Hidden relationships between corporate entities are often missing from traditional risk systems, thus leading to underestimated exposures. This session demonstrates how Databricks + Neo4j can transform credit risk modeling. We’ll integrate ownership structures and transactions into Delta Lake and apply Cypher graph queries to detect circular ownership, shared liabilities, and exposure chains. With Databricks ML, we’ll take these graph insights further, enabling predictive models for entity-level risk and portfolio concentrations.

Attendees will see a novel architecture that unifies graph algorithms with the scalability of Databricks, delivering faster and smarter credit risk decisions.