As a result, we can give our customers more detailed fraud data.” With the ability to correctly detect fraud, Anderson says saves its customers about $1 million a week by helping them detect and prevent fraud.Īdditionally, is using AWS to maintain its fast application response times of under 200 milliseconds. “Being able to ask more questions gives us better answers. “We can add more models and ask more questions using Amazon Machine Learning,” says Clark. Now, can continuously identify new fraud strategies and help its customers detect fraud more accurately. We can see correlations we wouldn’t have been able to see otherwise, and we can get answers to questions it would have taken us way too long to answer ourselves.” “We are able to build and retrain models in almost half the time it took on other platforms we tested.”Īdds Clark, “Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns. “Using Amazon Machine Learning, we've quickly created and trained a number of specific, targeted models, rather than building a single algorithm to try and capture all the different forms of fraud,” says Anderson. Using Amazon Machine Learning, can easily launch and train new machine-learning models to target today’s evolving forms of fraud. And the choice was easier for us because we were already on the Amazon stack.” Amazon keeps the effort and resources required to build a model to a minimum. “The amount of pain involved in building a machine-learning model on some of these other platforms was substantial. “We considered five other platforms, but Amazon Machine Learning was the best solution,” says Anderson. Amazon Machine Learning also enables the use of simple APIs to get predictions for applications without having to deploy prediction generation code. Most recently, started using Amazon Machine Learning, a service that provides tools to easily guide developers through the process of building machine-learning models. “We can provision servers much faster than we could using a traditional hardware platform.” also collects online fraud data in Amazon Simple Storage Service (S3) and moves it to Amazon Redshift for analysis, which Clarks says is a "great structured feeder of data to our machine-learning models.” “The scalability in both Amazon DynamoDB and AWS Lambda is phenomenal,” says Clark. The organization also uses AWS Lambda to run code without the need to provision or manage servers. “The AWS cloud offered the most flexibility and reliability, as well as cost savings.” uses Amazon DynamoDB, a NoSQL database service, to host. “Even before I was with, I had used AWS and it always worked very well,” says Oliver Clark, CTO at. To address its scalability needs, chose to use Amazon Web Services (AWS) to host its customer platform. “We grew tenfold in the last year, and we plan on another tenfold growth this year.” ![]() “Scalability is at the core of everything we do,” Anderson says. also needed to find a scalable solution to help it keep pace with fast business growth. “As a young company, we have to have the ability to ramp things up very quickly, without spending a lot of money on maintaining our own servers,” Anderson says. However, the organization didn’t want to invest time and resources in creating a back-end platform to support its new machine-learning models. ![]() “As new fraud schemes pop up, we have to identify and create models around those specialized situations.” “On any given day, we might see 100 different fraud schemes, each one with 100 different variations,” says Anderson. “Once you start catching a form of fraud, the fraudsters themselves will change their strategy-so it’s a constantly evolving problem,” says Whitney Anderson, CEO of .īecause fraud is so fluid, also wanted to build and retrain models more quickly. In order to counter the increasingly different and evolving forms of fraud, needed to build and train a larger number of more targeted and more precise machine-learning models. e-commerce, and its client base and data requirements are growing at a pace of more than 1,000 percent per year. The platform protects more than 2 percent of all U.S. merchants an estimated $20 billion annually. A collaborative program, is currently the largest merchant-led effort to combat online payment fraud, which costs U.S. We can see correlations we wouldn’t have been able to see otherwise and answer questions it would have taken us way too long to answer ourselves.į is the world’s leading crowdsourced fraud prevention platform, aggregating and analyzing large amounts of fraud data from thousands of online merchants in real time. Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns.
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