Detecting fraud with Amazon Fraud Detector

This section describes a typical workflow for detecting fraud with Amazon Fraud Detector. It also summarizes how you can accomplish those tasks. The following diagram provides a high-level view of the workflow for detecting fraud with Amazon Fraud Detector.

Fraud detection is a continuous process. After you deploy your model, make sure to evaluate its performance scores and metrics based on the prediction explanations. By doing so, you can identify top risk indicators, narrow down root causes that lead to false positives, and analyze fraud patterns across your dataset and detect bias, if any exist. To increase the accuracy of the predictions, you can tweak your dataset to include new or revised data. Then, you can retrain your model with the updated dataset. As more data becomes available, you continue retraining your model to increase accuracy.