over data. By bringing together renowned international privacy experts from the EU and the US, the book provides an accurate analysis of key trends and prospects in the transatlantic context, including spaces of tensions and cooperation between the EU and the US in the field of data protection law. The chapters explore recent legal and policy developments both in the private and law enforcement sectors, including recent rulings by the Court of Justice of the EU dealing with Google and Facebook, recent legislative initiatives in the EU and the US such as the CLOUD Act and the e-evidence proposal, as well as ongoing efforts to strike a transatlantic deal in the field of data sharing. All of the topics are thoroughly examined and presented in an accessible way that will appeal to scholars in the fields of law, political science and international relations, as well as to a wider and non-specialist audience. The book is an essential guide to understanding contemporary challenges to data protection across the Atlantic. |
| data and risk management: Operational Risk Management Philippa X. Girling, 2013-10-14 A best practices guide to all of the elements of an effective operational risk framework While many organizations know how important operational risks are, they still continue to struggle with the best ways to identify and manage them. Organizations of all sizes and in all industries need best practices for identifying and managing key operational risks, if they intend on exceling in today's dynamic environment. Operational Risk Management fills this need by providing both the new and experienced operational risk professional with all of the tools and best practices needed to implement a successful operational risk framework. It also provides real-life examples of successful methods and tools you can use while facing the cultural challenges that are prevalent in this field. Contains informative post-mortems on some of the most notorious operational risk events of our time Explores the future of operational risk in the current regulatory environment Written by a recognized global expert on operational risk An effective operational risk framework is essential for today's organizations. This book will put you in a better position to develop one and use it to identify, assess, control, and mitigate any potential risks of this nature. |
| data and risk management: The Risk-based Approach to Data Protection Raphaël Gellert, 2020 This title provides an extensive analysis of the risk-based approach taken to data protection. It also considers risk management methodologies and provides discussions at the intersection of data protection law scholarship, regulation theory, and risk and risk management literature. |
| data and risk management: Event- and Data-Centric Enterprise Risk-Adjusted Return Management Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil, 2022-01-06 Take a holistic view of enterprise risk-adjusted return management in banking. This book recommends that a bank transform its siloed operating model into an agile enterprise model. It offers an event-driven, process-based, data-centric approach to help banks plan and implement an enterprise risk-adjusted return model (ERRM), keeping the focus on business events, processes, and a loosely coupled enterprise service architecture. Most banks suffer from a lack of good quality data for risk-adjusted return management. This book provides an enterprise data management methodology that improves data quality by defining and using data ontology and taxonomy. It extends the data narrative with an explanation of the characteristics of risk data, the usage of machine learning, and provides an enterprise knowledge management methodology for risk-return optimization. The book provides numerous examples for process automation, data analytics, event management, knowledge management, and improvements to risk quantification. The book provides guidance on the underlying knowledge areas of banking, enterprise risk management, enterprise architecture, technology, event management, processes, and data science. The first part of the book explains the current state of banking architecture and its limitations. After defining a target model, it explains an approach to determine the gap and the second part of the book guides banks on how to implement the enterprise risk-adjusted return model. What You Will Learn Know what causes siloed architecture, and its impact Implement an enterprise risk-adjusted return model (ERRM) Choose enterprise architecture and technology Define a reference enterprise architecture Understand enterprise data management methodology Define and use an enterprise data ontology and taxonomy Create a multi-dimensional enterprise risk data model Understand the relevance of event-driven architecture from business generation and risk management perspectives Implement advanced analytics and knowledge management capabilities Who This Book Is For The global banking community, including: senior management of a bank, such as the Chief Risk Officer, Head of Treasury/Corporate Banking/Retail Banking, Chief Data Officer, and Chief Technology Officer. It is also relevant for banking software vendors, banking consultants, auditors, risk management consultants, banking supervisors, and government finance professionals. |
| data and risk management: Credit Risk Management Tony Van Gestel, Bart Baesens, 2009 This first of three volumes on credit risk management, providing a thorough introduction to financial risk management and modelling. |
| data and risk management: Measuring and Managing Information Risk Jack Freund, Jack Jones, 2014-08-23 Using the factor analysis of information risk (FAIR) methodology developed over ten years and adopted by corporations worldwide, Measuring and Managing Information Risk provides a proven and credible framework for understanding, measuring, and analyzing information risk of any size or complexity. Intended for organizations that need to either build a risk management program from the ground up or strengthen an existing one, this book provides a unique and fresh perspective on how to do a basic quantitative risk analysis. Covering such key areas as risk theory, risk calculation, scenario modeling, and communicating risk within the organization, Measuring and Managing Information Risk helps managers make better business decisions by understanding their organizational risk. - Uses factor analysis of information risk (FAIR) as a methodology for measuring and managing risk in any organization. - Carefully balances theory with practical applicability and relevant stories of successful implementation. - Includes examples from a wide variety of businesses and situations presented in an accessible writing style. |
| data and risk management: Risk Management Antonio Borghesi, Barbara Gaudenzi, 2012-10-06 Businesses now operate amid a welter of risks that exist at various levels, both inside companies and at the network level. This handbook provides the latest integrated managerial approaches that help protect businesses from adverse events and their effects. |
| data and risk management: Practical Enterprise Risk Management Gregory H. Duckert, 2010-10-12 The most practical and sensible way to implement ERM-while avoiding all of the classic mistakes Emphasizing an enterprise risk management approach that utilizes actual business data to estimate the probability and impact of key risks in an organization, Practical Enterprise Risk Management: A Business Process Approach boils this topic down to make it accessible to both line managers and high level executives alike. The key lessons involve basing risk estimates and prevention techniques on known quantities rather than subjective estimates, which many popular ERM methodologies consist of. Shows readers how to look at real results and actual business processes to get to the root cause of key risks Explains how to manage risks based on an understanding of the problem rather than best guess estimates Emphasizes a focus on potential outcomes from existing processes, as well as a look at actual outcomes over time Throughout, practical examples are included from various healthcare, manufacturing, and retail industries that demonstrate key concepts, implementation guidance to get started, as well as tables of risk indicators and metrics, physical structure diagrams, and graphs. |
| data and risk management: Credit Risk Analytics Bart Baesens, Daniel Roesch, Harald Scheule, 2016-10-03 The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process. |
| data and risk management: Winning With Risk Management Russell Walker, 2013-04-04 This book develops the notion that companies can succeed on the basis of risk management, much as companies compete on efficiency, costs, labor, location, and other dimensions. The reality of risk and how it impacts companies is that it is much more definite, often catastrophic and looks more like a shock. This is striking, as a difference between firms on risk different than a marginal difference in operating efficiencies, for example. Competing on Risk Management requires a discipline, a commitment to using information and recognizing shocks and then acting upon those to redistribute assets. This book will examine how leading firms that compete on risk have done this and showcase best practices and impacts to the capital structure of firms and their organizational formation. |
| data and risk management: Privacy is Power Carissa Veliz, 2021-04-06 An Economist Book of the Year Every minute of every day, our data is harvested and exploited… It is time to pull the plug on the surveillance economy. Governments and hundreds of corporations are spying on you, and everyone you know. They're not just selling your data. They're selling the power to influence you and decide for you. Even when you've explicitly asked them not to. Reclaiming privacy is the only way we can regain control of our lives and our societies. These governments and corporations have too much power, and their power stems from us--from our data. Privacy is as collective as it is personal, and it's time to take back control. Privacy Is Power tells you how to do exactly that. It calls for the end of the data economy and proposes concrete measures to bring that end about, offering practical solutions, both for policymakers and ordinary citizens. |
| data and risk management: Information Security Risk Assessment Toolkit Mark Talabis, Jason Martin, 2012-10-26 In order to protect company's information assets such as sensitive customer records, health care records, etc., the security practitioner first needs to find out: what needs protected, what risks those assets are exposed to, what controls are in place to offset those risks, and where to focus attention for risk treatment. This is the true value and purpose of information security risk assessments. Effective risk assessments are meant to provide a defendable analysis of residual risk associated with your key assets so that risk treatment options can be explored. Information Security Risk Assessment Toolkit gives you the tools and skills to get a quick, reliable, and thorough risk assessment for key stakeholders. Based on authors' experiences of real-world assessments, reports, and presentations Focuses on implementing a process, rather than theory, that allows you to derive a quick and valuable assessment Includes a companion web site with spreadsheets you can utilize to create and maintain the risk assessment |
| data and risk management: Information Risk Management David Sutton, 2014 Information risk management (IRM) is about identifying, assessing and prioritising risks to keep information secure and available. This accessible book is a practical guide to understanding the principles of IRM and developing a strategic approach to an IRM programme. It also includes a chapter on applying IRM in the public sector. It is the only textbook for the BCS Practitioner Certificate in Information Risk Management. |
| data and risk management: Essentials of Modeling and Analytics David B. Speights, Daniel M. Downs, Adi Raz, 2017-09-11 Essentials of Modeling and Analytics illustrates how and why analytics can be used effectively by loss prevention staff. The book offers an in-depth overview of analytics, first illustrating how analytics are used to solve business problems, then exploring the tools and training that staff will need in order to engage solutions. The text also covers big data analytical tools and discusses if and when they are right for retail loss prevention professionals, and illustrates how to use analytics to test the effectiveness of loss prevention initiatives. Ideal for loss prevention personnel on all levels, this book can also be used for loss prevention analytics courses. Essentials of Modeling and Analytics was named one of the best Analytics books of all time by BookAuthority, one of the world's leading independent sites for nonfiction book recommendations. |
| data and risk management: Why Don't We Defend Better? Robert Sloan, Richard Warner, 2019-07-05 The wave of data breaches raises two pressing questions: Why don’t we defend our networks better? And, what practical incentives can we create to improve our defenses? Why Don't We Defend Better?: Data Breaches, Risk Management, and Public Policy answers those questions. It distinguishes three technical sources of data breaches corresponding to three types of vulnerabilities: software, human, and network. It discusses two risk management goals: business and consumer. The authors propose mandatory anonymous reporting of information as an essential step toward better defense, as well as a general reporting requirement. They also provide a systematic overview of data breach defense, combining technological and public policy considerations. Features Explains why data breach defense is currently often ineffective Shows how to respond to the increasing frequency of data breaches Combines the issues of technology, business and risk management, and legal liability Discusses the different issues faced by large versus small and medium-sized businesses (SMBs) Provides a practical framework in which public policy issues about data breaches can be effectively addressed |
| data and risk management: Cyber-Risk Management Atle Refsdal, Bjørnar Solhaug, Ketil Stølen, 2015-10-01 This book provides a brief and general introduction to cybersecurity and cyber-risk assessment. Not limited to a specific approach or technique, its focus is highly pragmatic and is based on established international standards (including ISO 31000) as well as industrial best practices. It explains how cyber-risk assessment should be conducted, which techniques should be used when, what the typical challenges and problems are, and how they should be addressed. The content is divided into three parts. First, part I provides a conceptual introduction to the topic of risk management in general and to cybersecurity and cyber-risk management in particular. Next, part II presents the main stages of cyber-risk assessment from context establishment to risk treatment and acceptance, each illustrated by a running example. Finally, part III details four important challenges and how to reasonably deal with them in practice: risk measurement, risk scales, uncertainty, and low-frequency risks with high consequence. The target audience is mainly practitioners and students who are interested in the fundamentals and basic principles and techniques of security risk assessment, as well as lecturers seeking teaching material. The book provides an overview of the cyber-risk assessment process, the tasks involved, and how to complete them in practice. |
| data and risk management: Elements of Financial Risk Management Peter Christoffersen, 2011-11-22 The Second Edition of this best-selling book expands its advanced approach to financial risk models by covering market, credit, and integrated risk. With new data that cover the recent financial crisis, it combines Excel-based empirical exercises at the end of each chapter with online exercises so readers can use their own data. Its unified GARCH modeling approach, empirically sophisticated and relevant yet easy to implement, sets this book apart from others. Five new chapters and updated end-of-chapter questions and exercises, as well as Excel-solutions manual, support its step-by-step approach to choosing tools and solving problems. Examines market risk, credit risk, and operational risk Provides exceptional coverage of GARCH models Features online Excel-based empirical exercises |
| data and risk management: The Failure of Risk Management Douglas W. Hubbard, 2009-04-27 An essential guide to the calibrated risk analysis approach The Failure of Risk Management takes a close look at misused and misapplied basic analysis methods and shows how some of the most popular risk management methods are no better than astrology! Using examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and more, Hubbard reveals critical flaws in risk management methods–and shows how all of these problems can be fixed. The solutions involve combinations of scientifically proven and frequently used methods from nuclear power, exploratory oil, and other areas of business and government. Finally, Hubbard explains how new forms of collaboration across all industries and government can improve risk management in every field. Douglas W. Hubbard (Glen Ellyn, IL) is the inventor of Applied Information Economics (AIE) and the author of Wiley's How to Measure Anything: Finding the Value of Intangibles in Business (978-0-470-11012-6), the #1 bestseller in business math on Amazon. He has applied innovative risk assessment and risk management methods in government and corporations since 1994. Doug Hubbard, a recognized expert among experts in the field of risk management, covers the entire spectrum of risk management in this invaluable guide. There are specific value-added take aways in each chapter that are sure to enrich all readers including IT, business management, students, and academics alike —Peter Julian, former chief-information officer of the New York Metro Transit Authority. President of Alliance Group consulting In his trademark style, Doug asks the tough questions on risk management. A must-read not only for analysts, but also for the executive who is making critical business decisions. —Jim Franklin, VP Enterprise Performance Management and General Manager, Crystal Ball Global Business Unit, Oracle Corporation. |
| data and risk management: Risk Management for Enterprises and Individuals Baranoff, Patrick L. Brockett, Yehuda Kahane, 2009 |
| data and risk management: Risk Assessment in the Federal Government National Research Council, Division on Earth and Life Studies, Commission on Life Sciences, Committee on the Institutional Means for Assessment of Risks to Public Health, 1983-02-01 The regulation of potentially hazardous substances has become a controversial issue. This volume evaluates past efforts to develop and use risk assessment guidelines, reviews the experience of regulatory agencies with different administrative arrangements for risk assessment, and evaluates various proposals to modify procedures. The book's conclusions and recommendations can be applied across the entire field of environmental health. |
| data and risk management: Risk Assessment Marvin Rausand, Stein Haugen, 2020-03-31 Introduces risk assessment with key theories, proven methods, and state-of-the-art applications Risk Assessment: Theory, Methods, and Applications remains one of the few textbooks to address current risk analysis and risk assessment with an emphasis on the possibility of sudden, major accidents across various areas of practice—from machinery and manufacturing processes to nuclear power plants and transportation systems. Updated to align with ISO 31000 and other amended standards, this all-new 2nd Edition discusses the main ideas and techniques for assessing risk today. The book begins with an introduction of risk analysis, assessment, and management, and includes a new section on the history of risk analysis. It covers hazards and threats, how to measure and evaluate risk, and risk management. It also adds new sections on risk governance and risk-informed decision making; combining accident theories and criteria for evaluating data sources; and subjective probabilities. The risk assessment process is covered, as are how to establish context; planning and preparing; and identification, analysis, and evaluation of risk. Risk Assessment also offers new coverage of safe job analysis and semi-quantitative methods, and it discusses barrier management and HRA methods for offshore application. Finally, it looks at dynamic risk analysis, security and life-cycle use of risk. Serves as a practical and modern guide to the current applications of risk analysis and assessment, supports key standards, and supplements legislation related to risk analysis Updated and revised to align with ISO 31000 Risk Management and other new standards and includes new chapters on security, dynamic risk analysis, as well as life-cycle use of risk analysis Provides in-depth coverage on hazard identification, methodologically outlining the steps for use of checklists, conducting preliminary hazard analysis, and job safety analysis Presents new coverage on the history of risk analysis, criteria for evaluating data sources, risk-informed decision making, subjective probabilities, semi-quantitative methods, and barrier management Contains more applications and examples, new and revised problems throughout, and detailed appendices that outline key terms and acronyms Supplemented with a book companion website containing Solutions to problems, presentation material and an Instructor Manual Risk Assessment: Theory, Methods, and Applications, Second Edition is ideal for courses on risk analysis/risk assessment and systems engineering at the upper-undergraduate and graduate levels. It is also an excellent reference and resource for engineers, researchers, consultants, and practitioners who carry out risk assessment techniques in their everyday work. |
| data and risk management: Financial Risk Management Francisco Javier Población García, 2017-02-16 This book provides a quantitative overview of corporate risk management for both financial and non-financial organisations. It systematically explores a range of important risks, including interest rate risk, equity risk, commodity price risk, credit risk management, counterparty risk, operational risk, liquidity risk, market risk, derivative credit risk and country risk. Chapters also provide comprehensive and accessible analysis of risk-related phenomena and the corporate strategies employed to minimise the impacts of risk in each case. Chapters begin with an explanation of basic concepts and terminology, before going on to present quantitative examples and qualitative discussion sections. The author leverages his lifetime’s experience of working in risk management to offer this clear and empirical guide for scholars and practitioners researching financial stability. |
| data and risk management: Risk Analytics: From Concept To Deployment Edward Hon Khay Ng, 2021-10-04 This book is written to empower risk professionals to turn analytics and models into deployable solutions with minimal IT intervention. Corporations, especially financial institutions, must show evidence of having quantified credit, market and operational risks. They have databases but automating the process to translate data into risk parameters remains a desire.Modelling is done using software with output codes not readily processed by databases. With increasing acceptance of open-source languages, database vendors have seen the value of integrating modelling capabilities into their products. Nevertheless, deploying solutions to automate processes remains a challenge. While not comprehensive in dealing with all facets of risks, the author aims to develop risk professionals who will be able to do just that. |
| data and risk management: The Owner's Role in Project Risk Management National Research Council, Division on Engineering and Physical Sciences, Board on Infrastructure and the Constructed Environment, Committee for Oversight and Assessment of U.S. Department of Energy Project Management, 2005-02-25 Effective risk management is essential for the success of large projects built and operated by the Department of Energy (DOE), particularly for the one-of-a-kind projects that characterize much of its mission. To enhance DOE's risk management efforts, the department asked the NRC to prepare a summary of the most effective practices used by leading owner organizations. The study's primary objective was to provide DOE project managers with a basic understanding of both the project owner's risk management role and effective oversight of those risk management activities delegated to contractors. |
| data and risk management: Risk and Security Management Michael Blyth, 2015-05-14 Learn to measure risk and develop a plan to protect employees and company interests by applying the advice and tools in Risk and Security Management: Protecting People and Sites Worldwide. In a world concerned with global terrorism, instability of emerging markets, and hazardous commercial operations, this book shines as a relevant and timely text with a plan you can easily apply to your organization. Find a series of strategic to granular level policies, systems, and concepts which identify and address risk, enabling business to occur in a manner which best protects you and your company. |
| data and risk management: Risk Management Treatise for Engineering Practitioners Chike F Oduoza, 2019-04-23 This book Risk Management Treatise for Engineering Practitioners has been published by academic researchers and experts on risk management concepts mainly in the construction engineering sector. It addresses basic theories and principles of risk management backed up, in most cases, with case studies. The contributions for this book came from authors in Europe, the Far East and Africa, and it is hoped that the contents of this book will be useful to anyone interested in understanding the principles and applications of risk management, especially within the construction engineering sector. Researchers and postgraduate students in science and engineering disciplines, especially those interested in project management, will find this book useful. |
| data and risk management: Risk Management and Governance Terje Aven, Ortwin Renn, 2010-09-27 Risk is a popular topic in many sciences - in natural, medical, statistical, engineering, social, economic and legal disciplines. Yet, no single discipline can grasp the full meaning of risk. Investigating risk requires a multidisciplinary approach. The authors, coming from two very different disciplinary traditions, meet this challenge by building bridges between the engineering, the statistical and the social science perspectives. The book provides a comprehensive, accessible and concise guide to risk assessment, management and governance. A basic pillar for the book is the risk governance framework proposed by the International Risk Governance Council (IRGC). This framework offers a comprehensive means of integrating risk identification, assessment, management and communication. The authors develop and explain new insights and add substance to the various elements of the framework. The theoretical analysis is illustrated by several examples from different areas of applications. |
| data and risk management: Data Protection Implementation Guide Brendan Quinn, 2021-09-02 The complexities of implementing the General Data Protection Regulation (GDPR) continue to grow as it progresses through new and ever-changing technologies, business models, codes of conduct, and decisions of the supervisory authorities, and the courts. This eminently practical guide to implementing the GDPR – written in an original, problem-solving style by a highly experienced data protection expert with equal knowledge of both law and technology – provides a step-by-step project management approach to building a GDPR-compliant data protection system, assessing, and documenting the risks and then implementing these changes through processes at the operational level. With detailed attention to case law (Member State, ECJ, and ECHR), especially where affecting high-risk areas that have attracted scrutiny, the guidance proceeds systematically through such topics and issues as the following: required documentation, policies, and procedures; risk assessment tools and analysis frameworks; children’s data; employee and health data; international transfers post-Schrems II; data subject rights including the right of access; data retention and erasure; tracking and surveillance; and effects of technologies such as artificial intelligence, biometrics, and machine learning. With its practical examples derived from the author’s experience in building GDPR-compliant software, as well as its analysis of case law and enforcement priorities, this incomparable guide enables company data protection officers and compliance staff to advise on key issues with full awareness of the legal and reputational risks and how to mitigate them. It is also sure to be of immeasurable value to concerned regulators and policymakers at all government levels. “…it's going to be the go to resource for practitioners.” Tom Gilligan, Data Protection Consultant, September 2021 I purchased this book recently and I’m very glad I did. It’s the textbook I have been waiting for. As someone relatively new to data protection, I was finding it very difficult to find books on the practical side of data protection. This book is very clearly laid out with practical examples and case law given for each topic, which is immensely helpful. I would recommend it to any data protection practitioners. Jennifer Breslin, LLM CIPP/E, AIPP Member |
| data and risk management: The Practice of Risk Management , 1998 This title is designed to be accessible to both technical and non-technical readers. The Practice of Risk Management is unique in its presentation of information and techniques indispensible to any form aspiring to efficient risk management. |
| data and risk management: Knowledge Risk Management Susanne Durst, Thomas Henschel, 2020-02-04 This book provides an in-depth introduction to knowledge risk management (KRM) as well as methods, tools and cases to address knowledge risk management issues in both the public and private sector. It focuses on the integration of knowledge risks into the holistic risk management of organizations. In addition, this book is accompanied by an external website that includes additional checklists, videos and company cases. The combination of a sound theoretical framework along with practical instruments, tools and ancillary materials makes this book a unique, interactive book for professionals, managers, and executives as well as students, academics and policy makers. |
| data and risk management: Financial Risk Management Jimmy Skoglund, Wei Chen, 2015-09-04 A global banking risk management guide geared toward the practitioner Financial Risk Management presents an in-depth look at banking risk on a global scale, including comprehensive examination of the U.S. Comprehensive Capital Analysis and Review, and the European Banking Authority stress tests. Written by the leaders of global banking risk products and management at SAS, this book provides the most up-to-date information and expert insight into real risk management. The discussion begins with an overview of methods for computing and managing a variety of risk, then moves into a review of the economic foundation of modern risk management and the growing importance of model risk management. Market risk, portfolio credit risk, counterparty credit risk, liquidity risk, profitability analysis, stress testing, and others are dissected and examined, arming you with the strategies you need to construct a robust risk management system. The book takes readers through a journey from basic market risk analysis to major recent advances in all financial risk disciplines seen in the banking industry. The quantitative methodologies are developed with ample business case discussions and examples illustrating how they are used in practice. Chapters devoted to firmwide risk and stress testing cross reference the different methodologies developed for the specific risk areas and explain how they work together at firmwide level. Since risk regulations have driven a lot of the recent practices, the book also relates to the current global regulations in the financial risk areas. Risk management is one of the fastest growing segments of the banking industry, fueled by banks' fundamental intermediary role in the global economy and the industry's profit-driven increase in risk-seeking behavior. This book is the product of the authors' experience in developing and implementing risk analytics in banks around the globe, giving you a comprehensive, quantitative-oriented risk management guide specifically for the practitioner. Compute and manage market, credit, asset, and liability risk Perform macroeconomic stress testing and act on the results Get up to date on regulatory practices and model risk management Examine the structure and construction of financial risk systems Delve into funds transfer pricing, profitability analysis, and more Quantitative capability is increasing with lightning speed, both methodologically and technologically. Risk professionals must keep pace with the changes, and exploit every tool at their disposal. Financial Risk Management is the practitioner's guide to anticipating, mitigating, and preventing risk in the modern banking industry. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Managing the risks and opportunities of generative AI - PwC
that, you will need a risk management frameworkthat also allows you to embraceopportunity. A risk-based approach to generative AI will start you on the right digital foot with regulators, …
Quantitative Risk Management for Healthcare Cybersecurity
• Case Study: Mayo Clinic Supply Chain Risk Management • Data Breaches from 2019 Verizon Data Breach Investigation Report • Legislation, Regulations and Standards Non-Technical: …
Data Quality Management and Financial Services
• Problem / data ownership - Risk operations - Data providers • Multitudes of Log files • Data didn’t arrive • Data entry errors • Loose rules on source systems • Data consistency errors • File …
CPG 235 – Managing Data Risk - Australian Prudential …
that data risk management and related business processes are properly designed and operating effectively to meet the needs of the regulated entity. In APRA’s view, effective governance of …
Methodology for Privacy Risk Management - English version
1. Theory: risk management concepts . Risk management is used in many areas (information security, safety, finance, insurance…). This chapter provides an implementation of this …
WHITE PAPER Federated Data Governance Model - Boston …
mesh or de-centralized governance models can ensure data management practices create business value swiftly, and effectively manage business risk. Data governance design should …
Model Risk Management toolkit - KPMG
Model Risk Management function and framework within a financial entity. The guidance aims to help banks understand the ... Identify all risks associated to data, methodology, implementation …
Embracing Data-Driven Risk Management in 2022 - Moody's
Three Ways Enterprise Risk Management is Evolving Data-focused executives are helping enterprise risk functions use data in sophisticated ways to inform their strategic decisions. …
Data Risk Management Report 2023 - India - Veritas
DATA RISK MANAGEMENT The State of the Market—Cyber to Compliance Research conducted by: INTRODUCTION The ability to manage risk is a foundation of a successful organization. …
Heightened Risk Standards: Focus on Data Management
services companies’ data management and data governance practices over risk management data, from aggregation capabilities to internal risk reporting practices. This focus on RDARR …
Enterprise risk management (ERM): The modern approach to …
they face have grown increasingly sophisticated since the term “enterprise risk management” (ERM) was first used in the late 1990s. While the common definition of ERM still holds—an ...
OCC Raises Expectations of Model, Data, and Tools …
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Cybersecurity of Genomic Data - NIST
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UNITED STATES OF AMERICA DEPARTMENT OF THE …
Jul 9, 2024 · $75 million, $75,000,000, enterprise-wide risk management, compliance risk management, data governance, internal controls, persistent weakness, data quality Created …
A Case Study of the Capital One Data Breach
Sloan School of Management, Room E62-422 Massachusetts Institute of Technology Cambridge, MA 02142 ... data records breached increased from 4.3 billion in 2018 to over 11.5 billion in …
Elements of Financial Risk Management - ndl.ethernet.edu.et
risk management: first, master’s and Ph.D. students specializing in finance and economics; second, market practitioners with a quantitative undergraduate or ... Updates to the material in …
Quantitative Risk Management for Healthcare Cybersecurity
May 7, 2020 · • Case Study: Mayo Clinic Supply Chain Risk Management • Data Breaches from 2019 Verizon Data Breach Investigation Report • Legislation, Regulations and Standards Non …
Optimizing data controls in banking - McKinsey & Company
Dimensions and characterizations in data program scope Main risk types: market, credit, operational Material legal entities Bank holding company Risk Board and/or senior …
Data Privacy Handbook - PwC
Data privacy laws draw a clear distinction between data ‘controllers’ and data ‘processors’ to recognise that not all organisations involved with the processing of personal data have the …
Risk and control self-assessment: What s next? - KPMG
found popularity in the hazard risk management focused industries such as outer space and motor racing. ‘Digital Twins’ are virtual simulations of a physical product, process or system …
Risk management guidance - GOV.UK
Risk Management in DFID Introduction 1. Risk management is important: it enables DFID to be innovative and to avoid disasters. But, like all management, it has to be done well. …
Data Security in Financial Services - Financial Conduct …
responsibly and effectively, with adequate risk management systems’. 4. In line with these principles, firms’ senior management are responsible for making an appropriate assessment of …
Risk Management, a Practical Guide - MSCI
8.6 What is good market data 100 8.7 The task of the risk data analyst 101 8.8 Where to get market risk data 102 8.9 Summary 102 Chapter 9. Position data for risk mapping 105 9.1 The …
Big Data and Insurance: Implications for Innovation, …
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Jamaica's Comprehensive DRM Policy. JULY 22 - Office of …
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Circular 2023/1 Operational risks and resilience banks
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SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT
banks have been increasing the use of data-driven, quantitative decision-making tools for a number of years. ... model risk management for specific types of models or pay particular …
Implications of Big Data for Customs - How It Can Support …
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DAMA-DMBOK Functional Framework - Governance …
In the Information Age, the Data Management function is vital to every organization. Whether known as Data Management, Data Resource Management or Enterprise Information …
Risk Management Framework for Information Systems …
chain risk management processes Organizations can . use the frameworks and processes in a complementary ... data, proof of concept implementations, and technical analyses to advance …
Global and industry frameworks for data governance - PwC
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February 2024 Heightened Risk Standards: Focus on Risk
of processes in such areas as internal controls, data management, change management, issues management. As part of the current focus on heightened risk governance and risk …
The future of bank risk management - McKinsey & Company
The future of bank risk management 5 Risk management in banks has changed substantially over the past ten years. The regulations that emerged from the global financial crisis and the fines …
Data privacy as a strategic priority - Deloitte United States
attention and resources on managing data privacy risk: • Regulatory requirements: Data privacy and cybersecurity rules not only require the protection of customer data, they impose …
Identifying and Estimating Cybersecurity Risk for Enterprise …
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Ready-to-Use KRI Examples - Wiley Online Library
broader risk management and decision-making processes. KRI Title: Number of Data Breaches Specific Risk: Unauthorized access and potential exfiltration of sensitive data. Metric Summary: …
Data Analytics Based Risk Management for Students
Data Analytics Based Risk Management for Students’ Performance – A Case Study M. Somasundaram a,1, K.A. Mohamed Junaid a, D. Sudha b and Sabari L. Umamaheswari c a …
Guide on effective risk data aggregation and risk reporting
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Bank Negara, Malaysia Policy Document Risk Management …
The Risk Management in Technology (RMiT) Compliance issued by Bank Negara Malaysia applies to all financial institutions operating within Malaysia. This includes banks, insurance …
ARTIFICIAL INTELLIGENCE MODEL RISK MANAGEMENT
6.2 Data Management 18 6.3 Development 21 6.4 Validation 30 6.5 Deployment, Monitoring and Change Management 31 7 Other Key Areas 36 7.1 Generative AI 36 7.2 Third-Party AI 43 8 …
(U) RISK MANAGEMENT FRAMEWORK DOCUMENTATION, …
National Security Directive 42 (Reference 1), is issuing this Instruction 1254, Risk Management Framework Documentation, Data Element Standards, and Reciprocity Process for National …
The value in digitally transforming credit risk management
specify requirements for data management and the accuracy and timeliness of the data used in stress testing. 1 Exhibit 1 Five trends are altering the current risk-management model and …
GOOD PRACTICES FOR DATA MANAGEMENT AND …
communicate their data integrity risk management activities in a coherent manner. Absence of a data governance system may indicate uncoordinated data integrity systems, with potential for …
Data management trends in the financial services sector
Data risk, health, and resiliency Cyber and data risk remains a top priority. The number, sophistication, and financial and reputational impact of cybersecurity breaches continue to …
Artificial Intelligence Risk Management Framework: …
risk management efforts. To help streamline risk management efforts, each risk is mapped in Section 3 (as well as in tables in Appendix B) to relevant Trustworthy AI Characteristics …
Veritas Data Risk Management Infographic 2023
DATA RISK MANAGEMENT ˚˛˚˝ GLOBAL REVIEW The State of the Market— Cyber to Compliance Organizations and their employees need to balance data risk with their roles and …
Integrating Cybersecurity and Enterprise Risk Management …
business objectives. Focusing on the use of risk registers to set out cybersecurity risk, this document explains the value of rolling up measures of risk usually addressed at lower system …
CPS 230 Operational Risk Management July 2023 - KPMG
risk profile reporting. Key data should be identified and ensure data risk is managed appropriately. • Information systems enable real time and aggregated reporting and integrate risk data across …
HANDBOOK OF FINANCIAL DATA AND RISK INFORMATION
Risk management data and information for improved insight 328 Margarita S. Brose, Mark D. Flood and David M. Rowe PART III REGULATORY DATA 381 Margarita S. Brose and Mark D. …
KPMG Whitepaper Model Risk Management
follow-up management actions. A model risk management framework should consist of the following components: Model governance. Usually, model risk management is carried out …
