The financial services industry relies heavily on AI and Machine Learning solutions across all business functions and services. However, due to regulatory compliance concerns, most models and AI systems are built with conservative operating environment assumptions. In recent months/years, major global shifts have occurred across the globe triggered by the Covid pandemic. These abrupt changes impacted the environmental assumptions used by AI/ML systems and their corresponding input data patterns (ranging from changes in behavioral patterns such as online/in-person shopping patterns and spending to market risk assessment models and to trading patterns). As a result, many AI/ML systems faced serious performance challenges and failures. Industry-wide reports highlight large-scale remediation efforts to fix the failures and performance issues. Yet, most of these efforts highlighted the challenges of model governance and compliance processes. This workshop starts with the acknowledgment of the fundamental challenges of robustness and adaptiveness in financial services modeling and explores systematic solutions to solve these underlying problems to prevent future failures. Some of the key questions to be explored include:

1. Why did so many AI/ML models fail during the pandemic?

2. What are the primary lessons learned from the model failures?

3. What techniques and approaches can be used to detect and effectively manage similar scenarios in the future (such as major data shifts)?

4. What approaches are emerging in building fundamentally robust and adaptive AI/ML systems?

5. How can the financial services industry balance the regulatory compliance and model governance pressures with adaptive models?

6. Are there emerging solution approaches to achieve key performance indicators in robustness, adaptiveness, compliance/governance to prepare for a rapidly changing world?