As banks go digital they face new levels of fraud, or fraud threats that have to be checked at very high speed. Often the major cost is turning away good business because fraud detection is too restrictive (see my story on MacSales which would have missed $2 million in good sales before implementing Forter for fraud detection) or wasting time, and annoying customers, with investigations of legitimate transactions. Denmark’s Danske Bank was picking up 1,200 false positives per day in its transaction monitoring, and 99.5 percent were false positives, said Nadeem Gulzar, head of global analytics at the bank. With machine learning they were able to reduce false positives by 35 percent and improve detection of true positives — actual fraud, at roughly the same percent. “From the beginning we wanted a data driven approach with machine learning and deep learning," Gulzar said.

Source: Forbes October 30, 2017 20:15 UTC