Accelerate Fraud Detection with Graph Databases

Fraud grows more sophisticated and costly each year, challenging companies to stay ahead of fraud detection techniques. Many rely on reactive measures, risking false positives and missed alerts. While relational databases and machine learning offer some insight, they fall short in understanding intricate behaviors and patterns across customers, devices, and companies.

Neo4j is the solution to these fraud-detection shortcomings. It is the only graph database that helps developers ground AI with knowledge graphs, using six common design patterns to complement their current fraud-detection solutions and improve results.

This ebook explores how Neo4j Graph Database can help developers and data scientists:

Build robust, scalable fraud detection solutions—or fortify existing ones
Quickly identify and investigate suspicious activity using graph design patterns
Boost detection rates to thwart complex fraud schemes
Seamlessly integrate machine learning with graph for enriched models
Use Amazon Web Services to create innovative new solutions for fraud detection

Download this ebook to learn how to build more accurate, sophisticated fraud detection applications with graph databases.

Summary