Risk Analytics From Concept To Deployment Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Risk Analytics From Concept To Deployment book. This book definitely worth reading, it is an incredibly well-written.
Author : Edward Hon Khay Ng Publisher : World Scientific Page : 243 pages File Size : 47,9 Mb Release : 2021-10-04 Category : Business & Economics ISBN : 9789811239076
Risk Analytics: From Concept To Deployment by Edward Hon Khay Ng Pdf
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.
"Risk analytics has seen a spike in interest and demand with the quantification of risks and global regulatory requirements. Financial institutions like banks, in particular, have to show evidence of having measured credit, market and operational risks using numbers and models rather than qualitative judgments. These corporations already have massive databases but automating the process to translate data into risk parameters remains a desire in most of them. In the past, this was partly due to the lack of cost-effective tools to accomplish the task. Modeling was done using software with output codes not readily processed by databases. Data have to be manually extracted and run on the models with results input into the databases manually again. With the increasing acceptance of open source languages, database vendors have seen the value of integrating modeling capabilities into their products. That has made it possible to insert models developed using R, Python or other languages directly into SQL scripts used for database transactions. As R or Python are free, there is no additional cost involved. Nevertheless, deploying solutions developed to automate the process remains a challenge. While not comprehensive in dealing with all facets of risks, the author with his wealth of consulting experience, aims to contribute to the development of risk professionals who will be able to progress beyond theories and concepts to create solutions that can support planning and automated decision-making".
The 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth’s conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing newrisks. Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human and artificial intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components, such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation, but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation. This book moves from a review of data to the concepts of a RAS. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics. Written for executives, analytics professionals, risk management professionals, strategy professionals, and postgraduate students, this book shows a way to implement the analytics process to develop a risk management practice that creates an adaptive competitive advantage under uncertainty.
Data Mining and Predictive Analysis by Colleen McCue Pdf
Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining in homeland security, security analysis, and operational law enforcement settings. This revised text highlights new and emerging technology, discusses the importance of analytic context for ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of predictive analytics in intelligence and security analysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment. Discusses new and emerging technologies and techniques, including up-to-date information on predictive policing, a key capability in law enforcement and security Demonstrates the importance of analytic context beyond software Covers new models for effective delivery of advanced analytics to the operational environment, which have increased access to even the most powerful capabilities Includes terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis
Author : Kurt J. Engemann,Rory V. O'Connor Publisher : Walter de Gruyter GmbH & Co KG Page : 284 pages File Size : 47,9 Mb Release : 2021-03-08 Category : Business & Economics ISBN : 9783110652321
Project Risk Management by Kurt J. Engemann,Rory V. O'Connor Pdf
Managing risk is essential for every organization. However, significant opportunities may be lost by concentrating on the negative aspects of risk without bearing in mind the positive attributes. The objective of Project Risk Management: Managing Software Development Risk is to provide a distinct approach to a broad range of risks and rewards associated with the design, development, implementation and deployment of software systems. The traditional perspective of software development risk is to view risk as a negative characteristic associated with the impact of potential threats. The perspective of this book is to explore a more discerning view of software development risks, including the positive aspects of risk associated with potential beneficial opportunities. A balanced approach requires that software project managers approach negative risks with a view to reduce the likelihood and impact on a software project, and approach positive risks with a view to increase the likelihood of exploiting opportunities. Project Risk Management: Managing Software Development Risk explores software development risk both from a technological and business perspective. Issues regarding strategies for software development are discussed and topics including risks related to technical performance, outsourcing, cybersecurity, scheduling, quality, costs, opportunities and competition are presented. Bringing together concepts across the broad spectrum of software engineering with a project management perspective, this volume represents both a professional and scholarly perspective on the topic.
Managing Model Risk by Bart Baesens,Seppe vanden Broucke Pdf
Get up to speed on identifying and tackling model risk! Managing Model Risk provides data science practitioners, business professionals and analytics managers with a comprehensive guide to understand and tackle the fundamental concept of analytical model risk in terms of data, model specification, model development, model validation, model operationalization, model security and model management. Providing state of the art industry and research insights based on the author''s extensive experience, this illustrated textbook has a well-balanced theory-practice focus and covers all essential topics. Key Features: Extensive coverage of important trending topics and their risk impact on analytical models, starting from the raw data up until the operationalization, security and management. Various examples and case studies to highlight the topics discussed. Key references to background literature for further clarification. An online website with various add-ons and recent developments: www.managingmodelriskbook.com. What Makes this Book Different? This book is based on both authors having worked in analytics for more than 30 years combined, both in industry and academia. Both authors have co-authored more than 300 scientific publications on analytics and machine learning and have worked with firms in different industries, including (online) retailers, financial institutions, manufacturing firms, insurance providers, governments, etc. all over the globe estimating, deploying and validating analytical models. Throughout this time, we have read many books about analytical modeling and data science, which are typically written from the perspective of a theorist, providing lots of details with regards to different model algorithms and related mathematics, but with limited attention being given to how such models are used in practice. If such concerns are tackled, it is mainly from an implementation, use case or data engineering perspective. From our own experience, however, we have encountered many cases where analytics, AI, machine learning etc. fail in organizations, even with skilled people working on them, due to a myriad of reasons: bad data quality, difficulties in terms of model deployment, lack of model buy-in, incorrect definitions of underlying goals, wrong evaluation metrics, unrealistic expectations and many other issues can arise which cause models to fail in practice. Most of these issues have nothing to do with the actual algorithm being used to construct the model, but rather with everything else surrounding it: data, governance, maintenance, business, management, the economy, budgeting, culture etc. As such, we wanted to offer a new perspective with this book: it aims to provide a unique mix of both practical and research-based insights and report on do''s and don''ts for model risk management. Model risk issues are not only highlighted but also recommendations are given on how to deal with them, where possible. Target Audience This book is targeted towards everyone who has previously been exposed to both predictive and descriptive analytics. The reader should hence have some basic understanding of the analytics process model, the key activities of data preprocessing, the steps involved in developing a predictive analytics model (using e.g. linear or logistic regression, decision trees, etc.) and a descriptive analytics model (using e.g. association or sequence rules or clustering techniques). It is also important to be aware of how an analytical model can be properly evaluated, both in terms of accuracy and interpretation. This book aims to offer a comprehensive guide for both data scientists as well as (C-level) executives and data science or engineering leads, decision-makers and managers who want to know the key underlying concepts of analytical model risk.
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar Pdf
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Risk-Based Policing by Leslie W. Kennedy,Joel M. Caplan,Eric L. Piza Pdf
"Risk-based policing is the latest advancement in the long history of policing innovations, where research and planning have combined to manage crime risks, prevent crime, and enhance public safety. In Risk-Based Policing the authors share case studies from different agencies to demonstrate how focusing police resources at risky places, based on smart uses of data and strong analytical work, can address the worst effects of disorder and crime while improving public safety and community relations. Topics include the role of big data; the evolution of modern policing; dealing with high-risk targets; designing, implementing, and evaluating risk-based policing strategies; and the role of multiple stakeholders in risk-based policing. Case studies explore cities such as Colorado Springs, Glendale, Newark, Kansas City, and others. The book also demonstrates how Risk Terrain Modeling (RTM) can be extended to offer a more comprehensive view of prevention and deterrence"--Provided by publisher.
Journeys to Data Mining by Mohamed Medhat Gaber Pdf
Data mining, an interdisciplinary field combining methods from artificial intelligence, machine learning, statistics and database systems, has grown tremendously over the last 20 years and produced core results for applications like business intelligence, spatio-temporal data analysis, bioinformatics, and stream data processing. The fifteen contributors to this volume are successful and well-known data mining scientists and professionals. Although by no means an exhaustive list, all of them have helped the field to gain the reputation and importance it enjoys today, through the many valuable contributions they have made. Mohamed Medhat Gaber has asked them (and many others) to write down their journeys through the data mining field, trying to answer the following questions: 1. What are your motives for conducting research in the data mining field? 2. Describe the milestones of your research in this field. 3. What are your notable success stories? 4. How did you learn from your failures? 5. Have you encountered unexpected results? 6. What are the current research issues and challenges in your area? 7. Describe your research tools and techniques. 8. How would you advise a young researcher to make an impact? 9. What do you predict for the next two years in your area? 10. What are your expectations in the long term? In order to maintain the informal character of their contributions, they were given complete freedom as to how to organize their answers. This narrative presentation style provides PhD students and novices who are eager to find their way to successful research in data mining with valuable insights into career planning. In addition, everyone else interested in the history of computer science may be surprised about the stunning successes and possible failures computer science careers (still) have to offer.
"A thoughtful, complete, and very readable approach to robust engineering. It presents insights that correlate with those learned at Ford while developing and executing Design for Six Sigma. Having this book three years ago could’ve helped with that effort."–David Amos, DFSS Deployment Director, Ford Motor Company Written by Anna C. Thornton, the well-known author who coined the phrase "variation risk management," this comprehensive book presents new methods and implementation strategies based on her research of industry practices and her personal experience with such companies as The Boeing Company, Eastman Kodak Company, Ford Motor Company, Johnson & Johnson, and many others. Step-by-step guidelines show how you can implement and apply variation risk management to real-world problems within the existing systems of an organization.
Financial Risk Management by Jimmy Skoglund,Wei Chen Pdf
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.
Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions by Ovidiu Vermesan Pdf
This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas.
Designing Distributed Systems by Brendan Burns Pdf
Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows
Web Services: Concepts, Methodologies, Tools, and Applications by Management Association, Information Resources Pdf
Web service technologies are redefining the way that large and small companies are doing business and exchanging information. Due to the critical need for furthering automation, engagement, and efficiency, systems and workflows are becoming increasingly more web-based. Web Services: Concepts, Methodologies, Tools, and Applications is an innovative reference source that examines relevant theoretical frameworks, current practice guidelines, industry standards and standardization, and the latest empirical research findings in web services. Highlighting a range of topics such as cloud computing, quality of service, and semantic web, this multi-volume book is designed for computer engineers, IT specialists, software designers, professionals, researchers, and upper-level students interested in web services architecture, frameworks, and security.
