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 comprised of stolen and synthetic identities. To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them.
No fraud prevention measures are perfect, but by looking beyond individual data points to the connections that link them your efforts significantly improve. Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database.
Enterprise organizations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Fraud Detection: Discovering Connections with Graph Databases
Discover how financial services enterprises are using graph technology to prevent and detect financial fraud.Read Now
Fraud Detection with Neo4j
This webinar walks you through the creation and operation of an example fraud detection application powered by Neo4j.Watch the webinar
Womens Lace White Design Ladies On Boot Wellington Up Spot Baseball Boots Bank Fraud Detection Example
Browse a sample dataset, graph model and queries for a fraud detection solution at a hypothetical bank.Explore the GraphGist
Detecting and stopping fraud
Catch fraud rings and prevent their incursions by augmenting discrete data scrutiny with data relationship analysis. Whether automated or human-augmented, graph analysis makes your fraud analytics go further.
By the time a relational database calculates the complex relationships within a fraud ring, the criminals have already struck and have likely disappeared. A graph database ensures that relationship-oriented queries are conducted in real time, so your anti-fraud team has a chance to strike first.
Anti-money laundering (AML)
In addition to outright and direct fraud detection, graph databases are also a powerful weapon against the murky world of money laundering and embezzlement, whether from internal employees or from sophisticated fraudsters posing as wealthy clients.
Design Boots Up Baseball Womens White On Boot Lace Spot Wellington Ladies Complex data relationships
Uncovering fraud rings requires you to overcome the computational complexity associated with the traversal of data relationships – a problem that’s exacerbated as a fraud ring grows.
Real-time query performance
Whether you are building an automated fraud detection system that detects and prevents fraud as it occurs or you are providing an analytics tool to your analysts to help with manual fraud detection, real-time traversal of a complex and highly interconnected set is essential.
Evolving and dynamic targets
Fraud rings are continuously growing in shape and size, and your fraud detection application needs to accommodate this highly dynamic and emerging environment.
Native graph storage
Unlike relational databases, 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.
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
Neo4j’s native graph processing engine supports high-performance graph queries on large datasets to enable real-time fraud detection.
The built-in, high-availability features of Neo4j ensure your mission critical fraud detection applications are always available.