Risk Modeling Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning
Produktdetaljer
Biografisk notat
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.
A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation
Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning 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.
Authors 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:
- Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk
- Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques
- Covers the basic principles and nuances of feature engineering and common machine learning algorithms
- Illustrates 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
- Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners
Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
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
Praise for Risk Modeling
“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.”
—RAYMOND ANDERSON, Rayan Risk Analytics
“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.”
—NAEEM SIDDIQI, Senior Risk Advisor, SAS Institute
“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.”
—ALAN FORREST, Advisory Senior Manager, Model Risk Oversight
"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."
—DAVID ASERMELY, Global Lead, Model Risk Management
In Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning, 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.
You’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.
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, Risk Modeling 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.
Perfect for C-suite executives, risk managers, and other business leaders, Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning 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.
Produktdetaljer
Biografisk notat
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.