Using Graph Machine Learning to Improve Fraud Detection Rates

Fraud detection is crucial in various industries, and as the world becomes increasingly digital, businesses must adapt their strategies to combat fraudulent activities effectively. Graph machine learning techniques can improve fraud detection by up to 20% in the Ethereum blockchain. By utilizing graph data structures, relationships between entities can be represented more naturally, allowing for richer feature extraction and improved similarity determination. Incorporating graph features into traditional ML models or using native graph models like Graph Neural Networks (GNNs) can lead to significant accuracy improvements in fraud detection tasks. TigerGraph is a highly scalable and performant graph database that enables businesses to compute novel graph features, making it an ideal choice for implementing these techniques.