Financial Crime Network Investigation
Money-laundering rings operate through webs of accounts, shell companies and shared devices, but transaction monitoring flags one account at a time — so banks close individual accounts while the network simply reroutes.
What stands in the way
Money-laundering rings operate through webs of accounts, shell companies and shared devices, but transaction monitoring flags one account at a time — so banks close individual accounts while the network simply reroutes.
How Scrydon solves it
A knowledge graph resolves customers, accounts, devices, addresses and counterparties into one entity graph, so investigators expand outward from a single alert to map the full ring before acting.
How this plays out
A mule network is designed to look unremarkable one account at a time: each account stays under thresholds, each customer looks legitimate, and the only thing that gives the ring away — shared devices, recycled addresses, circular flows between counterparties — is exactly what account-by-account monitoring can't see.
The Enterprise Knowledge Graph resolves customers, accounts, devices and counterparties into one entity graph inside the bank's own infrastructure, so when one account trips an alert, an investigator expands the graph around it and sees the shared phone linking it to eleven others — filing one network-level case instead of eleven disconnected ones, with the graph evidence attached.
Entire networks identified and dismantled from a single alert, instead of a years-long game of closing one account at a time.
See how this works for your organisation
Let's map this financial services use case onto your environment, your data and your sovereignty requirements.