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.
Businesses looking for a fully-managed AWS AI service for fraud detection can also use Amazon Fraud Detector, which you can use to identify suspicious online payments, detect new account fraud, prevent trial and loyalty program abuse, or improve account takeover detection.
Solution overview
The following diagram describes an exemplar financial transaction network that includes different types of information. Each transaction contains information like device

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