Amazon Puts AI Powered Fraud Alert System On General Release

Amazon Web Services Inc. (AWS), an Amazon.com company, recently 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.

The system could well prove useful for insurers and brokers alike, who are looking for duplicate card details being inputted online for insurance quotes, and especially claims settlements.

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.

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.”

GO DADDY SIGNS UP

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.”

 

About alastair walker 8997 Articles
20 years experience as a journalist and magazine editor. I'm your contact for press releases, events, news and commercial opportunities at Insurance-Edge.Net

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