Case Study: BNP Paribas Personal Finance reduces fraud by 20% with Neo4j

BNP Paribas Personal Finance reduces fraud by 20% with Neo4j

BNP Paribas Personal Finance, a retail financing subsidiary of the BNP Paribas Group, faced the challenge of detecting sophisticated fraud rings that manipulated personal information across thousands of credit applications. Their existing relational database was unable to uncover complex relationships in their data in real time. To overcome this, they turned to vendor Neo4j and its graph database technology to build a more effective fraud detection system.

By implementing Neo4j's graph database, the company developed a real-time fraud detection scoring model that identifies intricate patterns and connections between applications. This solution, achieved through close collaboration with Neo4j, resulted in a 20% reduction in total fraud while maintaining high loan volumes. The system processes data with a maximum latency of just two seconds, successfully thwarting fraud that would have previously gone undetected.

BNP Paribas Personal Finance