End-to-End AWS Real-time Fraud Detection
Fraud detection system using machine learning models deployed on AWS for real-time transaction monitoring.
End-to-End Real-time Fraud Detection on AWS
Detecting potential fraud in financial systems is a major challenge for organizations worldwide. Building robust solutions that enable real-time actions is essential for companies aiming to provide greater security to their customers during financial transactions.
This project demonstrates a complete machine learning pipeline for credit card fraud detection using the Kaggle Credit Card Fraud Detection dataset, which contains 284,807 European cardholder transactions from 2013 (including 492 fraudulent cases) with 28 PCA-transformed features plus original Amount and Time variables.
The project showcases a production-ready streaming architecture that integrates Amazon SageMaker for training both supervised and unsupervised ML models and deploying them as managed endpoints. The complete AWS solution includes:
- Training of supervised and unsupervised ML models and deployment to a managed-endpoint using Amazon SageMaker
- REST API deployment via Chalice (Lambda + API Gateway)
- Streaming data pipeline (Kinesis → Spark/Glue → RDS)
- (Optional) Interactive dashboard for real-time fraud monitoring and analysis.
Architecture overview:
Want to Contribute?
This project is open source. Contributions, issues, and feature requests are welcome!