### Q1: What is an algorithm, and how can it be applied to make predictions in the finance industry?  
### Q2: How do neural networks differ from traditional algorithms, and what is their significance in assessing credit risk?  
### Q3: What are the key components to consider when developing a robust AI model for predicting loan defaults?  
### Q4: How can the AI lifecycle framework assist finance professionals in launching effective AI initiatives?  
### Q5: What steps should be taken during the data collection phase to ensure the quality of data for AI training?  
### Q6: How does the deployment phase of the AI lifecycle impact the real-time decision-making capabilities of financial institutions?  
### Q7: Why is effective communication important when explaining complex AI concepts to stakeholders in the finance sector?  
### Q8: How can professionals in finance convey the importance of using diverse datasets to non-technical team members?  
### Q9: What strategies can be employed to facilitate clear communication between technical teams and business stakeholders during AI project development?