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Stop Fraud Rings in Their Tracks With Graph Databases [infographic]

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Stop Fraud Rings in Their Tracks With Graph Databases [infographic]

So how can fraud detection experts catch today’s highly sophisticated and organized fraud rings? With the power of graph database like Neo4j.

· Database Zone
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Read why times series is the fastest growing database category.

Fraud rings are big business.

First-party bank fraud costs banks (and their customers) over $16 billion each year in the United States, and insurance fraud costs nearly $80 billion annually. And, fraud rings organized around e-commerce fraud rack up nearly $4 billion in fraudulent charges each year in the U.S.

As a result of these staggering figures, it’s clear that fraud detection is a high stakes game.

Traditional fraud analytics look at discrete, isolated incidents but typically overlook fraud ring activity that appears normal on the surface.

So, how can fraud detection experts catch today’s highly sophisticated and organized fraud rings?With the power of graph database like Neo4j. 

Check out this infographic below to discover how graph databases help enterprises and governments detect—and prevent—fraud ring behaviors in real time.

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Learn how to harness the power of graph databases for real-time fraud detection with this white paper, Fraud Detection: Discovering Connections with Graph Databases.

Learn how to get 20x more performance than Elastic by moving to a Time Series database.

Topics:
insurance ,analytics ,detection ,fraud ,ecommerce ,fraud detection

Published at DZone with permission of Bryce Merkl Sasaki, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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