Fraud Detection in Real-Time with Neo4J

Fraud Detection in Real-Time

Banks and insurance companies lose billions of dollars each year to fraud, and investigating fraud often involves identifying suspicious patterns among mountains of uninteresting transactional data. Increasingly sophisticated fraudsters have developed a variety of ways to elude discovery, both by working together, and by leveraging various other means of constructing false identities.

Graph databases such as Neo4j offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time. This provides an enhanced degree of insight, compared to fraud detection algorithms that use basic statistical analysis and pattern recognition. Neo4j is allowing users to develop the next generation of fraud detection systems based on connected intelligence.

Speaker: David Montag

David Montag works with ensuring the success of Neo4j Enterprise customers. Working closely with customers such as Cisco, Tre, TechCrunch, and Pitney Bowes, he has first-hand experience working with large scale companies putting Neo4j into production. David is responsible for the Field Engineering organization in Europe at Neo Technology, commercial backer of Neo4j – the most widely deployed graph database in the world.