{"product_id":"risk-modeling-isbn-9781119824930","title":"Risk Modeling","description":"\u003cp\u003e\u003cb\u003eA wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process.\u003c\/p\u003e \u003cp\u003eAuthors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eHighlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk\u003c\/li\u003e \u003cli\u003eDiscusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques\u003c\/li\u003e \u003cli\u003eCovers the basic principles and nuances of feature engineering and common machine learning algorithms\u003c\/li\u003e \u003cli\u003eIllustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle\u003c\/li\u003e \u003cli\u003eExplains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRisk Modeling: Definition and Brief History 4\u003c\/p\u003e \u003cp\u003eUse of AI and Machine Learning in Risk Modeling 7\u003c\/p\u003e \u003cp\u003eThe New Risk Management Function 7\u003c\/p\u003e \u003cp\u003eOvercoming Barriers to Technology and AI Adoption with a Little Help from Nature 10\u003c\/p\u003e \u003cp\u003eThis Book: What It Is and Is Not 11\u003c\/p\u003e \u003cp\u003eEndnotes 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Data Management and Preparation 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eImportance of Data Governance to the Risk Function 18\u003c\/p\u003e \u003cp\u003eFundamentals of Data Management 20\u003c\/p\u003e \u003cp\u003eOther Data Considerations for AI, Machine Learning, and Deep Learning 22\u003c\/p\u003e \u003cp\u003eConcluding Remarks 29\u003c\/p\u003e \u003cp\u003eEndnotes 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRisk Modeling Using Machine Learning 35\u003c\/p\u003e \u003cp\u003eDefinitions of AI, Machine, and Deep Learning 40\u003c\/p\u003e \u003cp\u003eConcluding Remarks 52\u003c\/p\u003e \u003cp\u003eEndnotes 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifference Between Explaining and Interpreting Models 57\u003c\/p\u003e \u003cp\u003eWhy Explain AI Models 59\u003c\/p\u003e \u003cp\u003eCommon Approaches to Address Explainability of Data Used for Model Development 61\u003c\/p\u003e \u003cp\u003eCommon Approaches to Address Explainability of Models and Model Output 62\u003c\/p\u003e \u003cp\u003eLimitations in Popular Methods 68\u003c\/p\u003e \u003cp\u003eConcluding Remarks 69\u003c\/p\u003e \u003cp\u003eEndnotes 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Bias, Fairness, and Vulnerability in Decision-Making 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAssessing Bias in AI Systems 73\u003c\/p\u003e \u003cp\u003eWhat Is Bias? 76\u003c\/p\u003e \u003cp\u003eWhat Is Fairness? 77\u003c\/p\u003e \u003cp\u003eTypes of Bias in Decision-Making 78\u003c\/p\u003e \u003cp\u003eConcluding Remarks 89\u003c\/p\u003e \u003cp\u003eEndnotes 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Machine Learning Model Deployment, Implementation, and Making Decisions 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypical Model Deployment Challenges 93\u003c\/p\u003e \u003cp\u003eDeployment Scenarios 98\u003c\/p\u003e \u003cp\u003eCase Study: Enterprise Decisioning at a Global Bank 101\u003c\/p\u003e \u003cp\u003ePractical Considerations 102\u003c\/p\u003e \u003cp\u003eModel Orchestration 103\u003c\/p\u003e \u003cp\u003eConcluding Remarks 104\u003c\/p\u003e \u003cp\u003eEndnote 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstablishing the Right Internal Governance Framework 108\u003c\/p\u003e \u003cp\u003eDeveloping Machine Learning Models with Governance in Mind 109\u003c\/p\u003e \u003cp\u003eMonitoring AI and Machine Learning 112\u003c\/p\u003e \u003cp\u003eCompliance Considerations 122\u003c\/p\u003e \u003cp\u003eFurther Takeaway 125\u003c\/p\u003e \u003cp\u003eConcluding Remarks 126\u003c\/p\u003e \u003cp\u003eEndnotes 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Optimizing Parameters for Machine Learning Models and Decisions in Production 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOptimization for Machine Learning 131\u003c\/p\u003e \u003cp\u003eMachine Learning Function Optimization Using Solvers 133\u003c\/p\u003e \u003cp\u003eTuning of Parameters 136\u003c\/p\u003e \u003cp\u003eOther Optimization Algorithms for Risk Models 141\u003c\/p\u003e \u003cp\u003eMachine Learning Models as Optimization Tools 143\u003c\/p\u003e \u003cp\u003eConcluding Remarks 147\u003c\/p\u003e \u003cp\u003eEndnotes 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 The Interconnection between Climate and Financial Instability 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMagnitude of Climate Instability: Understanding the \"Why\" of Climate Change Risk Management 152\u003c\/p\u003e \u003cp\u003eInterconnected: Climate and Financial Stability 157\u003c\/p\u003e \u003cp\u003eAssessing the impacts of climate change using AI and machine learning 158\u003c\/p\u003e \u003cp\u003eUsing scenario analysis to understand potential economic impact 160\u003c\/p\u003e \u003cp\u003ePractical Examples 170\u003c\/p\u003e \u003cp\u003eConcluding Remarks 172\u003c\/p\u003e \u003cp\u003eEndnotes 172\u003c\/p\u003e \u003cp\u003eAbout the Authors 175\u003c\/p\u003e \u003cp\u003eIndex 177\u003c\/p\u003e \u003cb\u003eTERISA ROBERTS, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Global Solution Lead for Risk Modeling and Decisioning at SAS. She has nearly twenty years of experience in quantitative risk management and advanced analytics. She regularly advises banks and regulators around the world on industry best practices in AI, automation, and digitalization related to risk modeling and decisioning.  \u003cp\u003e\u003cb\u003eSTEPHEN J. TONNA, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Senior Banking Solutions Advisor at SAS. He is a member of the SAS Risk Finance Advisory team for SAS Risk Research and Quantitative Solutions (RQS) in Asia Pacific. He received his doctorate in genetics, mathematics, and statistics from the University of Melbourne and research fellowship from the Brigham and Women's Hospital and Harvard Medical School.   \u003c\/p\u003e\u003cp\u003e In \u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e, distinguished risk and analytics professionals Terisa Roberts and Stephen J. Tonna deliver an innovative and insightful exploration of the latest artificial intelligence technologies used to forecast and evaluate financial risks. The authors offer up-to-date information on how to apply current modeling techniques in risk management, as well as new opportunities and challenges associated with the implementation of artificial intelligence (AI) and machine learning (ML) in the risk management process. \u003c\/p\u003e \u003cp\u003eYou’ll learn the strengths and weaknesses of AI and ML where they’re applied to everyday risk management problems or to once-in-a-lifetime “black swan” events, like global pandemics or climate shocks. The authors clarify common misconceptions about AI and ML and offer step-by-step guidance to using the modern technologies within your organization’s existing risk management framework.  \u003c\/p\u003e\u003cp\u003e The book provides practical tools for assessing bias and the interpretability of ML models. It also covers the basic principles of feature engineering and the most commonly used ML algorithms. The authors discuss how risk modeling incorporates AI and ML to rapidly process complicated data and fills the gaps currently existing in the end-to- end risk modeling lifecycle. Finally, \u003ci\u003eRisk Modeling\u003c\/i\u003e explains how proprietary software and open-source languages can be combined to deliver the best of both worlds for risk models and for risk practitioners.  \u003c\/p\u003e\u003cp\u003e Perfect for C-suite executives, risk managers, and other business leaders, \u003ci\u003eRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning\u003c\/i\u003e is also an indispensable resource for compliance officers and managers, as well as anyone else who seeks to apply the latest AI and ML learning techniques to solve or mitigate quantitative risk problems.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for Risk Modeling \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“This book is highly accessible and directed at practitioners interested in the application of AI and ML in the financial services industry. I first met Terisa over twenty years ago and have marveled at her growth in the analytics space and ability to communicate regarding complex topics.”\u003cbr\u003e\u003cb\u003e —RAYMOND ANDERSON,\u003c\/b\u003e Rayan Risk Analytics\u003c\/p\u003e \u003cp\u003e“This comprehensive text answers all the critical questions bankers have been asking around using AI and ML for risk modeling for years. It should be part of every risk modeler’s library.” \u003cbr\u003e\u003cb\u003e —NAEEM SIDDIQI,\u003c\/b\u003e Senior Risk Advisor, SAS Institute\u003c\/p\u003e \u003cp\u003e“An ideal read for managers or senior managers in any financial institution. Roberts and Tonna’s writing is clear, direct, accurate, and uses exactly the right level of technicality to get to each point.”\u003cbr\u003e\u003cb\u003e —ALAN FORREST,\u003c\/b\u003e Advisory Senior Manager, Model Risk Oversight\u003cbr\u003e\u003cbr\u003e\"Machine Learning is disrupting the world of model and data governance. Roberts and Tonna succinctly describe how forward-looking organizations will pragmatically use these approaches to responsibly drive profits and gain a competitive advantage.\"\u003cbr\u003e\u003cb\u003e—DAVID ASERMELY, \u003c\/b\u003eGlobal Lead, Model Risk Management\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989967847653,"sku":"NP9781119824930","price":49.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119824930.jpg?v=1761786065","url":"https:\/\/k12savings.com\/products\/risk-modeling-isbn-9781119824930","provider":"K12savings","version":"1.0","type":"link"}