Stop Fraud Rings in Their Tracks with Neo4j

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.

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Fast Track

  • Money Laundering Prevention with Neo4j

    Discover how KERBEROS, using a Neo4j graph database, developed a compliance management system to help companies react quickly to suspected money laundering.

    Read the Case Study
  • Financial Fraud Detection with Graph Data Science

    Learn how to enhance your financial fraud detection patterns with machine learning, data visualization and graph data science.

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  • 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
  • Why Intelligent Applications Need a Graph Database with Granular Security

    Download this visual overview to learn how Neo4j’s granular database security features guard your sensitive enterprise data.

    Read the Visual Overview

Business Outcomes

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.

Real-time detection

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.

Challenges

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.

Why Neo4j?

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.

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

Neo4j’s 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.

White Paper: Financial Fraud Detection with Graph Data Science

White Paper: Financial Fraud Detection with Graph Data Science

How Graph Algorithms & Visualization Better Predict Emerging Fraud Patterns

Discover how graph data science augments your existing fraud analytics and machine learning pipelines to reduce fraudulent transactions and safeguard revenue streams.

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Neo4j Fraud Detection Ring - Infographic

Stop Fraud Rings in their Tracks with Graph Databases

Discover how graph databases help organizations detect – and prevent – fraud ring behaviors in real time.

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Ready to get started?

Your enterprise is driven by connections – now it's time for your database to do the same. Click below to download and dive into Neo4j for yourself – or download the white paper to learn how graph databases discover connections in today's most cutting-edge fraud detection solutions.

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