Amazon Fraud Detector: Use machine learning in the fight against online fraud
Amazon Fraud Detector is a fully managed service that makes it easy to quickly identify potentially fraudulent online activities like online payment and identity fraud.
Using machine learning under the hood and based on over 20 years of fraud detection expertise from Amazon, Amazon Fraud Detector automatically identifies potentially fraudulent activity in milliseconds—with no machine learning expertise required. With just a few clicks in the Amazon Fraud Detector console, customers can select a pre-built machine learning model template, upload historical event data, and create decision logic to assign outcomes to the predictions (e.g. initiate a fraud investigation when the machine learning model predicts potentially fraudulent activity).
There are no up-front payments, long-term commitments, or infrastructure to manage with Amazon Fraud Detector, and customers pay only for their actual usage of the service.
Today, tens of billions of dollars are lost to online fraud every year by organizations around the world. As a result, many businesses invest in large, expensive fraud management systems. These systems are often based on hand-coded rules that are time-consuming to set up, expensive to customize, and difficult to keep up-to-date as fraud patterns change—all of which leads to lower accuracy. This leads organizations to reject good customers as fraudsters, conduct more costly fraud reviews, and miss opportunities to drive down fraud rates.
Amazon has made significant investments over the past 20 years to combat fraudulent activity using sophisticated machine learning techniques that minimize customer friction while staying one step ahead of bad actors, and customers have asked Amazon to share this expertise and experience to help them combat online fraud.
Amazon Fraud Detector provides a fully managed service that uses machine learning for detecting potential fraud in real time (e.g. online payment and identity fraud, the creation of fake accounts, loyalty account and promotion code abuse, etc.), based on the same technology used by Amazon.com—with no machine learning experience required. With Amazon Fraud Detector, customers use their historical data of both fraudulent and legitimate transactions to build, train, and deploy machine learning models that provide real-time, low-latency fraud risk predictions.
To get started, customers upload historical event data (e.g. transactions, account registrations, loyalty points redemptions, etc.) to Amazon Simple Storage Service (Amazon S3), where it is encrypted in transit and at rest and used to customize the model’s training. Customers only need to provide any two attributes associated with an event (e.g. logins, new account creation, etc.) and can optionally add other data (e.g. billing address or phone number).
Based upon the type of fraud customers want to predict, Amazon Fraud Detector will pre-process the data, select an algorithm, and train a model. Amazon Fraud Detector uses machine learning models based on Amazon’s 20+ years of experience with fraud to help identify patterns commonly associated with fraudulent activity. This improves the accuracy of the trained model even if the number of fraudulent examples provided by a customer to Amazon Fraud Detector is low.
Amazon Fraud Detector trains and deploys a model to a fully managed, private Application Programming Interface (API) end point. Customers can send new activity (e.g. signups or new purchases) to the API and receive a fraud risk response, which includes a precise fraud risk score. Based on the report, a customer’s application can determine the right course of action (e.g. accept a purchase, or pass it to a human for review). With Amazon Fraud Detector, customers can detect fraud more quickly, easily, and accurately with machine learning while also preventing fraud from happening in the first place.
“Customers of all sizes and across all industries have told us they spend a lot of time and effort trying to decrease the amount of fraud occurring on their websites and applications,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning, Amazon Web Services Inc. “By leveraging 20 years of experience detecting fraud coupled with powerful machine learning technology, we’re excited to bring customers Amazon Fraud Detector so they can automatically detect potential fraud, save time and money, and improve customer experiences—with no machine learning experience required.”
Developers with machine learning experience who want to extend what Amazon Fraud Detector delivers can customize Amazon Fraud Detector using a combination of machine learning models built with Amazon Fraud Detector and those built with Amazon SageMaker (a fully managed service for building, training, and deploying machine learning models quickly). Amazon Fraud Detector is available today in US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), Asia Pacific (Singapore), and Asia Pacific (Sydney), with availability in additional regions in the coming months.
“GoDaddy is committed to preventing fraudulent accounts, and we’re continually bolstering our capabilities to automatically detect such accounts during sign-up,” said John Kercheval, Senior Director, Identity Services Group at GoDaddy. “We recently began using Amazon Fraud Detector, and we’re pleased that it offers low cost of implementation and a self-service approach to building a machine learning model that is customized to our business. The model can be easily deployed and used in our new account process without impacting the signup experience for legitimate customers. The model we built with Amazon Fraud Detector is able to detect likely fraudulent sign-ups immediately, so we’re very pleased with the results and look forward to accomplishing more.”