Risk Modeling – Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning: Wiley and SAS Business Series

Acknowledgments xi Preface xiii Chapter 1 Introduction 1 Risk Modeling: Definition and Brief History 4 Use of AI and Machine Learning in Risk Modeling 7 The New Risk Management Function 7 Overcoming Barriers to Technology and AI Adoption with a Little Help from Nature 10 This Book: What It Is and Is Not 11 Endnotes 12 Chapter 2 Data Management and Preparation 15 Importance of Data Governance to the Risk Function 18 Fundamentals of Data Management 20 Other Data Considerations for AI, Machine Learning, and Deep Learning 22 Concluding Remarks 29 Endnotes 30 Chapter 3 Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management 31 Risk Modeling Using Machine Learning 35 Definitions of AI, Machine, and Deep Learning 40 Concluding Remarks 52 Endnotes 52 Chapter 4 Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models 55 Difference Between Explaining and Interpreting Models 57 Why Explain AI Models 59 Common Approaches to Address Explainability of Data Used for Model Development 61 Common Approaches to Address Explainability of Models and Model Output 62 Limitations in Popular Methods 68 Concluding Remarks 69 Endnotes 69 Chapter 5 Bias, Fairness, and Vulnerability in Decision-Making 71 Assessing Bias in AI Systems 73 What Is Bias? 76 What Is Fairness? 77 Types of Bias in Decision-Making 78 Concluding Remarks 89 Endnotes 89 Chapter 6 Machine Learning Model Deployment, Implementation, and Making Decisions 91 Typical Model Deployment Challenges 93 Deployment Scenarios 98 Case Study: Enterprise Decisioning at a Global Bank 101 Practical Considerations 102 Model Orchestration 103 Concluding Remarks 104 Endnote 104 Chapter 7 Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring 105 Establishing the Right Internal Governance Framework 108 Developing Machine Learning Models with Governance in Mind 109 Monitoring AI and Machine Learning 112 Compliance Considerations 122 Further Takeaway 125 Concluding Remarks 126 Endnotes 127 Chapter 8 Optimizing Parameters for Machine Learning Models and Decisions in Production 129 Optimization for Machine Learning 131 Machine Learning Function Optimization Using Solvers 133 Tuning of Parameters 136 Other Optimization Algorithms for Risk Models 141 Machine Learning Models as Optimization Tools 143 Concluding Remarks 147 Endnotes 148 Chapter 9 The Interconnection between Climate and Financial Instability 149 Magnitude of Climate Instability: Understanding the "Why" of Climate Change Risk Management 152 Interconnected: Climate and Financial Stability 157 Assessing the impacts of climate change using AI and machine learning 158 Using scenario analysis to understand potential economic impact 160 Practical Examples 170 Concluding Remarks 172 Endnotes 172 About the Authors 175 Index 177

TERISA ROBERTS, PHD, 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. STEPHEN J. TONNA, PHD, 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.