The Battle For Our Bank Accounts – How Machine Learning and Continuous Monitoring Can Prevent Fraud Attacks
July 6, 2020
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The ultimate prize for cybercriminals is to obtain access to other people’s money – so it’s no wonder that account takeover attacks are on the rise. In this article, originally published by Fraud Intelligence, Greg Hancell, Manager of Global Fraud Consulting at OneSpan, explains how banks can apply continuous monitoring and machine learning to defend against account takeover attacks.
The battle for our bank accounts – continuous monitoring
Account takeover fraud (ATO) is one of the top threats to financial institutions and their customers. In an industry survey by the Aite Group, 89 per cent of financial institution executives pointed to account takeover fraud as the most common cause of losses in the digital channel. Today, cybercriminals remain focused on ATO, new account fraud, and card-not-present fraud. The 2020 Identity Fraud report by Javelin Strategy & Research found account takeovers trending at the highest loss rate to date, up a staggering 72 per cent on 2019 , to $5.1 billion, and a 120 per cent increase on 2016.  As fraudsters get more aggressive, they continue to leverage phishing, spear phishing and identity theft to perpetrate further new account fraud. In fact, 1.5 million victims of existing account fraud had an intermediary account opened in their name – a 200 per cent increase on the previous year.
Our digital identities are no longer private. In 2018, roughly 3.2 billion personal data records were compromised ; that’s nearly half of the world’s population. Today’s data breaches are being published online in dark web marketplaces, where there’s a lot of profit being made.