aws-solutions-library-samples/fraud-detection-using-machine-learning
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
This project helps businesses fight online fraud and abuse using machine learning. It takes historical transaction data, either labeled or unlabeled, and trains smart models to predict which incoming transactions are likely fraudulent. The output is a deployed system that integrates into your existing business software to provide real-time fraud predictions. This is for fraud prevention specialists, risk managers, and operations teams in online businesses.
333 stars. No commits in the last 6 months.
Use this if you want to move beyond static, rule-based systems to a dynamic, self-improving machine learning approach for detecting fraud in online transactions.
Not ideal if you do not have historical transaction data or prefer a simpler, purely rule-based fraud detection system.
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Oct 02, 2024
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