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Use Graph Machine Learning to detect fraud with Amazon Neptune Analytics and GraphStorm
- Businesses and consumers face significant losses to fraud, with reports of $12.5 billion lost to fraud in 2024, showing a 25% increase year over year.
- Fraud networks operate coordinated schemes that are challenging for companies to detect and stop.
- Amazon Neptune Analytics and GraphStorm are utilized to develop a fraud analysis pipeline with AWS services.
- Graph machine learning offers advantages in capturing complex relationships crucial for fraud detection.
- GraphStorm enables the use of Graph Neural Networks (GNNs) for learning from large-scale graphs.
- Steps involve exporting data from Neptune Analytics, training graph ML models on SageMaker AI, and enriching graph data back into Neptune Analytics.
- Prerequisites include an AWS account, S3 bucket, required IAM roles, SageMaker execution role, and Amazon SageMaker Studio domain.
- The article provides detailed steps for setting up environment, creating a Neptune Analytics graph, training models with GraphStorm, and conducting fraud analysis.
- The workflow includes data preparation, training GraphStorm models, deploying SageMaker pipelines, enriching graphs, and analyzing high-risk transactions.
- Advanced analytics include detecting community structures, ranking communities by risk scores, and using node embeddings to find similar high-risk transactions.
- The post encourages further integrations with Neptune Database for online transactional graph queries and highlights workflow extensions.
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