9 Practical Actions to Improve Machine Learning for Fraud Prevention - insideBIGDATA

In this special guest feature, Arjun Kakkar, Vice President Strategy and Operations at Ekata, provides 9 practical and actionable principles for product managers and business leaders working to use machine learning for fraud detection. Arjun works with Ekata's operating teams to drive customer value across e-commerce, payments, marketplaces and online lending verticals. 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.