Using Amazon Machine Learning, Fraud.net can easily launch and train new machine-learning models to target today’s evolving forms of fraud. “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. “We are able to build and retrain models in almost half the time it took on other platforms we tested.”

Adds Clark, “Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns. 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.”

Now, Fraud.net can continuously identify new fraud strategies and help its customers detect fraud more accurately. “We can add more models and ask more questions using Amazon Machine Learning,” says Clark. “Being able to ask more questions gives us better answers. As a result, we can give our customers more detailed fraud data.” With the ability to correctly detect fraud, Anderson says Fraud.net saves its customers about $1 million a week by helping them detect and prevent fraud.

Additionally, Fraud.net is using AWS to maintain its fast application response times of under 200 milliseconds. “Application response time is very important to our clients,” says Anderson. “Our fraud data analysis has to be very fast, and we can provide that with all the AWS services we have in place.”

Fraud.net has leveraged AWS to cost-effectively build and maintain its machine-learning solution. “Our machine-learning platform wouldn’t have been viable without AWS,” says Clark. “We would have needed an army of servers and people to manage it all otherwise.” Fraud.net also has the scalability it requires. “We have 20 machine-learning models up and being tested, and I could see us having several hundred models in the near future,” says Clark. “On any other platform, we would need teams of data scientists to run and optimize that many models. We will be able to meet our future growth needs using Amazon Machine Learning.”