Amazon Fraud Detector Documentation

Amazon Fraud Detector is a fully managed service that enables customers to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts. Amazon Fraud Detector uses machine learning (ML) and 20 years of fraud detection expertise from AWS and Amazon.com to identify potentially fraudulent activity. With Amazon Fraud Detector, you can create a fraud detection model with just a few clicks and no prior ML experience because Amazon Fraud Detector handles the ML heavy lifting for you.

Automated model creation

Amazon Fraud Detector can automate the creation of machine learning models that identify potential fraud for common online activities such as new account creations, online payments, and guest checkouts. The automated model-building process is designed to perform various tasks such as data validation and enrichment, feature engineering, algorithm selection, hyperparameter tuning, and model deployment.  

Models that continuously learn

Your model is designed to maintain its performance longer between trainings because Amazon Fraud Detector is designed to automatically calculate information like account age, time since last activity, and counts of activities. This means that your model can learn the difference between trusted customers who frequently make transactions and fraudsters’ continued attempts.

Insights into your model performance

For models you train, you can see inputs you provided ranked by their impact on model performance. Using the importance values and relative ranking, you can gain insight into what inputs are driving your model performance.

Trigger rule-based actions

Once you create an Amazon Fraud Detector fraud detection model, you can use the Amazon Fraud Detector console or application programming interface (API) to create rules based on model predictions. Customers can create rules to take actions such as accept, review, or collect more information for specific model scores. For example, you can easily create a rule to flag suspicious customer accounts for review if the model score is greater than your predetermined threshold and the account’s phone number country and IP address country do not match.

Real-time fraud prediction API

The Amazon Fraud Detector API is designed to perform real-time fraud predictions and evaluate online activities in your application as they occur. For example, you can call the fraud predictions API to check every new account sign-up for potential fraud risk, using your model and rules to trigger an action.

A single interface to review and audit your predictions and detection logic

The Amazon Fraud Detector console is designed to allow easy search and review of your past fraud evaluations to audit detection logic. View event data, detection logic applied during the evaluation, and the conditions that resulted in a fraud prediction outcome.

Amazon SageMaker integration

Amazon Fraud Detection is designed to be integrated with Amazon SageMaker. You can use both your Amazon SageMaker and Amazon Fraud Detector models in your application to detect different types of fraud. For example, your application can use the Amazon Fraud Detector model to assess the fraud risk of customer accounts, and simultaneously use your Amazon SageMaker model to check for account compromise risk.

Additional Information

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.aws.amazon.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.amazon.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.