Most financial services data is already “connected.” It just isn’t modeled that way.
Fraud rings don’t show up as a single row. Money laundering doesn’t announce itself in one transaction. Counterparty exposure isn’t obvious from one booking. The meaningful signal lives in relationships: who shares an address, which accounts route funds through the same nodes, where devices and identities overlap, and how risk propagates through a network.
Graph in Microsoft Fabric is designed for exactly that: turning your OneLake data into a connected model you can explore visually, query with GQL, and enrich with built-in graph algorithms—without standing up a separate graph stack and duplicating data.
In financial services, this is the difference between “we have the data” and “we can reason over the connections.”
Continue reading “From Tables to Networks: A Deep Dive into Graph in Microsoft Fabric for Financial Services Insights”