From risk management to fraud detection, context is key. Fraud hides in relationships: shared devices, recycled addresses, circular money flows. In a table it looks like noise. In a graph it looks exactly like what it is.
One flagged account, five hidden connections, all visible in a single query.
Financial firms face increasing regulation on risk and compliance reporting. Rather than reconciling silos by hand, teams unite transaction, customer, and device data into one graph, so suspicious structure is detected as it forms, every alert carries an explainable path, and time-travel reconstructs exactly what was known when a decision was made. The bottom line: timeliness and accuracy in risk analytics and compliance reporting.
Unite data silos into a federated graph model. Trace and analyze exposure across the enterprise, and update the model in real time instead of re-running quarterly extracts.
Circular transaction patterns, collusion networks, and shared identifiers surface as graph structure, detected by shape, fast enough to block rather than just report.
Fraud ops, AML, and credit risk query the same graph instead of three copies of the truth. An investigation started in one team continues in another without a data hand-off.
Accounts, devices, addresses, and beneficiaries resolved around one customer: the context that turns a borderline score into a confident decision.
How it's built, and the reproducible numbers behind it.
SDKs, query examples, and local setup: what's live and what's coming.
Five phases, honestly labelled: shipped, live, in development.
8 questions, free gap analysis against EU AI Act and SR 11-7.
Book a session and we’ll walk this use case end to end on real data: architecture, queries, and what a 90-day path looks like for your team.