Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and fake account creation. Amazon Fraud Detector uses machine learning (ML) and 20 years of fraud detection expertise from Amazon Web Services (AWS) and Amazon.com to automatically identify potential fraudulent activity in milliseconds. There are no upfront payments or long-term commitments, and no infrastructure to manage with Amazon Fraud Detector; you pay only for your actual usage.
First, you define the event you want to assess for fraud. Next, you upload your historical event dataset to Amazon Simple Storage Service (Amazon S3) and select a fraud detection model type, which specifies a combination of features and algorithms optimized to detect a specific form of fraud. The service then automatically trains, tests, and deploys a customized fraud detection model based on your unique information. During this process, you can boost your model performance with a series of models pre-trained on fraud patterns based on AWS and Amazon’s own fraud expertise. The model’s output is a score ranging from 0 to 1,000 that predicts the likelihood of fraud risk. At the final stage of the process, you set up decision logic (e.g. rules) to interpret your model’s score and assign outcomes such as passing or sending transactions to a human investigator for review.
After this framework is created, you can integrate the Amazon Fraud Detector API into you website’s transactional functions, such as account sign-up or order checkout. Amazon Fraud Detector will process these activities in real time and provide fraud predictions in milliseconds to help you adjust your end-user experience.
Amazon Fraud Detector is designed for online fraud use cases requiring real-time ML modeling and rules-based evaluation. For example:
Yes. You can customize Amazon Fraud Detector for each use case, using a mix of Amazon Fraud Detector ML models, Amazon SageMaker models, and rules. First, gather the relevant risk data to use as fraud evaluation inputs. These include email addresses, phone numbers, and IP addresses. This data feeds into an ML model, which outputs a score. Finally, you can detection rules to interpret the score and other risk data to make decisions, such as approving a claim or sending orders to fraud analysts for investigation. An example of a simple rule and corresponding outcome could be: “IF model_score < 50 & credit_card_country = US THEN approve_order.”
With 20 years of fraud experience, Amazon has seen firsthand how bad actors conduct various forms of online fraud. Amazon Fraud Detector helps you tap into this knowledge. During the automated model training process, Amazon Fraud Detector uses a series of models trained on patterns from AWS and Amazon’s own fraud expertise to boost your model’s performance.
Amazon Fraud Detector automatically trains, tests, and deploys custom fraud detection machine learning models based on your historical fraud data, with no ML experience required. For developers with more machine learning experience, you can add your own models to Amazon Fraud Detector using Amazon SageMaker.
Amazon Fraud Detector makes it possible to perform rule-based fraud predictions with or without ML. With Amazon Fraud Detector, you can author detection rules (e.g. “IF model_score < 50 & credit_card_country = US THEN approve_order”) using a simple rule-writing language. You can also specify the order in which rules trigger during an evaluation using an intuitive interface.
Yes, you can review your past fraud evaluations to audit decision logic using the Amazon Fraud Detector console. In the Amazon Fraud Detector console, you can search for past events based on characteristics of the event and/or the detection logic applied, such as the outcome, models or rules used, or event metadata. You can then drill down into how the detection logic assessed an event.
No. Security and privacy are our top concerns. As a fundamental tenet of earning customer trust, AWS will never share customer data.