OntoGraph catches fraud by analysing the connections between transactions, devices and accounts, in real time.
Rule engines and tabular models score each payment as an isolated row. The fraud ring, the synthetic identity and the laundering chain only exist in the links between rows.
Fraud rings, synthetic identities and laundering chains share devices and IPs and move money in circular flows. Rule engines and tabular ML models score transactions as isolated rows, missing the graph of connections that links them into a fraud network.
With the vast majority of transactions legitimate, static thresholds and uncalibrated models flood review queues with false flags. Every one is a declined customer and an analyst hour. Because verdicts never feed back into the model, the noise never improves.
Attackers automate at the speed of an API call, route around static rules within days, and silently decay detection models through drift, all while regulators demand traceable evidence for every decline.
The crime only shows up in the connections: shared devices, shared phones, money moving in circles. Score transactions as isolated rows and you are blind by design. We build graphs because the signal does not exist anywhere else.
A criminal can invent a new name every day, but the device, the phone and the address they touch are expensive to change at scale. Graphs follow the trail of what fraudsters touch, and the trail is hard to fake.
When an investigator confirms a fraud ring, every connected device, card and account inherits that verdict, and anything that links to it later is flagged on arrival. Row-based systems stay the same. Graphs compound.
A black-box alert gives an investigator a number. A graph gives them the why: this card was used on a device that touched 3 flagged accounts. That traceability is what investigators act on and what regulators demand.
