Case Study: Fortune 500 Financial Services Company achieves real-time fraud detection, halves manual review time and saves millions with Neo4j

Real-time Graph Analysis of Financial Data Creates Potential for Millions in Fraud Detection Savings

A Fortune 500 financial services company that processes millions in monthly transactions needed to analyze vast, real-time customer and third‑party data to detect fraud, but their SQL Server–based workflow required complex multi-join queries that could take five minutes or more. With 10,000 transactions reaching analysts daily and data visualizations that were hard to interpret under time pressure, the company faced a scalability and SLA bottleneck for manual reviews and faster fraud detection.

They adopted Neo4j’s graph database and visualization to perform efficient relationship queries and real‑time analysis; a lightweight pilot and training enabled rapid rollout. The new approach halved manual review time, nearly doubled the number of transactions reviewed daily, uncovered previously hidden fraud clusters, and integrated with their decision platform to stop fraudulent transactions in real time—yielding millions in potential annual savings.