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.
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.
Acknowledgments
Future of Risk Modeling – Preface
CHAPTER 1: Introduction
Risk Modeling: Definition and Brief History
Use of AI and Machine Learning in Risk Modeling
The New Risk Management Function
Overcoming Barriers to Technology and AI Adoption with a Little Help from Nature
This Book: What It Is and Is Not
Endnotes
CHAPTER 2: Data Management and Preparation
Importance of Data Governance to the Risk Function
Fundamentals of Data Management
Other Data Considerations for AI, Machine Learning, and Deep Learning
Concluding Remarks
Endnotes
CHAPTER 3: Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management
Risk Modeling Using Machine Learning
Definitions of AI, Machine, and Deep Learning
Concluding Remarks
Endnotes
CHAPTER 4: Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models
Difference Between Explaining and Interpreting Models
Why Explain AI Models
Common Approaches to Address Explainability of Data Used for Model Development
Common Approaches to Address Explainability of Models and Model Output
Limitations in Popular Methods
Future of Explainability
Endnotes
Chapter 5: Bias, Fairness, and Vulnerability in Decision-Making
Assessing Bias in AI Systems
What Is Bias?
What Is Fairness?
Types of Bias in Decision-Making
Concluding Remarks
Endnotes
CHAPTER 6: Machine Learning Model Deployment, Implementation, and Making Decisions
Typical Model Deployment Challenges
Deployment Scenarios
Case Study: Enterprise Decisioning at a Global Bank
Practical Considerations
Model Orchestration
Concluding Remarks
Endnotes
Chapter 7: Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring
Establishing the Right Internal Governance Framework
Developing Machine Learning Models with Governance in Mind
Monitoring AI and Machine Learning
Compliance Considerations
Further Takeaway
Concluding Remarks
Endnotes
CHAPTER 8: Optimizing Parameters for Machine Learning Models and Decisions in Production
Optimization for Machine Learning
Machine Learning Function Optimization Using Solvers
Tuning of Parameters
Other Optimization Algorithms for Risk Models
Machine Learning Models as Optimization Tools
Concluding Remarks
Endnotes
CHAPTER 9: The Interconnection between Climate and Financial Instability: The Race to Understand and Counter the Impacts to Protect the Financial Sector
Magnitude of Climate Instability: Understanding the “Why” of Climate Change Risk Management
Interconnected: Climate and Financial Stability
For Firms, Understanding Potential Economic Impacts of Climate Risk Can Be Difficult
Practical Examples
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