9781119824930

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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Risk Modeling Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning

Risk Modeling Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning

1119824931

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