Financial fraud detection typically relies on analysis of event volumes and transaction size by user/device bypassing much of the categorical data that defines user/device behavior patterns in a much more complete context.
Financial fraud detection requires monitoring billions of transactions, devices and users in real-time for suspect behaviors without false positives that alienate customers when service is denied in the middle of a foreign vacation or late night business event.
What is needed is a system that do four things:
detect complex patterns of behavior
combine multiple sources and scale up to millions of events/sec
take the appropriate, user-specified action when patterns are detected
do all of this in real time
Quine can monitor device and user behavior over extended time periods to detect expected exploit behaviors and new, novel, threat actions. By including categorical data such as store names, item types or sizes, geo locations, device versions, and day of the week, Quine understands the full context of behavior, eliminating false-positives. Additionally, Quine alerts provide a comprehensive view of past and current behavior for a device or user as supporting data for investigations.
Key Value Delivered
Behavior modeling for billions/trillions of users, devices and transactions
High-confidence risk scoring by leveraging the rich behavior context provided by categorical data analysis
Human-understandable alert information to support analysts investigations