How U.S. Bank Is Using Machine Learning To Stop Account Opening Fraud

Banks lost about $4 billion to account takeover (ATO) fraud attempts last year and the losses are set to further increase as this year progresses. ATO and other fraud methods that rely on using stolen credit card numbers or other personal information are becoming decidedly popular with bad actors, who are employing increasingly sophisticated technologies to execute their scams. The majority of ATO attacks are automated, meaning that fraudsters are becoming more comfortable utilizing advanced learning tools such as deep learning, artificial intelligence (AI) and machine learning (ML) to target financial institutions (FIs) and their customers. About 40 percent of all ATO attacks now count as high-risk, meaning banks of all shapes and sizes must reexamine how they think about data protection, security and the tools they use to guard against emerging threats. In the latest Digital Banking Tracker, PYMNTS analyzes the ways in which FIs are working to keep fraudsters from accessing customer information and funds on their mobile and digital channels.