1Chapter 1
1055.6 Exercise
21.1 Definition of AI
106Chapter 6
31.2 Overview of Risk Management
107AI in Risk Mitigation and Prevention
41.3 Importance of AI in Risk Management
1086.1 Definition
51.4 Future of the AI in Risk Management
1096.2 Traditional Approaches
61.4.1 Hyper-predictive AI
1106.2.1 Statistical Analysis and Modeling
71.4.2 Proactive Risk Automation
1116.2.2 Scenario Planning and Simulations
81.4.3 Explainable AI and Human-Machine Collaboration
1126.2.3 Rule-Based Systems and Expert Knowledge
91.4.4 Democratization of Risk Management
1136.2.4 Monitoring and Surveillance Systems
101.4.5 Ethical Considerations and Societal Impact
1146.2.5 Public Awareness and Education
111.5 Limitations and Scope for Further Research
1156.3 AI Techniques
121.5.1 Data Dependence
1166.3.1 Predictive Analytics and Machine Learning
131.5.2 Ethical Considerations
1176.3.2 Natural Language Processing (NLP) and Text Analytics
141.5.3 Lack of Common Sense and Adaptability
1186.3.3 Computer Vision and Image Recognition
151.5.3 Scope for Further Research
1196.3.4 Reinforcement Learning and Simulation
161.6 Summary
1206.3.5 Challenges and Considerations
171.7 Exercise
1216.4 Advantages
18Chapter 2
1226.4.1 Enhanced Early Warning and Predictive Capabilities
19Understanding Risk Management
1236.4.2 Improved Anomaly Detection and Threat Identification
202.1 Definition of Risk Management
1246.4.3 Streamlined and Automated Risk Management Processes
212.2 Types of Risks
1256.4.4 Enhanced Human Decision-Making and Risk Awareness
222.2.1 By Origin
1266.4.5 Cost-Effectiveness and Efficiency Gains
232.2.2 By Impact
1276.4.6 Continuous Learning and Improvement
242.2.3 By Probability
1286.4.7 Greater Personalization and Individualized Risk Assessment
252.2.4 By Domain
1296.4.8 Improved Regulatory Compliance and Legal Certainty
262.3 The Risk Management Process
1306.4.9 Enhanced Global Risk Management and Coordination: 6.4.10 Cost-Effectiveness and Efficiency Gains
272.3.1 Identifying Risks
1316.5 Disadvantages
282.3.2 Analyzing and Assessing Risks
1326.5.1 Data Bias and Algorithmic Fairness
292.3.3 Prioritization
1336.5.2 Explainability and Transparency Issues
302.3.4 Response Strategies
1346.5.3 Security and Privacy Concerns
312.3.5 Monitoring and Review
1356.5.4 Job Displacement and Automation Anxiety
322.4 Tools and Techniques used in Risk Management
1366.5.5 Overreliance on Technology and Reduced Human Oversight
332.4.1 Brainstorming Techniques
1376.5.6 Potential for Misuse and Discrimination
342.4.2 SWOT Analysis
1386.5.7 Lack of Standards and Regulations
352.4.3 FMEA (Failure Mode and Effects Analysis)
1396.5.8 Limited Generalizability and Adaptability
362.4.4 Contingency Planning
1406.5.9 Potential for Catastrophic Failure
372.4.5 Risk Management Software
1416.5.10 Lack of Public Trust and Understanding
382.4.6 Root Cause Analysis
1426.5.11 Potential for Manipulation and Social Engineering
392.4.7 Risk Culture Assessment
1436.5.12 Economic Inequality and Power Imbalance
402.5 Dangers of ignoring Risk Management in decision making
1446.5.13 Environmental Impact and Sustainability Concerns
412.6 Summary
1456.6 Case Studies
422.7 Exercise
1466.6.1 Real-time Fraud Detection in Banking
43Chapter 3
1476.6.2 Predictive Maintenance in Manufacturing
44AI in Risk Management: An Overview
1486.6.3 Proactive Cyberattack Prevention
453.1 Definition of AI in Risk Management
1496.6.4 Fraudulent Insurance Claims Detection
463.2 Historical Development
1506.7 Ethical considerations
473.3 AI Techniques used in Risk Management
1516.8 Summary
483.3.1 Machine Learning (ML)
1526.9 Exercise
493.3.2 Deep Learning (DL)
153Chapter 7
503.3.3 Natural Language Processing (NLP)
154AI in Risk Monitoring and Control
513.3.4 Computer Vision (CV)
1557.1 Overview
523.3.5 Bayesian Networks
1567.2 The Role of AI
533.3.6 Explainable AI (XAI)
1577.3 Machine Learning Algorithms
543.4 Advantages and Disadvantages
1587.3.1 Detection and Prediction
553.4.1 Advantages: 3.4.2 Disadvantages
1597.3.2 Optimization and Decision Making
563.5 AI and Human Control
1607.3.3 Clustering and Segmentation
573.5.1 AI’s Strengths
1617.3.4 Deep Learning
583.5.2 Humans Strengths
1627.4 Summary
593.5.3 Balancing the Powers
1637.5 Exercise
603.6 Case Studies of AI in Risk Management
164Chapter 8
613.6.1 Fraud Detection at Visa
165AI in Risk Communication
623.6.2 Predicting Equipment Failure at GE Aviation
1668.1 AI-based Tools
633.6.3 Cyberattack Mitigation at Bank of America
1678.1.1 Predictive analytics
643.6.4 Personalized Risk Scores at Lemonade Insurance
1688.1.2 Scenario planning
653.6.6 Security Risk Management at Marriott International
1698.1.3 Chatbots and virtual assistants
663.6.7 Anomaly Detection in Network Infrastructure (Google Cloud)
1708.1.4 Targeted messaging
673.6.8 Predictive Maintenance in Manufacturing (Siemens)
1718.1.5 Sentiment analysis
683.6.9 Fraud Detection in Online Transactions (PayPal)
1728.1.6 Crisis communication
693.6.10 Early Disease Detection (Mayo Clinic)
1738.1.7 Lexalytics Semantria
703.6.11 Medication Error Prevention (Stanford Medicine)
1748.1.8 Signal AI
713.6.12 Sepsis Prediction and Intervention (Cleveland Clinic)
1758.1.9 Ready.ai
723.7 Steps for Implementing AI in Risk Management
1768.2 AI and Social Media
733.8 How AI Can Help Assess Risks in Real-time?
1778.3 Legal and Ethical Implications
743.9 Analyzing Data with AI in Risk Management
1788.4 Summary
753.10 Integrating Machine Learning with Risk Management
1798.5 Exercise
763.11 Use Cases of AI in Risk Management
180Chapter 9
773.13 Summary
181AI in Financial Risk Management
783.14 Exercise
1829.1 Machine Learning Techniques and Algorithms
79Chapter 4
1839.1.1 Credit Risk Management
80AI in Risk Classification and Assessment
1849.1.2 Market Risk Management
814.1 Risk Classification and Assessment
1859.1.3 Fraud Detection
824.1.1 Risk Classification: 4.1.2 Risk Assessment
1869.1.4 Operational Risk Management
834.2 Traditional Approaches
1879.1.5 Portfolio Optimization
844.2.1 Risk Classification: 4.2.2 Risk Assessment
1889.2 Intelligent Risk Prediction Models for Investment
854.3 AI Techniques
1899.3 Credit Risk Modelling using AI
864.3.1 Risk Classification: 4.3.2 Risk Assessment
1909.4 Summary
874.4 Comparison of AI and Traditional Approaches
1919.5 Exercise
884.5 Types of Risks Assessed using AI
192Chapter 10
894.6 Data Collection Techniques
193AI in Cybersecurity Risk Management
904.7 Limitations
19410.1 AI for Intrusion Detection and Prevention: 10.2 Predictive Analytics for Vulnerability Management
914.8 Case Studies
19510.3 Ethical and Legal Implications: 10.4 Challenges and Limitations
924.9 Impact on Business and Society
19610.5 Summary
934.3.1 Business: 4.3.2 Society
19710.6 Exercise
944.10 Scope
198 Chapter 11
954.11 Summary
199Futuristic Studies in AI and Risk Management
964.12 Exercise
20011.1 Current Trends
97Chapter 5
20111.2 Prediction for the Future: 11.4 Theoretical Predictions and Speculations
98AI in Risk Estimation and Prediction
20211.5 Summary
995.1 Definition
20311.6 Exercise
1005.2 Challenges
204References
1015.3 AI Techniques Used
205Appendix A :Definitions
1025.4 Advantages and Disadvantages
206Appendix B :Acronyms
1035.4.1 Advantages: 5.4.2 Disadvantages
207Index
1045.5 Summary
