Amazon Fraud Detector features

Amazon Fraud Detector fully automates 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 takes care of all the heavy lifting such as data validation and enrichment, feature engineering, algorithm selection, hyperparameter tuning, and model deployment. You simply upload your dataset, select the model type, and Amazon Fraud Detector automatically finds the best-fitting fraud detection ML model. No coding or previous machine learning experience is required.

Your model maintains its performance longer between retrainings because Amazon Fraud Detector automatically calculates 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.

For each model you train, you can see all of the 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.

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.

You can use the Amazon Fraud Detector API 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.

Using the Amazon Fraud Detector console, you can easily search and review 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.

If you have already created a fraud detection model in Amazon SageMaker, you can integrate it with Amazon Fraud Detector to stop even more fraud. 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.