### Q1: What is anomaly detection and how does it differ from traditional rule-based fraud detection systems?  
### Q2: Can you explain the role of predictive modeling in enhancing fraud detection strategies?  
### Q3: What are the key differences between supervised and unsupervised learning in the context of fraud detection?  
### Q4: How can machine learning models improve the accuracy of fraud detection in real-time transactions?  
### Q5: In what ways can behavior analytics contribute to identifying fraudulent activities in financial transactions?  
### Q6: How might the integration of machine learning models speed up response times in fraud detection?  
### Q7: How would you communicate the importance of machine learning in fraud detection to non-technical stakeholders?  
### Q8: What strategies can be employed to effectively present complex fraud detection concepts to a diverse audience?  
### Q9: How can you ensure that your communication about fraud detection techniques is both clear and engaging for your team?