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.

What people are saying

thatDot Platform’s advanced Novelty Scoring for categorical data feature is the future of anomaly detection. Its speed powers our new real-time services while also significantly reducing false-positive findings for our customers.

Gery Szlobodnyik

CEO

The security industry is headed towards leveraging knowledge graphs of all the relevant evidence provided by threat groups, network indicators and endpoint artifacts to quickly identify and mitigate threats. The security companies that can most confidently extract relevant context from these graph and then immediately action on findings to mitigate threats will be the winners. This is exactly the problem Quine aims to address.

Evan Wright

Staff Data Scientist

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If you think Streaming Graph or Novelty might be for you, contact us to see them in action.

Financial Fraud Detection

The Problem

The Solution

Key Value Delivered

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