Case Study: Major Financial Institution achieves $50M in savings with TigerGraph graph analytics

a major financial institution saves $50M with TigerGraph

A major global bank faced a significant challenge with its existing fraud detection methods, which missed complex patterns and cost the institution hundreds of millions annually. Its legacy systems struggled with accuracy and could not scale to handle over 50 million daily transactions while meeting a critical sub-80ms response time requirement. The bank turned to TigerGraph to address this.

By implementing TigerGraph’s high-speed graph database, the bank revolutionized its fraud detection with real-time analytics. The solution integrated machine learning algorithms to detect sophisticated fraud through complex queries, processing over 30TB of data. This resulted in $50 million in operational savings, protected 60 million households, and delivered real-time transaction analysis with sub-80ms response times.

Major Financial Institution