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Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts. This repository contains a collection of example AWS solutions and Jupyter notebooks that interact with the Amazon Fraud Detector APIs. For more videos, blogs, and tutorials about Amazon Fraud Detector, refer to Amazon Fraud Detector resource.

• Fraud_Detector_End_to_End_External_Data_OFI.ipynb, provides an example of building a detector using Amazon Fraud Detector’s APIs for Online Fraud Inisights (OFI) model type using external data sets.

• Fraud_Detector_End_to_End_Stored_Data.ipynb, provides an example of building a detector using Amazon Fraud Detector’s APIs for Transaction Fraud Inisights (TFI) or Online Fraud Inisights (OFI) model type using data stored in Amazon Fraud Detector.

• Fraud_Detector_End_to_End_ATI.ipynb, provides an example of building a detector using Amazon Fraud Detector’s APIs for Account Takeover Inisights (ATI) model type using data stored in Amazon Fraud Detector. Sample dataset is available under data folder.

• Fraud_Detector_Send_Event.ipynb, provides an example of calling Amazon Fraud Detector's SendEvent API.

• Fraud_Detector_GetEventPrediction_API_example.ipynb, provides an example of calling Amazon Fraud Detector’s event prediction API.

• Fraud_Detector_BatchPrediction_API_Example.ipynb, provides an example of working with Amazon Fraud Detector's batch prediction API.

• AFD-Sagemaker-Model-Example.ipynb, provides an exmaple of training a sagemaker model, import to Amazon Fraud Detector and test.

The profiler generates an intuitive and comprehensive report of your dataset, including variable statistics, label distribution, categorical and numeric analysis, and even variable&label correlations. It provides guidance on variable types as well as an option to transform the dataset into the format in compliance with AFD. Refer to this blog post for more information.

To use it, follow steps below:

1. Open the CloudFormation quick launch link.
2. Fill in the parameters including: path to your CSV file in S3, some header names, and options.
3. Click create stack.
4. Wait a few minutes and open your S3 folder with your CSV file (e.g. myfile.csv). The profiling report is under folder /afd_data_myfile/report.html.

• data folder, provides sample data sets for OFI and TFI model types.
  • registration_data_20K_full.csv and registration_data_20K_minimum.csv, provide sample data sets for OFI.
  • transaction_data_100K_full.csv, provides sample data set for TFI.
  • ato_data_800K_full.csv.zip, provides sample data set for ATI. Download and unzip to get the CSV data.

This library is licensed under the MIT-0 License. See the LICENSE file.