Arjun works with Ekata’s operating teams to drive customer value across e-commerce, payments, marketplaces and online lending verticals. Before Ekata, Arjun was a Principal with Booz & Company. He has a B.Tech. from IIT Bombay and an MBA from The Wharton School.

The total recorded cost of global online fraud is about $25 billion [1]. But the real value is at least 20 times higher, because, to catch fraud, online merchants and banks often mistakenly reject legitimate customers. This blunder represents at least $500 billion in lost lifetime revenue for online commerce, not to mention a priceless amount of customer trust.

The unique characteristics of online fraud detection, including the availability of large and diverse data sets with known outcomes, repeating patterns, and a need for quick decisions, make it a good candidate for Machine Learning (ML). In fact, of the many problems that ML promises to solve, online fraud detection has been one of the earliest success stories.


見全文: insidebigdata

若喜歡本文,請關注我們的臉書 Please Like our Facebook Page: Big Data In Finance




  • 找不到回應