Download or read book Risk Analytics From Concept To Deployment written by Edward Hon Khay Ng and published by World Scientific. This book was released on 2021-10-04 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Variation Risk Management written by Anna C. Thornton and published by John Wiley & Sons. This book was released on 2003-11-05 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.

Download or read book Managing Model Risk written by Bart Baesens and published by . This book was released on 2021-06-30 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Financial Risk Management written by Jimmy Skoglund and published by John Wiley & Sons. This book was released on 2015-09-04 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Supply Chain Risk written by George A. Zsidisin and published by Springer Science & Business Media. This book was released on 2008-09-08 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk is of fundamental importance in this era of the global economy. Supply chains must into account the uncertainty of demand. Moreover, the risk of uncertain demand can cut two ways: (1) there is the risk that unexpected demand will not be met on time, and the reverse problem (2) the risk that demand is over estimated and excessive inventory costs are incurred. There are other risks in unreliable vendors, delayed shipments, natural disasters, etc. In short, there are a host of strategic, tactical and operational risks to business supply chains. Supply Chain Risk: A Handbook of Assessment, Management, and Performance will focus on how to assess, evaluate, and control these various risks.

Download or read book Trade off Analytics written by Gregory S. Parnell and published by John Wiley & Sons. This book was released on 2016-10-25 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents information to create a trade-off analysis framework for use in government and commercial acquisition environments This book presents a decision management process based on decision theory and cost analysis best practices aligned with the ISO/IEC 15288, the Systems Engineering Handbook, and the Systems Engineering Body of Knowledge. It provides a sound trade-off analysis framework to generate the tradespace and evaluate value and risk to support system decision-making throughout the life cycle. Trade-off analysis and risk analysis techniques are examined. The authors present an integrated value trade-off and risk analysis framework based on decision theory. These trade-off analysis concepts are illustrated in the different life cycle stages using multiple examples from defense and commercial domains. Provides techniques to identify and structure stakeholder objectives and creative, doable alternatives Presents the advantages and disadvantages of tradespace creation and exploration techniques for trade-off analysis of concepts, architectures, design, operations, and retirement Covers the sources of uncertainty in the system life cycle and examines how to identify, assess, and model uncertainty using probability Illustrates how to perform a trade-off analysis using the INCOSE Decision Management Process using both deterministic and probabilistic techniques Trade-off Analytics: Creating and Exploring the System Tradespace is written for upper undergraduate students and graduate students studying systems design, systems engineering, industrial engineering and engineering management. This book also serves as a resource for practicing systems designers, systems engineers, project managers, and engineering managers. Gregory S. Parnell, PhD, is a Research Professor in the Department of Industrial Engineering at the University of Arkansas. He is also a senior principal with Innovative Decisions, Inc., a decision and risk analysis firm and has served as Chairman of the Board. Dr. Parnell has published more than 100 papers and book chapters and was lead editor of Decision Making for Systems Engineering and Management, Wiley Series in Systems Engineering (2nd Ed, Wiley 2011) and lead author of the Handbook of Decision Analysis (Wiley 2013). He is a fellow of INFORMS, the INCOSE, MORS, and the Society for Decision Professionals.

Download or read book Value Realization from Efficient Software Deployment written by Alex Louwe Kooijmans and published by IBM Redbooks. This book was released on 2011-10-25 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many companies have a complex process for purchasing software that is required by IT projects, or better, by the business. Usually software is purchased by a centralized procurement function, and is either purchased on a project-by-project basis or as a large periodic software contract. Unfortunately purchasing software products does not automatically mean that these products are exploited throughout the organization providing the maximum possible value to the business units. Several issues call for a structured approach that gets the most business value out of software already purchased. The objectives of this approach are to: Create maximum awareness throughout the organization of the software purchased. Track software use in IT projects and act if products are not used at all, used improperly, or insufficiently used. Facilitate use of software products in projects, especially when software products are complex and require a lot of integration. We can summarize the overall objective of this approach as ensuring that the business units in an organization obtain the maximum possible value of software products purchased, which is also the scope of this IBM® Redbooks® publication.

Download or read book Risk Analytics written by Edward H. K. Ng and published by . This book was released on 2021 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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".

Download or read book International Handbook on Risk Analysis and Management written by Beat Habegger and published by . This book was released on 2008 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Risk Analytics written by Eduardo Rodriguez and published by CRC Press. This book was released on 2023-08-08 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Risk Assessment written by Marvin Rausand and published by John Wiley & Sons. This book was released on 2020-03-31 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Information Quality written by Ron S. Kenett and published by John Wiley & Sons. This book was released on 2016-12-19 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management. This book: Explains how to integrate the notions of goal, data, analysis and utility that are the main building blocks of data analysis within any domain. Presents a framework for integrating domain knowledge with data analysis. Provides a combination of both methodological and practical aspects of data analysis. Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects. Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys. Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website. This book will be beneficial for researchers in academia and in industry, analysts, consultants, and agencies that collect and analyse data as well as undergraduate and postgraduate courses involving data analysis.

Download or read book Deployment Guide for InfoSphere Guardium written by Whei-Jen Chen and published by IBM Redbooks. This book was released on 2015-04-14 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: IBM® InfoSphere® Guardium® provides the simplest, most robust solution for data security and data privacy by assuring the integrity of trusted information in your data center. InfoSphere Guardium helps you reduce support costs by automating the entire compliance auditing process across heterogeneous environments. InfoSphere Guardium offers a flexible and scalable solution to support varying customer architecture requirements. This IBM Redbooks® publication provides a guide for deploying the Guardium solutions. This book also provides a roadmap process for implementing an InfoSphere Guardium solution that is based on years of experience and best practices that were collected from various Guardium experts. We describe planning, installation, configuration, monitoring, and administrating an InfoSphere Guardium environment. We also describe use cases and how InfoSphere Guardium integrates with other IBM products. The guidance can help you successfully deploy and manage an IBM InfoSphere Guardium system. This book is intended for the system administrators and support staff who are responsible for deploying or supporting an InfoSphere Guardium environment.

Download or read book Agile Machine Learning with DataRobot written by Bipin Chadha and published by Packt Publishing Ltd. This book was released on 2021-12-24 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key FeaturesGet well-versed with DataRobot features using real-world examplesUse this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycleMake use of advanced DataRobot capabilities to programmatically build and deploy a large number of ML modelsBook Description DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization. You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities. By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors. What you will learnUnderstand and solve business problems using DataRobotUse DataRobot to prepare your data and perform various data analysis tasks to start building modelsDevelop robust ML models and assess their results correctly before deploymentExplore various DataRobot functions and outputs to help you understand the models and select the one that best solves the business problemAnalyze a model's predictions and turn them into actionable insights for business usersUnderstand how DataRobot helps in governing, deploying, and maintaining ML modelsWho this book is for This book is for data scientists, data analysts, and data enthusiasts looking for a practical guide to building and deploying robust machine learning models using DataRobot. Experienced data scientists will also find this book helpful for rapidly exploring, building, and deploying a broader range of models. The book assumes a basic understanding of machine learning.

Download or read book Risk Based Performance Management written by A. Smart and published by Springer. This book was released on 2013-10-31 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pulling together into a single framework the two separate disciplines of strategy management and risk management, this book provides a practical guide for organizations to shape and execute sustainable strategies with full understanding of how much risk they are willing to accept in pursuit of strategic goals.

Download or read book Trustworthy Online Controlled Experiments written by Ron Kohavi and published by Cambridge University Press. This book was released on 2020-04-02 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.