Using R to detect fraud at 1 million transactions per second

September 28, 2016

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

In Joseph Sirosh's keynote presentation at the Data Science Summit on Monday, Wee Hyong Took demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card transaction being fraudulent, based on attributes like the charge amount and the country of origin. Then, a stored procedure in SQL Server 2016 was used to score transactions streaming into the database at a rate of 3.6 billion per hour. 


Later in the keynote (starting at 25:00), John Salch, VP of Technology and Platforms at PROS describes using R to determine prices for airline tickets, hotel rooms, and laptops. PROS has been using R for a while in development, but found running R within SQL Server 2016 to be 100 times (not 100%, 100x!) faster for price optimization. "This really woke us up that we can use R in a production setting … it's truly amazing," he says.

It's great to see these global-scale applications of R, driving the intelligence of businesses behind the scenes. As Joseph said in the opening, "If there's one language you should learn today … it's R." 

Channel 9: Microsoft Machine Learning & Data Science Summit 2016

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