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Fraud Detection and Prevention

  • Anomaly Detection: Identify unusual patterns in transaction data that may indicate fraudulent activity.
  • Real-time Fraud Detection: Use real-time analytics to detect and prevent fraudulent transactions as they occur.
  • Behavioral Biometrics: Analyze user behavior (e.g., face detection, liveness in KYC) to detect potential fraud.

Amazon Fraud Detector​

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.

Amazon Fraud Detector

  1. Step 1 - Explore data models for your business use case.
  2. Step 2 - Define the event you want to evaluate for fraud.
  3. Step 3 - Upload your historical event dataset to Amazon S3 or stream and store your event data directly in AFD.
  4. Step 4 - Select a model type and train your model. The service automatically inspects and enriches data, performs feature engineering, selects algorithms, trains and tunes your model, and hosts the model.
  5. Step 5 - Create rules to either accept, review, or collect more information based on model predictions.
  6. Step 6 - Call the Amazon Fraud Detector API from your online application to receive real-time fraud predictions and take action based on your configured detection rules.

Models​

  • Transaction Fraud Insights
  • Online Fraud Insights
  • Account Takeover Insights

Model metrics​

Model performance charts

ROC Curve

Model variable importance

Fraud Detection using Amazon Sagemaker​