Risk Modeling

Risk Modeling

by Terisa Roberts, Stephen J. Tonna
Released September 2022
Publisher(s): Wiley
ISBN: 9781119824930

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Book description

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. 

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Acknowledgments
  5. Preface
    1. FUTURE OF RISK MODELING
  6. CHAPTER 1: Introduction
    1. RISK MODELING: DEFINITION AND BRIEF HISTORY
    2. USE OF AI AND MACHINE LEARNING IN RISK MODELING
    3. THE NEW RISK MANAGEMENT FUNCTION
    4. OVERCOMING BARRIERS TO TECHNOLOGY AND AI ADOPTION WITH A LITTLE HELP FROM NATURE
    5. THIS BOOK: WHAT IT IS AND IS NOT
    6. ENDNOTES
  7. CHAPTER 2: Data Management and Preparation
    1. IMPORTANCE OF DATA GOVERNANCE TO THE RISK FUNCTION
    2. FUNDAMENTALS OF DATA MANAGEMENT
    3. OTHER DATA CONSIDERATIONS FOR AI, MACHINE LEARNING, AND DEEP LEARNING
    4. CONCLUDING REMARKS
    5. ENDNOTES
  8. CHAPTER 3: Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management
    1. RISK MODELING USING MACHINE LEARNING
    2. DEFINITIONS OF AI, MACHINE, AND DEEP LEARNING
    3. CONCLUDING REMARKS
    4. ENDNOTES
  9. CHAPTER 4: Explaining Artificial Intelligence, Machine Learning, and Deep Learning Models
    1. DIFFERENCE BETWEEN EXPLAINING AND INTERPRETING MODELS
    2. WHY EXPLAIN AI MODELS
    3. COMMON APPROACHES TO ADDRESS EXPLAINABILITY OF DATA USED FOR MODEL DEVELOPMENT
    4. COMMON APPROACHES TO ADDRESS EXPLAINABILITY OF MODELS AND MODEL OUTPUT
    5. LIMITATIONS IN POPULAR METHODS
    6. CONCLUDING REMARKS
    7. ENDNOTES
  10. CHAPTER 5: Bias, Fairness, and Vulnerability in Decision-Making
    1. ASSESSING BIAS IN AI SYSTEMS
    2. WHAT IS BIAS?
    3. WHAT IS FAIRNESS?
    4. TYPES OF BIAS IN DECISION-MAKING
    5. CONCLUDING REMARKS
    6. ENDNOTES
  11. CHAPTER 6: Machine Learning Model Deployment, Implementation, and Making Decisions
    1. TYPICAL MODEL DEPLOYMENT CHALLENGES
    2. DEPLOYMENT SCENARIOS
    3. CASE STUDY: ENTERPRISE DECISIONING AT A GLOBAL BANK
    4. PRACTICAL CONSIDERATIONS
    5. MODEL ORCHESTRATION
    6. CONCLUDING REMARKS
    7. ENDNOTE
  12. CHAPTER 7: Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring
    1. ESTABLISHING THE RIGHT INTERNAL GOVERNANCE FRAMEWORK
    2. DEVELOPING MACHINE LEARNING MODELS WITH GOVERNANCE IN MIND
    3. MONITORING AI AND MACHINE LEARNING
    4. COMPLIANCE CONSIDERATIONS
    5. FURTHER TAKEAWAY
    6. CONCLUDING REMARKS
    7. ENDNOTES
  13. CHAPTER 8: Optimizing Parameters for Machine Learning Models and Decisions in Production
    1. OPTIMIZATION FOR MACHINE LEARNING
    2. MACHINE LEARNING FUNCTION OPTIMIZATION USING SOLVERS
    3. TUNING OF PARAMETERS
    4. OTHER OPTIMIZATION ALGORITHMS FOR RISK MODELS
    5. MACHINE LEARNING MODELS AS OPTIMIZATION TOOLS
    6. CONCLUDING REMARKS
    7. ENDNOTES
  14. CHAPTER 9: The Interconnection between Climate and Financial Stability
    1. MAGNITUDE OF CLIMATE INSTABILITY: UNDERSTANDING THE “WHY” OF CLIMATE CHANGE RISK MANAGEMENT
    2. INTERCONNECTED: CLIMATE AND FINANCIAL STABILITY
    3. ASSESSING THE IMPACTS OF CLIMATE CHANGE USING AI AND MACHINE LEARNING
    4. USING SCENARIO ANALYSIS TO UNDERSTAND POTENTIAL ECONOMIC IMPACTS
    5. PRACTICAL EXAMPLES
    6. CONCLUDING REMARKS
    7. ENDNOTES
  15. About the Authors
  16. Index
  17. End User License Agreement

Product information

  • Title: Risk Modeling
  • Author(s): Terisa Roberts, Stephen J. Tonna
  • Release date: September 2022
  • Publisher(s): Wiley
  • ISBN: 9781119824930