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Use Case

Financial Fraud Detection

  • The Problem

    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.

  • The Solution

    What is needed is a system that do four things:

    1. detect complex patterns of behavior
    2. combine multiple sources and scale up to millions of events/sec
    3. take the appropriate, user-specified action when patterns are detected
    4. 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

  • Cost effective at scale with on premise licensing

  • Integrates with existing Apache Kafka, AWS Kinesis, data lake, and API event sources.

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