BioCatch's Cross-Bank Fraud Network

BioCatch gets stronger as more banks feed it more strange behavior to learn from. Every login, payment, and account change adds more examples of what normal customers and fraud rings actually look like in the wild, which improves detection across the whole customer base. That makes the product harder to match with a simple rules engine, because the edge comes from shared patterns learned across hundreds of institutions, not just one bank’s own history.

• The raw product workflow is simple. BioCatch watches how someone types, swipes, moves a mouse, and behaves during a session, then scores risk in real time. More protected sessions mean more labeled examples of scams, bots, and mule activity, which sharpens the model for every bank on the network.
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• This is the same moat seen in other fraud networks, but applied to banking behavior. LexisNexis says its Digital Identity Network processed 92 billion transactions in 2023 and matches signals across customers, while Mastercard says NuData analyzes 80 billion online interactions yearly. Scale matters because fraud patterns often repeat across institutions before any one bank sees the full picture.
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• BioCatch is pushing beyond a model flywheel into a true network product with BioCatch Trust. In Australia, five major banks share real time intelligence on suspicious receiving accounts before money moves. That turns pooled data into direct cross bank prevention, which is a stronger form of network effect than better scoring alone.
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The next step is a shift from selling software to individual banks toward operating shared fraud infrastructure for the banking system. As scam regulation tightens and banks need to stop authorized payments before funds leave, the vendors with the broadest cross institution visibility are positioned to capture more budget and become embedded deeper in payment workflows.