Amazon Web Services Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced the general availability of Amazon Fraud Detector, 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. To get started with Amazon Fraud Detector, visit http://aws.amazon.com/fraud-detector
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 the world’s largest services platform for entrepreneurs around the globe and is on a mission to empower their worldwide community of 19+ million customers by giving them all the help and tools they need to grow online. “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.”
Truevo makes simple, intuitive and user-friendly payment products that allow their clients to receive payments effortlessly, so they can focus on growing their businesses. “Amazon Fraud Detector has enabled us to drastically improve operations, increase our flexibility to respond to bad actors, and have greater control of systems and processes. Initially, we were exploring an in-house and third-party solution. When Amazon Fraud Detector was announced, we immediately changed course. We have been an AWS customer for many years and have great trust in Amazon’s products,” said Charles Grech, COO at Truevo. “With Amazon Fraud Detector, we are no longer bound by the conventional limitations of on-premises or SaaS offerings. Instead, we have the flexibility to adapt a machine learning-powered service to meet our needs and the ability to use AWS’s rules-only option while easily scaling to full machine learning capabilities when needed. This saved Truevo 3-6 months in development! In fact, we deployed our first prototype model within 30 minutes. Overall, we are operating with greater confidence in our ability to detect fraud in real-time. We are better equipped to deploy rule detections when we notice odd activity that we may not fully understand, but need to stop. We are able to respond and adapt to ever-changing regulatory and scheme requirements allowing us to stay on top of our game.”
ActiveCampaign provides category defining Customer Experience Automation software to 100k small and growing businesses around the globe. “In Q1/Q2 2020 we experienced a spike in accounts being used for phishing attacks. As a result, we needed to supplement our existing homegrown solution with stronger transaction data and signals to identify bad actors sooner. A scalable solution based on predictive machine learning was important to us as a growing business ourselves,” said Alex Burch, Senior Email Operations Engineer at ActiveCampaign. “Amazon Fraud Detector made it easy to build a model using our own data that accurately identifies account signups that result in phishing attacks. More importantly, we were able to get these results with a very low false positive rate, which means no additional work for our operations staff. Amazon Fraud Detector has a competitive pricing model, and we can easily integrate the model into our existing workflow.”