The document discusses lessons learned from building AI models in the financial services industry. It highlights two main challenges - data is often scarce or restricted due to privacy concerns, and data comes from multiple jurisdictions with different regulations. It provides examples of using synthetic data and country-specific iterative modeling to address these challenges. The key takeaways are that rich yet difficult to acquire data is appealing but challenging for AI in financial services, and multi-jurisdiction modeling requires an end-to-end MLOps framework to develop machine learning at scale.