Cindy Tu

• Ensuring the needs of the business are top of mind when crafting a data strategy. 
• How to identify key use cases that can spearhead transformation. 
• Ways to improve data literacy across the organization and inside individual business units 
• To Establish a framework for continuous monitoring and optimization to ensure long-term success in data transformation initiatives. 

The rapid advancement of AI technologies is transforming the landscape of model and data governance in financial institutions. As AI systems become more sophisticated and integral to operations, data managers face new challenges in ensuring compliance, transparency, and ethical use of these technologies. The evolving nature of AI necessitates a proactive approach to governance frameworks that can adapt to emerging risks and regulatory requirements. Join us as we explore the key considerations for evolving model and data governance in the age of AI for financial institutions and learn how:

• Implement AI-specific governance policies that address model explainability, bias detection, and algorithmic accountability.
• Establish cross-functional teams to oversee AI development, deployment, and ongoing monitoring throughout the model lifecycle.
• Develop robust data quality and lineage processes to ensure the integrity and traceability of AI training and operational data.
• Foster a culture of responsible AI by engaging stakeholders, providing comprehensive training, and recognizing champions who drive governance initiatives