### Q1: What is the formula for calculating accuracy in AI/ML models, and why can it be misleading in imbalanced datasets?  
### Q2: How does precision differ from recall in terms of their importance in financial predictions, and can you provide an example where one is prioritized over the other?  
### Q3: Explain how the F1-score can provide a balanced view of model performance in scenarios with uneven class distribution.  
### Q4: In what ways can continuous evaluation of model performance impact a finance business's ability to adapt to market changes?  
### Q5: Describe a situation where failing to evaluate a model's performance could lead to significant financial loss in a fintech context.  
### Q6: How can stakeholders' trust in a finance company be influenced by regular reporting on model efficacy?  
### Q7: What communication strategies would you use to explain the importance of precision to a non-technical stakeholder?  
### Q8: How can you effectively communicate the implications of a high recall rate to your team, ensuring they understand the risks involved?  
### Q9: Share an example of how you would present insights gained from model performance metrics to a diverse audience, ensuring clarity and engagement.