Live Neo4j Demo: Using Graphs for Fraud Detection
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices, or IP addresses. However, today's sophisticated fraudsters escape detection by forming fraud rings using stolen and synthetic identities. To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them.
Join our 30-minute demo in which Neo4j experts showcase how enterprise organizations use Neo4j to augment their existing fraud detection capabilities, combating a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud, and money laundering - all in real time.
Our experts will guide you through these business outcomes:
* Detecting and stopping fraud - Catch fraud rings and prevent their incursions by augmenting discrete data scrutiny with data relationship analysis.
* Real-time detection - A graph database ensures that relationship-oriented queries are conducted in real time, so your anti-fraud team has a chance to strike first.
The Live Demo explores these key Neo4j advantages:
* Native graph storage - Neo4j stores interconnected data that is neither purely linear nor purely hierarchical, making it easier to detect rings of fraudulent activity regardless of the depth or the shape of the data.
* Flexible schema - Neo4j's versatile property graph model makes it easier for organizations to evolve fraud detection data models, helping security teams match the pace of ever-advancing fraudsters.
* Performance and scalability - The native graph processing engine supports high-performance graph queries on large datasets to enable real-time fraud detection.
* High availability - The built-in, high-availability features of Neo4j ensure your mission critical fraud detection applications are always available.