Fraud Detection: Discovering Connections With Graph Databases

Fraud detection is an ongoing challenge for industries such as banking, insurance, and e-commerce, where fraudsters increasingly employ sophisticated tactics, including using multiple identities to orchestrate fraud rings. Traditional fraud detection methods, which focus on discrete data points, often fail to uncover these hidden connections. This white paper from Neo4j demonstrates how graph databases, which specialize in understanding relationships and connections between data points, offer a much more effective approach to detecting and preventing fraud. Graph databases excel at identifying complex fraud rings by linking shared identifiers like phone numbers and addresses, revealing patterns that would otherwise go unnoticed. The paper explores three major types of fraud—first-party bank fraud, insurance fraud, and e-commerce fraud—showing how graph analysis can be applied to detect collusion and fraud at early stages. By using graph databases, organizations can improve their fraud detection systems, reduce false positives, and uncover criminal activity with high accuracy.

Learn how graph databases can revolutionize your fraud detection strategy—download the full white paper now.