Tag: fraud detection
As more businesses increase their online presence to serve their customers better, new fraud patterns are constantly emerging. In today’s ever-evolving digital landscape, where fraudsters are becoming more sophisticated in their tactics, detecting and preventing such fraudulent activities has become paramount for companies and financial institutions. Traditional rule-based fraud detection systems are capped in their […]
Build a GNN-based real-time fraud detection solution using the Deep Graph Library without using external graph storage
Fraud detection is an important problem that has applications in financial services, social media, ecommerce, gaming, and other industries. This post presents an implementation of a fraud detection solution using the Relational Graph Convolutional Network (RGCN) model to predict the probability that a transaction is fraudulent through both the transductive and inductive inference modes. You can deploy our implementation to an Amazon SageMaker endpoint as a real-time fraud detection solution, without requiring external graph storage or orchestration, thereby significantly reducing the deployment cost of the model.