Banking · Insurance · Payments
Financial institutions
Model accounts, transactions and counterparties as one temporal graph — indexed the moment data lands, and queryable as-of any point in time. One engine for the ledger, the relationships and the history, so you can decide, detect and forecast on live data instead of yesterday's batch.
Install GreyCat free Talk to us
The problem
In most banks the core ledger, the fraud engine, the risk model and the audit trail each live in a different system, stitched together by overnight batches. By the time the data reconciles, the decision window has already closed — the loan is approved or declined, the payment has cleared, the fraud ring has moved on. And when a regulator asks what a balance or an exposure looked like on a given date, reconstructing that as-of state across systems is slow and error-prone.
How GreyCat solves it
GreyCat indexes every transaction as it arrives and stores it as a versioned edge in a graph that also holds accounts, customers and counterparties as nodes. Because the graph, the time-series and the machine learning live in one engine, you can traverse relationships, replay any point in time and run forecasts in a single query — no ETL between a graph DB, a time-series DB and a warehouse.
Fast transaction indexing
Millions of transactions ingested and indexed per second — queryable the instant they land, not after the nightly batch.
Temporal graph
Accounts, customers and counterparties linked by transactions — every node and edge versioned in time, so relationships and values are never overwritten.
Point-in-time queries
Ask what any balance, exposure or relationship looked like as-of any timestamp — audit, compliance and back-testing on the same live data.
One self-hosted engine
Graph, time-series, ML and audit in one binary on your own hardware — regulated data never leaves the bank.
What a temporal graph gives a bank
A transaction is never an isolated row. It connects two parties, carries a value, and happens at a moment in time. GreyCat keeps all three: the graph of who paid whom, the time-series of how each balance moved, and the ability to replay or forecast either one.
Traverse a counterparty network, reconstruct an account's exact state last quarter, and project its balance forward — all in the same store, in one query.
Use cases
The same temporal graph powers decisions that all depend on the same thing: the right relationships and the right history, fast enough to act on.
Instant lending decisions
The problem. Deciding whether to grant a loan or a limit means pulling an applicant's full history and their web of obligations and connected entities — usually a slow join across several systems, so "instant" credit isn't really instant.
Why GreyCat. An applicant's entire transaction history and counterparty graph sit in one indexed store. Traverse income sources, existing obligations and related parties, evaluate a scoring model, and return an approve/decline — in milliseconds, on point-in-time-correct data.
Result. Real-time credit decisions at the point of sale or in the app, backed by the exact data state used to make them.
Fraud & AML detection
The problem. Fraud and money-laundering rarely show up in a single transaction. They surface as patterns across the graph over time — rings, mule chains, sudden bursts of activity — which row-by-row checks and siloed systems miss.
Why GreyCat. Traverse the transaction graph and its temporal windows in one query: follow the money across hops, spot circular flows and velocity spikes, and score anomalies against machine-learning models running in the same engine — as transactions happen.
Result. Suspicious patterns flagged in real time, with the full graph and history on hand to investigate and explain them.
Predictive balance & liquidity
The problem. Treasury needs to know not just today's balances but tomorrow's — per account, per branch, and for the bank as a whole — yet forecasts live in spreadsheets and models disconnected from the live ledger.
Why GreyCat. Built-in machine learning forecasts each account's future balance from its own time-series, then rolls those forecasts up into branch- and bank-wide liquidity — and lets you stress-test scenarios against the same live data.
Result. Forward-looking liquidity you can plan against, refreshed continuously instead of assembled once a month.
Figures are GreyCat's own documented engine benchmarks, not a specific customer result; real-world throughput depends on hardware and schema. See the benchmarks →