Antifraud Ownership Screening

Uncover Hidden Risks in Connections

Free online tool that showcases the power of Graphlytic on data from the Open Ownership Register. Graphlytic uses interactive graph visualization to give a comprehensive view of company ownership structures in an easily digestible form.

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Here is a breakdown of how Graphlytic is used in antifraud

Modern fraud isn't a series of isolated events; it's a sophisticated, hidden network. Traditional analysis tools, like spreadsheets or tables, are blind to these connections. Graph technology changes the game by focusing on the relationships between data points. It maps entities (people, accounts, devices, companies) as nodes and their connections (transactions, shared addresses, ownership) as edges. This approach reveals critical patterns that are otherwise invisible.

Here are core ways how Graphlytic revolutionize antifraud:

  • Uncovering Hidden Networks and Fraud Rings This is the primary power of Graphlytic. Where an analyst sees three separate, "low-risk" bank applications, a graph instantly visualizes that all three "different" applicants are connected to the same single "burner" phone number or login IP address. This connection, invisible in a table, immediately exposes a coordinated fraud ring. It turns multiple seemingly unrelated events into one high-risk, connected network, allowing analysts to see the true coordinated nature of the attack.
  • Tracing Complex Money Flows (AML) In Anti-Money Laundering (AML), criminals hide illicit funds by moving them through dozens of shell companies and mule accounts. Graphlytic makes "following the money" a visual, interactive process. An investigator can instantly trace the path of funds across multiple "hops" to find the true origin and destination. Graphs excel at spotting complex typologies like "layering" (splitting funds) or "circular" payment loops (e.g., Company A pays B, B pays C, C pays A), patterns designed specifically to confuse traditional, linear analysis.
  • Identifying Critical "Super-Connectors" Not all fraudsters are equal. Every network has "ringleaders" or central "mule" accounts that are far more important than others. Graph algorithms (like centrality analysis) automatically identify these key players. Instead of chasing dozens of small, low-level fraudsters, investigators can use the graph to pinpoint the "super-connectors" or central entities. This allows teams to prioritize their resources, focusing on the high-impact targets to dismantle the entire network far more effectively.
We are using data about legal entities from the world-renowned Open Ownership Register.
We are using data about legal entities from the world-renowned Open Ownership Register.
A graph database is used to represent the data as nodes and relationships.
A graph database is used to represent the data as nodes and relationships.
Visualization powered by Graphlytic is used to show complex relationships in a single picture.
Visualization powered by Graphlytic is used to show complex relationships in a single picture.