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

Cover -- Title Page -- Copyright Page -- Contents -- Acknowledgments -- 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 Managementand Preparation -- Importance of Data Governance to the Risk Function -- Fundamentals of Data Management -- Master Data Management -- Standardizing Datasets and Ensuring Data Quality -- Other Data Considerations for AI, Machine Learning, and Deep Learning -- Utilizing "Alternative Data" -- Extending Risk Data to "Alternative Data" for AI and Machine Learning -- Synthetic Data Generation -- Typical Data Preprocessing, Including Feature Engineering -- Concluding Remarks -- Endnotes -- Chapter 3 Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management -- Risk Modeling Using Machine Learning -- Tier 1 Commercial Bank in Latin America -- Tier 1 Financial Institution in Asia Pacific -- Process Automation for Claims Processing -- Navigating through the Storm of COVID-19 -- Approximation of Complex Risk Calculations -- Definitions of AI, Machine, and Deep Learning -- Artificial Intelligence -- Machine Learning -- Deep Learning -- Putting It All Together -- 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 -- Concluding Remarks -- Endnotes.