How Amazon Fraud Detector works - Amazon Fraud Detector

How Amazon Fraud Detector works

To generate fraud predictions, Amazon Fraud Detector uses machine learning models that are trained with historical fraud data that you provide. Each model is trained using a model type. A model type is a specialized recipe that’s used to build a fraud detection model for a specific fraud use case. Deployed models are imported to detectors. There you can configure decision logic (for example, rules) to interpret the model’s score and assign outcomes. Outcomes can be approve transaction, review transaction, or send transaction for further investigation.

Amazon Fraud Detector components include event dataset, models, detectors, rules, and outcomes. Using these components, you can build an evaluation that contains your fraud detection logic.

For information about your workflow for detecting fraud using Amazon Fraud Detector, see Detecting fraud with Amazon Fraud Detector