Community banks hold more data than ever, yet most struggle to convert it into measurable financial outcomes. Growth, efficiency, fraud reduction, personalization– every strategic priority depends on intelligence. But intelligence only emerges when two disciplines operate together: data science, which builds the models, and data governance, which ensures those models are trustworthy, explainable, and ready for real world decisioning.

This session shows leaders how to combine both to turn dormant data into active revenue. Most banks chase AI before fixing the data foundation required to make it work. The result is predictable: stalled initiatives, inconsistent outputs, and prototypes that never reach production.

This session begins by exposing the root cause (fragmented data) and demonstrates how a modern foundation transforms model performance, adoption, and financial impact.

Learning Objectives:

• Learn the specific model types that reliably drive revenue across institutions of all sizes including: fraud detection, credit risk, segmentation, and churn models.
• Learn to build a production-ready model pipeline from data ingestion, feature engineering, and training to deployment, monitoring, and drift management.
• Learn how to operationalize intelligence across teams and businses units from data science and governance to risk and compliance.