Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning
- Length: 208 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2022-09-27
- ISBN-10: 1119824931
- ISBN-13: 9781119824930
- Sales Rank: #658285 (See Top 100 Books)
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.
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 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 Current Guidance, Laws, and Regulations Methods and Measures to Address Bias and Fairness Using AI and Machine Learning to Detect and Remediate Bias: A Word of Caution Vulnerability Concluding Remarks Endnotes Chapter 6 Machine Learning Model Deployment, Implementation, and Making Decisions Typical Model Deployment Challenges Lack of Structured Deployment Processes The Need to Manually Recode Complex Models Managing Multiple Analytical Tools and Programming Languages Signoff and Approvals Adoption of Agile Practices for ModelOps Deployment Scenarios Deploying Models in Batch Processes Deploying Models in Real Time Deployment of Models in Database Management Systems Deployment of Models to Lightweight Containers Deployments in Business Decision Workflows Case Study: Enterprise Decisioning at a Global Bank Practical Considerations Begin with the End in Mind Continuous Model Monitoring Model Orchestration Concluding Remarks Endnote 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 Model Decay Stability Population Drift Feature Drift Robustness, Benchmarking, and Backtesting Interpretability Variable Importance Partial Dependence Individual Conditional Expectation Shapley Values Anomaly Detection Bias Compliance Considerations GDPR (Global Data Protection Regulation) ECOA (Equal Credit Opportunity Act) SR-Letter 11-7 EU Guidelines for Trustworthy AI Further Takeaway Concluding Remarks Endnotes Chapter 8 Optimizing Parameters for Machine Learning Models and Decisions in Production Optimization for Machine Learning Solvers for When the Target Objective Function Is Convex Tuning of Parameters Other Optimization Algorithms for Risk Models Logistic Regression Neural Networks Decision Science Optimization Tool to Reduce Credit Decisioning Policy Rules Concluding Remarks Endnotes Chapter 9 The Interconnection between Climate and Financial Stability Magnitude of Climate Instability: Understanding the “Why” of Climate Change Risk Management Climate Change Crisis: Not Just about CO2 Emissions United Nations and Climate Change Limitations of the Paris Accord Target Interconnected: Climate and Financial Stability Assessing the impacts of climate change using AI and machine learning Using scenario analysis to understand potential economic impacts Regulatory Guidance and Compliance Measures Stress Testing: Getting a Foot in the Door Firms Can Start by Strengthening Their Analytics Frameworks Practical Examples Climate Risk Management Solution Environmental, Social, and Governance Application in APAC-Based Financial Companies Sustainability Investment Screening Concluding Remarks Endnotes About the Authors Index EULA
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