December 06, 2017
Banks Need More Robust Analytics to Thrive—and Even Survive
According to a 2016 Juniper Research infographic, online transaction fraud is expected to hit $25.6 billion by 2020, meaning $4 of every $1,000 spent online will be fraudulent. The research firm also reports that retailers stand to lose $71 billion globally from fraudulent card-not-present transactions over the next five years.
David Excell, CTO and co-founder at Featurespace explores the future of machine learning in fraud detection at banks and payment processors with Payments and Cards Network. Read the full article here.
As settlement for Automated Clearing House (ACH) and wire payments accelerates, retail banks need to focus intently on catching fraud in real time—or risk losing business. One key tactic will be a greater reliance on machine learning and adaptive behavioural analytics. These technologies will not only help banks reduce real losses, but also cut the number of genuine transactions that get turned down—incidents known as “false positives.”
It’s not a question of whether banks can fight fraud, it’s how efficiently and cost effectively they can do it in a world of shrinking payment windows for ACH and wire transfers...
Read the full article in Payments and Cards Magazine.