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| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: Enterprise Risk Management Stefan Hunziker, 2021-05-18 This textbook demonstrates how Enterprise Risk Management creates value in strategic- and decision-making-processes. The author introduces modern approaches to balancing risk and reward based on many examples of medium-sized and large companies from different industries. Since traditional risk management in practice is often an independent stand-alone process with no impact on decision-making processes, it is unable to create value and ties up resources in the company unnecessarily. Herewith, he serves students as well as practitioners with modern approaches that promote a connection between ERM and corporate management. The author demonstrates in a didactically appropriate manner how companies can use ERM in a concrete way to achieve better risk-reward decisions under uncertainty. Furthermore, theoretical and psychological findings relevant to entrepreneurial decision-making situations are incorporated. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: Enterprise Risk Management James Lam, 2014-01-06 A fully revised second edition focused on the best practices of enterprise risk management Since the first edition of Enterprise Risk Management: From Incentives to Controls was published a decade ago, much has changed in the worlds of business and finance. That's why James Lam has returned with a new edition of this essential guide. Written to reflect today's dynamic market conditions, the Second Edition of Enterprise Risk Management: From Incentives to Controls clearly puts this discipline in perspective. Engaging and informative, it skillfully examines both the art as well as the science of effective enterprise risk management practices. Along the way, it addresses the key concepts, processes, and tools underlying risk management, and lays out clear strategies to manage what is often a highly complex issue. Offers in-depth insights, practical advice, and real-world case studies that explore the various aspects of ERM Based on risk management expert James Lam's thirty years of experience in this field Discusses how a company should strive for balance between risk and return Failure to properly manage risk continues to plague corporations around the world. Don't let it hurt your organization. Pick up the Second Edition of Enterprise Risk Management: From Incentives to Controls and learn how to meet the enterprise-wide risk management challenge head on, and succeed. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: A Notion of Enterprise Risk Management Soumi Majumder, Nilanjan Dey, 2024-07-17 Soumi Majumder and Nilanjan Dey address the unique challenges posed by Industry 4.0, exploring the intersection of risks and cultural shifts within the business landscape. Key topics include the transformative potential of machine learning; big data; and IoT in the domain of enterprise risk management. |
| enterprise risk management data analytics: Problem, Risk, and Opportunity Enterprise Management Brian Hagen, 2018-08 Dr. Hagen presents a complete system by which companies can more easily and consistently manage their portfolio of problems, risks, and opportunities. His methodology was based on a foundation of neuroscience and logical decision analytics. |
| enterprise risk management data analytics: Analytics Across the Enterprise Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton, 2014-05-15 How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics |
| enterprise risk management data analytics: Enterprise Risk Management John R. S. Fraser, Rob Quail, Betty Simkins, 2021-06-04 Unlock the incredible potential of enterprise risk management There has been much evolution in terms of ERM best practices, experience, and standards and regulation over the past decade. Enterprise Risk Management: Today’s Leading Research and Best Practices for Tomorrow’s Executives, Second Edition is the revised and updated essential guide to the now immensely popular topic of enterprise risk management (ERM). With contributions from leading academics and practitioners, this book offers insights into what practitioners are doing and what the future holds. You’ll discover how you can implement best practices, improve ERM tools and techniques, and even learn to teach ERM. Retaining the holistic approach to ERM that made the first edition such a success, this new edition adds coverage of new topics including cybersecurity risk, ERM in government, foreign exchange risk, risk appetite, innovation risk, outsourcing risk, scenario planning, climate change risk, and much more. In addition, the new edition includes important updates and enhancements to topics covered in the first edition; so much of it has been revised and enhanced that it is essentially an entirely new book. Enterprise Risk Management introduces you to the concepts and techniques that allow you to identify risks and prioritize the appropriate responses. This invaluable guide offers a broad overview, covering key issues while focusing on the principles that drive effective decision making and determine business success. This comprehensive resource also provides a thorough introduction to ERM as it relates to credit, market, and operational risk, as well as the evolving requirements of the board of directors’ role in overseeing ERM. Through the comprehensive chapters and leading research and best practices covered, this book: Provides a holistic overview of key topics in ERM, including the role of the chief risk officer, development and use of key risk indicators and the risk-based allocation of resources Contains second-edition updates covering additional material related to teaching ERM, risk frameworks, risk culture, credit and market risk, risk workshops and risk profiles and much more. Over 90% of the content from the first edition has been revised or enhanced Reveals how you can prudently apply ERM best practices within the context of your underlying business activities Filled with helpful examples, tables, and illustrations, Enterprise Risk Management, Second Edition offers a wealth of knowledge on the drivers, the techniques, the benefits, as well as the pitfalls to avoid, in successfully implementing ERM. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: Enterprise Risk Analytics for Capital Markets Raghurami Reddy Etukuru, 2014-10-09 While quantitative models can help predict the trends in Capital Markets, forecasts dont always hold up and can quickly cause things to spiral out of control and can lead to global risk. In order to reduce systemic risk, the G20 committed to a fundamental reform of the financial system, to correct the fault lines, and to rebuild the financial system as a safer, more resilient source of finance that better serves the real economy. This requires Financial Institutions to develop sound Risk Management practices. In straightforward language, youll learn about key components of risk management, including risk knowledge, risk quantification, risk data management, risk data aggregation, risk architectures, risk analytics and reporting, risk regulation. Youll also get definitions explaining how different financial products work, mathematical formulas with explanations, and insights on different asset classes, different approaches to hedging, and much more. This book Enterprise Risk Analytics for Capital Markets will help whether you are just beginning a career in risk management or advancing your career with in risk management. |
| enterprise risk management data analytics: Enterprise Risk Management in Today’s World Jean-Paul Louisot, 2024-10-28 Enterprise Risk Management in Today’s World examines enterprise risk management in its past, present and future, exploring the role that directors and leaders in organizations have in devising risk management strategies, analysing values such as trust, resilience, CSR and governance within organizations. |
| enterprise risk management data analytics: ERM - Enterprise Risk Management Jean-Paul Louisot, Christopher H. Ketcham, 2014-03-25 A wealth of international case studies illustrating current issues and emerging best practices in enterprise risk management Despite enterprise risk management's relative newness as a recognized business discipline, the marketplace is replete with guides and references for ERM practitioners. Yet, until now, few case studies illustrating ERM in action have appeared in the literature. One reason for this is that, until recently, there were many disparate, even conflicting definitions of what, exactly ERM is and, more importantly, how organizations can use it to utmost advantage. With efforts underway, internationally, to mandate ERM and to standardize ERM standards and practices, the need has never been greater for an authoritative resource offering risk management professionals authoritative coverage of the full array of contemporary ERM issues and challenges. Written by two recognized international thought leaders in the field, ERM-Enterprise Risk Management provides that and much more. Packed with international cases studies illustrating ERM best practices applicable across all industry sectors and business models Explores contemporary issues, including quantitative and qualitative measures, as well as potential pitfalls and challenges facing today's enterprise risk managers Includes interviews with leading risk management theorists and practitioners, as well as risk managers from a variety of industries An indispensable working resource for risk management practitioners everywhere and a valuable reference for researchers, providing the latest empirical evidence and an exhaustive bibliography |
| enterprise risk management data analytics: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-09-26 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. |
| enterprise risk management data analytics: Business Intelligence Jerzy Surma, 2011-03-06 This book is about using business intelligence as a management information system for supporting managerial decision making. It concentrates primarily on practical business issues and demonstrates how to apply data warehousing and data analytics to support business decision making. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. All examples are based on the most recent achievements in business intelligence. Finally this book outlines an overview of a methodology that takes into account the complexity of developing applications in an integrated business intelligence environment. This book is written for managers, business consultants, and undergraduate and postgraduates students in business administration. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: From Big Data to Big Profits Russell Walker, 2015-07-01 Technological advancements in computing have changed how data is leveraged by businesses to develop, grow, and innovate. In recent years, leading analytical companies have begun to realize the value in their vast holdings of customer data and have found ways to leverage this untapped potential. Now, more firms are following suit and looking to monetize Big Data for big profits. Such changes will have implications for both businesses and consumers in the coming years. In From Big Data to Big Profits, Russell Walker investigates the use of Big Data to stimulate innovations in operational effectiveness and business growth. Walker examines the nature of Big Data and how businesses can use it to create new monetization opportunities. Using case studies of Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leaders in the use of Big Data, Walker explores how digital platforms such as mobile apps and social networks are changing the nature of customer interactions and the way Big Data is created and used by companies. Such changes, as Walker points out, will require careful consideration of legal and unspoken business practices as they affect consumer privacy. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which he has developed to assess companies for Big Data readiness and provide direction on the steps necessary to get the most from Big Data. Rigorous and meticulous, From Big Data to Big Profits is a valuable resource for students, researchers, and professionals with an interest in Big Data, digital platforms, and analytics |
| enterprise risk management data analytics: Implementing Enterprise Risk Management James Lam, 2017-03-09 A practical, real-world guide for implementing enterprise risk management (ERM) programs into your organization Enterprise risk management (ERM) is a complex yet critical issue that all companies must deal with in the twenty-first century. Failure to properly manage risk continues to plague corporations around the world. ERM empowers risk professionals to balance risks with rewards and balance people with processes. But to master the numerous aspects of enterprise risk management, you must integrate it into the culture and operations of the business. No one knows this better than risk management expert James Lam, and now, with Implementing Enterprise Risk Management: From Methods to Applications, he distills more than thirty years' worth of experience in the field to give risk professionals a clear understanding of how to implement an enterprise risk management program for every business. Offers valuable insights on solving real-world business problems using ERM Effectively addresses how to develop specific ERM tools Contains a significant number of case studies to help with practical implementation of an ERM program While Enterprise Risk Management: From Incentives to Controls, Second Edition focuses on the what of ERM, Implementing Enterprise Risk Management: From Methods to Applications will help you focus on the how. Together, these two resources can help you meet the enterprise-wide risk management challenge head on—and succeed. |
| enterprise risk management data analytics: 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. |
| enterprise risk management data analytics: Enterprise Risk Management in the Fourth Industrial Revolution Tankiso Moloi, Tshilidzi Marwala, 2023-11-30 This book examines enterprise risk management in the fourth industrial revolution, and the technologies associated with this phenomenon. In doing so, it seeks to understand these technologies' potential capabilities, and how they could be utilised in the enterprise risk management setting. With this, the book first details the fourth industrial revolution (4IR), and discusses the concept of enterprise risk management, the stakeholders involved, the typical information stakeholders will be responsible for, and their role in integrating risk management information. The book then examines the information processing steps and the new capabilities in the enterprise risk setting necessitated by the capabilities of the 4IR technologies to harness, analyse and integrate information for decision-making and understanding internal and external contexts. In the final chapter, the book conceptualises enterprise risk management in the 4IR, and maps out potential role changes in this space. |
| enterprise risk management data analytics: Risk Management in Business: Identifying and Mitigating Risks , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| enterprise risk management data analytics: Data-Driven Modelling and Predictive Analytics in Business and Finance Alex Khang, Rashmi Gujrati, Hayri Uygun, R. K. Tailor, Sanjaya Gaur, 2024-07-24 Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices. |
| enterprise risk management data analytics: Intelligent Systems and Applications Kohei Arai, |
| enterprise risk management data analytics: Enterprise Risk Management Mirna Jabbour, Jason Crawford, 2024-12-02 ERM is considered a dynamic capability that is critical to companies’ success from strategic and performance perspectives and is increasingly implemented in response to growing pressure from external stakeholders to enact and add legitimacy to existing management control systems. However, implementing ERM is a challenging process where success is dependent on balancing technical and social factors. This book explores the challenges of implementing ERM from technical, cognitive, and social perspectives to enhance the organisation’s capacity to generate and integrate information and knowledge about risk and uncertainty. In existing publications, ERM implementation is mainly viewed from technical or educational perspectives and treated as formal, technical, linear processes. This book takes a different stance by recognising that implementation depends on formal and informal mechanisms that require a balanced combination of technical and social approaches. It changes the paradigm to demonstrate that the implementation of ERM is not a linear process that is similar across industries and organisations, but relies on multiple dependencies such as leadership, corporate governance, and the culture of the organisation. This book will be a valuable resource for scholars, as well as upper-level students, across disciplines related to risk management, including accounting and finance, business and management, leadership, and organisational studies. |
| enterprise risk management data analytics: Effectiveness of Enterprise Risk Management Izabela Jonek-Kowalska, 2022-02-21 Effective risk management is a crucial part of the success of any organization. In scholarly research, numerous publications have been written on the design of complex enterprise risk management systems, however very little consideration has been given to the effectiveness of implemented management solutions. This book seeks to fill this important gap. Based on a study featuring a representative group of 722 companies, the author presents the various determinants of risk management effectiveness, including behavioural determinants (such as attitude to risk) as well as internal and external determinants (such as human and financial resources and the environment in which the organization operates). Along with a theoretical and practical overview of the various considerations from an international perspective, the reader will gain an insight into the implications for practice. Ultimately, this book formulates conclusions and recommendations for the improvement of tools and systems of enterprise risk management. |
| enterprise risk management data analytics: Actuaries' Survival Guide Ping Wang, Fred Szabo, 2024-02-02 Actuaries' Survival Guide: Navigating the Exam and Data Science, Third Edition explains what actuaries are, what they do, and where they do it. It describes exciting combinations of ideas, techniques, and skills involved in the day-to-day work of actuaries. This edition has been updated to reflect the rise of social networking and the internet, the progress toward a global knowledge-based economy, and the global expansion of the actuarial field that has occurred since the prior edition. - Includes details on the Society of Actuaries' (SOA) and Casualty Actuarial Society (CAS) examinations, as well as sample questions and answers - Presents an overview of career options and includes profiles of companies and agencies that employ actuaries - Provides a link between theory and practice and helps readers understand the blend of qualitative and quantitative skills and knowledge required to succeed in actuarial exams - Offers insights provided by real-life actuaries and actuarial students about the profession |
| enterprise risk management data analytics: Artificial Intelligence and Blockchain in Industry 4.0 Rohit Sharma, Rajendra Prasad Mahapatra, Gwanggil Jeon, 2023-10-26 The book addresses the challenges in designing blockchain-based secured solutions for Industry 4.0 applications using artificial intelligence. It further provides a comparative analysis of various advanced security approaches such as edge computing, cybersecurity, and cloud computing in the realm of information technology. This book: • Address the challenges in designing blockchain-based secured solutions for Industry 4.0 applications using artificial intelligence • Provides a comparative analysis of various advanced security approaches such as edge computing, cybersecurity, and cloud computing in the realm of information technology • Discusses the evolution of blockchain and artificial intelligence technology, from fundamental theories to practical aspects • Illustrates the most recent research solutions that handle the security and privacy threats while considering the resource-constrained in Industry 4.0 devices • Showcases the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection The text aims to fill the gap between the theories of blockchain and its practical application in business, government, and defense among other areas. It further highlights the challenges associated with the use of blockchain for various industry 4.0 applications such as data analytics, software-defined networks, cyber-physical systems, drones, and cybersecurity. The text is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, manufacturing engineering, and industrial engineering. |
| enterprise risk management data analytics: A Handbook to Business Analytics Sahil Kohli, Deepanshi Wadhwa, 2023-02-14 Business Analytics has become a crucial aspect of decision-making in the modern business world. With the availability of vast amounts of Data and the increasing use of technology, organizations are now relying more than ever on data-driven insights to drive growth and gain a competitive advantage. In A Handbook to Business Analytics, authors Sahil Kohli and Deepanshi Wadhwa present a comprehensive guide to understanding the principles and practices of Business Analytics. The book covers a wide range of topics, from the basics of Data Collection and Analysis to Visualisation and Decision Analysis. With clear explanations and practical examples, this handbook is designed to be accessible to readers with little or no prior experience in the field. Whether you are a student, professional, or entrepreneur, this book will provide you with the knowledge and skills you need to make informed decisions based on data. By the end of this handbook, you will have a deep understanding of the role of Analytics in Business, the various tools and techniques available for Data Analysis, and how to apply these techniques to real-world business problems. Whether you are looking to build a career in Business Analytics or simply want to gain a competitive advantage in your current role, this book is an essential resource for anyone interested in using Data to drive Business success. |
| enterprise risk management data analytics: The Risk Management Handbook David Hillson, 2023-08-03 The Risk Management Handbook offers readers knowledge of current best practice and cutting-edge insights into new developments within risk management. Risk management is dynamic, with new risks continually being identified and risk techniques being adapted to new challenges. Drawing together leading voices from the major risk management application areas, such as political, supply chain, cybersecurity, ESG and climate change risk, this edited collection showcases best practice in each discipline and provides a comprehensive survey of the field as a whole. This second edition has been updated throughout to reflect the latest developments in the industry. It incorporates content on updated and new standards such as ISO 31000, MOR and ISO 14000. It also offers brand new chapters on ESG risk management, legal risk management, cyber risk management, climate change risk management and financial risk management. Whether you are a risk professional wanting to stay abreast of your field, a student seeking a broad and up-to-date introduction to risk, or a business leader wanting to get to grips with the risks that face your business, this book will provide expert guidance. |
| enterprise risk management data analytics: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics Patil, Bhushan, Vohra, Manisha, 2020-10-23 Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies. |
| enterprise risk management data analytics: Public Administration and Public Affairs Nicholas Henry, 2017-09-01 Public Administration and Public Affairs demonstrates how to govern efficiently, effectively, and responsibly in an age of political corruption and crises in public finance. Providing a comprehensive, accessible and humorous introduction to the field of Public Administration, this text is designed specifically for those with little to no background in the field. Now in its 13th edition, this beloved book includes: Engaging, timely new sections designed to make students think, such as Why Are So Many Leaders Losers? and Even Terrorists Like Good Government Comparisons throughout of the challenges and opportunities found in the nonprofit sector vs. the public sector (sections such as The Dissatisfied Bureaucrat, the Satisfied Nonprofit Professional?) Extensive new material on e-governance, performance management, HRM, intersectoral and intergovernmental administration, government contracting, public budgeting, and ethics. The 13th edition is complete with an Instructor’s Manual, Testbank, and PowerPoint slides for instructors, as well as Learning Objectives and Self-test Questions for students, making it the ideal primer for public administration/management, public affairs, and nonprofit management courses. |
| enterprise risk management data analytics: Principles of Risk Analysis Charles Yoe, 2019-01-30 In every decision problem there are things we know and things we do not know. Risk analysis science uses the best available evidence to assess what we know while it is carefully intentional in the way it addresses the importance of the things we do not know in the evaluation of decision choices and decision outcomes. The field of risk analysis science continues to expand and grow and the second edition of Principles of Risk Analysis: Decision Making Under Uncertainty responds to this evolution with several significant changes. The language has been updated and expanded throughout the text and the book features several new areas of expansion including five new chapters. The book’s simple and straightforward style—based on the author’s decades of experience as a risk analyst, trainer, and educator—strips away the mysterious aura that often accompanies risk analysis. Features: Details the tasks of risk management, risk assessment, and risk communication in a straightforward, conceptual manner Provides sufficient detail to empower professionals in any discipline to become risk practitioners Expands the risk management emphasis with a new chapter to serve private industry and a growing public sector interest in the growing practice of enterprise risk management Describes dozens of quantitative and qualitative risk assessment tools in a new chapter Practical guidance and ideas for using risk science to improve decisions and their outcomes is found in a new chapter on decision making under uncertainty Practical methods for helping risk professionals to tell their risk story are the focus of a new chapter Features an expanded set of examples of the risk process that demonstrate the growing applications of risk analysis As before, this book continues to appeal to professionals who want to learn and apply risk science in their own professions as well as students preparing for professional careers. This book remains a discipline free guide to the principles of risk analysis that is accessible to all interested practitioners. Files used in the creation of this book and additional exercises as well as a free student version of Palisade Corporation’s Decision Tools Suite software are available with the purchase of this book. A less detailed introduction to the risk analysis science tasks of risk management, risk assessment, and risk communication is found in Primer of Risk Analysis: Decision Making Under Uncertainty, Second Edition, ISBN: 978-1-138-31228-9. |
| enterprise risk management data analytics: Proceedings of the 8th Global Conference on Business, Management, and Entrepreneurship (GCBME 2023) Ratih Hurriyati, 2024 |
| enterprise risk management data analytics: Primer on Risk Analysis Charles Yoe, 2019-01-18 Primer on Risk Analysis: Decision Making Under Uncertainty, Second Edition lays out the tasks of risk analysis in a straightforward, conceptual manner, tackling the question, What is risk analysis? Distilling the common principles of many risk dialects into serviceable definitions, it provides a foundation for the practice of risk management and decision making under uncertainty for professionals from all disciplines. New in this edition is an expanded risk management emphasis that includes an overview chapter on enterprise risk management and a chapter on decision making under uncertainty designed to help decision makers use the results of risk analysis in practical ways to improve decisions and their outcomes. This book will empower you to enter the world of risk management in your own domain of expertise by providing you with practical, insightful, useful and adaptable knowledge of risk analysis science including risk management, risk assessment, and risk communication. Features: Answers the fundamental question, What is Risk Analysis? Presents the tasks of risk management, risk assessment, and risk communication in a straightforward, conceptual manner Responds to the continuing evolution of risk science and addresses the language of risk as it continues to evolve Expands the risk management emphasis with a new chapter to serve private industry and a growing public sector interest in the growing practice of enterprise risk management Includes a new chapter on decision making under uncertainty provides practical guidance and ideas for using risk science to improve decisions and their outcomes Features an expanded set of examples of the risk process that demonstrate the growing applications of risk analysis This book is suitable for executives, professionals and students who seek a fundamental understanding of risk management, risk assessment, and risk communication. A more detailed examination of this topic, suitable for practitioners from any discipline as well as students and professionals who aspire to become experts in the practice of risk analysis science, is found in Principles of Risk Analysis: Decision Making Under Uncertainty, Second Edition, ISBN: 978-1-138-47820-6. |
| enterprise risk management data analytics: The Risk of Trading Michael Toma, 2012-03-23 Develop the skills to manage risk in the high-stakes world of financial speculation The Risk of Trading is a practical resource that takes an in-depth look at one of the most challenging factors of trading—risk management. The book puts a magnifying glass on the issue of risk, something that every trader needs to understand in order to be successful. Most traders look at risk in terms of a stop-loss that enables them to exit a losing trade quickly. In The Risk of Trading, Michael Toma explains that risk is ever-present in every aspect of trading and advocates that traders adopt a more comprehensive view of risk that encompasses the strategic trading plan, account size, drawdowns, maximum possible losses, psychological capital, and crisis management. Shows how to conduct a detailed statistical analysis of an individual's trading methodology through back-testing and real-time results so as to identify when the methodology may be breaking down in actual trading Reveals why traders should think of themselves as project managers who are strategically managing risk The book is based on the author's unique 'focus on the risk' approach to trading using data-driven risk statistical analytics Using this book as a guide, traders can operate more as business managers and learn how to avoid market-busting losses while achieving consistently good results. |
| enterprise risk management data analytics: Self-Service Data Analytics and Governance for Managers Nathan E. Myers, Gregory Kogan, 2021-05-12 Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment. In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap. This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands. |
| enterprise risk management data analytics: Computing for Data Analysis: Theory and Practices Sanjay Chakraborty, Lopamudra Dey, 2023-02-04 This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area. |
| enterprise risk management data analytics: Reliability Engineering and Risk Analysis Mohammad Modarres, Mark P. Kaminskiy, Vasiliy Krivtsov, 2009-09-22 Tools to Proactively Predict Failure The prediction of failures involves uncertainty, and problems associated with failures are inherently probabilistic. Their solution requires optimal tools to analyze strength of evidence and understand failure events and processes to gauge confidence in a design’s reliability. Reliability Engineering and Risk Analysis: A Practical Guide, Second Edition has already introduced a generation of engineers to the practical methods and techniques used in reliability and risk studies applicable to numerous disciplines. Written for both practicing professionals and engineering students, this comprehensive overview of reliability and risk analysis techniques has been fully updated, expanded, and revised to meet current needs. It concentrates on reliability analysis of complex systems and their components and also presents basic risk analysis techniques. Since reliability analysis is a multi-disciplinary subject, the scope of this book applies to most engineering disciplines, and its content is primarily based on the materials used in undergraduate and graduate-level courses at the University of Maryland. This book has greatly benefited from its authors' industrial experience. It balances a mixture of basic theory and applications and presents a large number of examples to illustrate various technical subjects. A proven educational tool, this bestselling classic will serve anyone working on real-life failure analysis and prediction problems. |
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