Accelerate Fraud Detection with Graph Databases

Summary

Fraudsters are leveraging Generative AI’s powerful self-learning capabilities, leading to significant increases in fraud. The Deloitte Center for Financial Services predicts fraud losses could reach $40B in a single year by 2027 in FSI alone. The US Treasury warns that cyber threat actors using AI tools may initially outpace their targets.

Neo4j offers a flexible, native graph database and algorithms to quickly uncover and investigate complex fraud, enhancing your existing solutions and maximizing your AWS investments.

Key Benefits:

• Enhance fraud detection with graph technology
• Improve outcomes using three key graph design patterns
• Integrate seamlessly with existing AWS solutions

Graph databases offer flexibility that traditional relational databases can’t match, making data exploration and experimentation intuitive. Neo4j’s Cypher query language reduces coding and uncovers complex fraud patterns quickly.

Graph Design Patterns:

• Pattern Matching: Identify fraudulent behavior by specifying and querying patterns in the graph.
• Pathfinding: Reveal hidden bad actors and indirect relationships in large datasets.
• Machine Learning: Discover valuable relationships for feature engineering in fraud detection models.

By using these graph design patterns, you can strengthen your fraud detection solutions, reduce false positives, and stay ahead of evolving threats.

Download our ebook to learn more now.