Industry · Fintech
Graphs for fintech
Money movement is a network. The questions that matter — who's connected to whom, through what, how many hops from a flagged entity — are traversals, and forcing them through joins is where risk pipelines go to die.
Risk queries in the decision path
Short traversals return fast enough to sit inline: shared-identifier checks at onboarding, path-to-flagged-entity checks at transfer time.
Ownership chains at arbitrary depth
Beneficial ownership is a variable-length path problem. [:OWNS*1..N] follows the chain as far as it goes — the depth is data, not schema.
Auditability by construction
Separate data-path and console credentials, an audit log of every administrative change, and encrypted backups that restore to a new instance so investigation never mutates evidence.
Where to start
The solutions fintech teams reach for.
Manage risk
Fraud detection
Fraud hides in relationships — shared cards, devices and addresses. Model transactions as a graph and expose rings that row-based queries can't reach.
Read moreManage risk
Identity resolution
The same person exists three times across your systems. Model records and their attributes as a graph and merge duplicates by walking what they share.
Read moreBuild with AI
Agent memory
Give an agent a database it can query instead of a transcript it must re-read. One instance per agent or session, connected over MCP.
Read moreStart now
~98.7%
token efficiency at 2,000 entities — see the footnotes above
Put your first graph up in a minute.
A free instance takes about a minute and no card. Write two MERGE statements, read them back, and you have a living graph — with provenance on every fact.
First-graph path
LiveCreate a free instance
No card. Ready in about a minute.
Connect your driver
bolt+ssc:// URI into the driver you already use.
Write two MERGEs
That's the entire shape of agent memory.
Point an agent at it
One MCP config block. No integration code.