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AWS Solutions Library

Guidance for Near Real-Time Fraud Detection with Graph Neural Network on AWS

Overview

This Guidance demonstrates an end-to-end, near real-time anti-fraud system based on deep learning graph neural networks. This blueprint architecture uses Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network (GNN) model to detect fraudulent transactions.

How it works

Near Real-Time Fraud Detection

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram
This diagram illustrates an AWS architecture for near real-time fraud detection using a graph neural network. It shows the flow from users through Amazon API Gateway, AWS Lambda, Amazon Neptune, Amazon SageMaker, Amazon SQS, and Amazon DocumentDB, as well as the involvement of AWS AppSync, Amazon CloudFront, and Amazon S3.

Offline Model Training

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram
Architecture diagram illustrating a near real-time fraud detection workflow on AWS using graph neural networks (GNN) with offline model training. It showcases AWS Step Functions orchestrating services like AWS Lambda, AWS Glue, Amazon SageMaker, AWS Fargate, Amazon Neptune, Amazon S3, and Amazon CloudWatch.

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance uses AWS Serverless services like AWS Glue, SageMaker, AWS Fargate, Lambda as compute resources for processing data, training models, serving the API functionalities, and keeping billing to pay-as-you-go pricing. One of the data stores is designed using Amazon S3, providing a low total cost of ownership for storing and retrieving data. The business dashboard uses CloudFront, Amazon S3 and AWS AppSync, Lambda to implement the web application.

Read the Operational Excellence whitepaper

API Gateway and Lambda provide a protection layer when invoking Lambda functions through an outbound API. All the proposed services support integration with AWS Identity and Access Management (IAM), which can be used to control access to resources and data. All traffic in the VPC between services are controlled by security groups.

Read the Security whitepaper

API Gateway, Lambda, AWS Step Functions, AWS Glue, Amazon S3, Neptune, Amazon DocumentDB, and AWS AppSync provide high availability within a Region. Customers can deploy SageMaker endpoints in a highly available manner.

Read the Reliability whitepaper

All the services used in the design provide cloud watch metrics that can be used to monitor individual components of the design. MLOps pipelines orchestrated by Step Functions helps to continuously iterate the model. API Gateway and Lambda allow publishing of new versions through an automated pipeline.

Read the Performance Efficiency whitepaper

This Guidance requires GNN model training for fraud detection. The performance requirements for batch processing range from minutes to hours; AWS Glue and SageMaker training jobs are designed to meet them. Neptune is a purpose-built, high-performance graph database engine. Neptune efficiently stores and navigates graph data, and uses a scale-up, in-memory optimized architecture for fast query evaluation over large graphs. Provisioned concurrency in Lambda and the HTTP API in API Gateway can support a latency requirement of less than 10 ms.

Read the Cost Optimization whitepaper

This Guidance uses the scaling behaviors of Lambda and API Gateway to reduce over-provisioning resources. It uses AWS Managed Services to maximize resource utilization and to reduce the amount of energy needed to run a given workload.

Read the Sustainability whitepaper

Related content

Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

This blog post demonstrates how many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine learning (ML) models, to name a few.

Learn more

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.