Excerpt from article by Emil Eifrem for CTOvision.com
Stopping the fraudsters connections
Fraud detection applications are not a new concept, but continuously changing techniques used by sophisticated fraud rings are making their detection far more difficult. Gartner cautions “Don’t consider legacy fraud detection technology adequate if the vendor fails to keep up with criminal trends. Replace or complement the technology with solutions from vendors that continue to innovate, which is a necessity when combating rapidly evolving criminal behavior” (See Gartner’s Market Guide for Online Fraud Detection)
This is where the power of the graph database comes in.
Graph databases have been developed to work with data at scale, by manipulating the patterns within it. Graph databases, utilized together with a data query languages like Cypher, provide a simple semantic for detecting fraud rings and navigating the data connections in-memory, even in real time. This makes noticing the connections between fraudsters and their activities far more open to detection.
Unlike most other ways of looking at data, graph databases are designed to exploit relationships in data, which means they can uncover patterns difficult to detect using traditional representations such as tables. Forrester says over a quarter of enterprises will be using such databases by 2017, for instance (see Forrester’s Graph Database market overview).