Case Study: Tier 1 Bank saves over $10M annually with TigerGraph

Tier 1 Bank saves over $10M annually with TigerGraph

A tier 1 bank faced the challenge of detecting organized credit card fraud rings. Its existing fraud detection methods missed complex patterns, costing hundreds of millions annually, and legacy systems could not scale to handle over 50 million daily transactions while meeting a strict sub-80ms response time requirement. The bank turned to TigerGraph for a solution.

The bank implemented TigerGraph to connect six months of application data and identify relationships between known and unknown fraud. The graph analytics generated features for the bank's machine learning system in less than 80 milliseconds. This solution improved the accuracy and speed of fraud detection, saving TigerGraph's customer over $10 million annually and helping to protect more than 60 million U.S. households.