The bank was losing millions of dollars annually due to fraudulent transactions. Traditional fraud detection methods were insufficient against sophisticated fraudsters who created synthetic identities and operated in fraud rings.

Implemented network analysis to detect fraud rings.

1. Fraud Scenario Analysis: Identified common patterns of fraud rings, such as sharing legitimate contact information to create synthetic identities, opening multiple accounts, and bust-out schemes.

2. Graph Database Integration: Used graph databases to run entity link analysis queries and detect fraud rings. Conducted real-time graph traversals during key stages of the customer and account lifecycle (e.g., account creation, investigations, credit balance thresholds).

3. Graph Data Model: Created a graph data model to represent the data and identify connections. Languages like Cypher were used to detect rings by navigating connections in real-time.

4. Entity Link Analysis: Performed entity link analysis to uncover fraud rings, visualizing the data to identify potential fraud patterns and rings.

Enhanced detection of sophisticated fraud rings.

The bank significantly improved its ability to detect and prevent advanced fraud scenarios in real-time, reducing financial losses and enhancing the effectiveness of its fraud detection infrastructure.