online gambling singapore online gambling singapore online slot malaysia online slot malaysia mega888 malaysia slot gacor live casino malaysia online betting malaysia mega888 mega888 mega888 mega888 mega888 mega888 mega888 mega888 mega888 9 Practical Actions to Improve Machine Learning for Fraud Prevention

 

 

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.

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