{"id":436,"date":"2020-10-21T22:47:10","date_gmt":"2020-10-21T22:47:10","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/10\/21\/performing-batch-fraud-predictions-using-amazon-fraud-detector-amazon-s3-and-aws-lambda\/"},"modified":"2020-10-21T22:47:10","modified_gmt":"2020-10-21T22:47:10","slug":"performing-batch-fraud-predictions-using-amazon-fraud-detector-amazon-s3-and-aws-lambda","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/10\/21\/performing-batch-fraud-predictions-using-amazon-fraud-detector-amazon-s3-and-aws-lambda\/","title":{"rendered":"Performing batch fraud predictions using Amazon Fraud Detector, Amazon S3, and AWS Lambda"},"content":{"rendered":"
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Amazon Fraud Detector<\/a> is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud. Amazon Fraud Detector combines your data, the latest in ML science, and more than 20 years of fraud detection experience from Amazon.com<\/a> and AWS to build ML models tailor-made to detect fraud in your business.<\/p>\n

This post walks you through how to use Amazon Fraud Detector with Amazon Simple Storage Service<\/a> (Amazon S3) and AWS Lambda<\/a> to perform a batch of fraud predictions on event records (such as account registrations and transactions) in a CSV file. This architecture enables you to trigger a batch of predictions automatically upon uploading your CSV file to Amazon S3 and retrieve the fraud prediction results in a newly generated CSV also stored in Amazon S3.<\/p>\n

Solution overview<\/h2>\n

Amazon Fraud Detector can perform low-latency fraud predictions, enabling your company to dynamically adjust the customer experience in your applications based on real-time fraud risk detection. But suppose you want to generate fraud predictions for a batch of events after the fact; perhaps you don\u2019t need a low-latency response and want to evaluate events on an hourly or daily schedule. How do you accomplish this using Amazon Fraud Detector? One approach is to use an Amazon S3 event notification to trigger a Lambda function that processes a CSV file of events stored in Amazon S3 when the file is uploaded to an input S3 bucket. The function runs each event through Amazon Fraud Detector to generate predictions using a detector (ML model and rules) and uploads the prediction results to an S3 output bucket. The following diagram illustrates this architecture.<\/p>\n

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To create this Lambda-based batch prediction system, you complete the following high-level steps:<\/p>\n

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  1. Create and publish a detector version containing a fraud detection model and rules, or simply a ruleset.<\/li>\n
  2. Create two S3 buckets. The first bucket is used to land your CSV file, and the second bucket is where your Lambda function writes the prediction results to.<\/li>\n
  3. Create an AWS Identity and Access Management<\/a> (IAM) role to use as the execution role in the Lambda function.<\/li>\n
  4. Create a Lambda function that reads in a CSV file from Amazon S3, calls the Amazon Fraud Detector get_event_prediction<\/code> function for each record in the CSV file, and writes a CSV file to Amazon S3.<\/li>\n
  5. Add an Amazon S3 event trigger to invoke your Lambda function whenever a new CSV file is uploaded to the S3 bucket.<\/li>\n
  6. Create a sample CSV file of event records to test the batch prediction process.<\/li>\n
  7. Test the end-to-end process by uploading your sample CSV file to your input S3 bucket and reviewing prediction results in the newly generated CSV file in your output S3 bucket.<\/li>\n<\/ol>\n

    Creating and publishing a detector<\/h2>\n

    You can create and publish a detector version using the Amazon Fraud Detector console or via the APIs. For console instructions, see Get started (console)<\/a> or Amazon Fraud Detector is now Generally Available<\/a>. After you complete this step, note the following items, which you need in later steps:<\/p>\n