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Machine Learning Supercharges Banks’ Efforts To Fight Debit Fraud

Consumers have more heavily leaned on debit during the pandemic, with the economic downturn making shoppers more cautious than ever about the prospect of taking on credit card debt. A recent study even estimates that shoppers could ultimately shift $100 billion worth of annual spending from credit cards to debit cards. Debit solutions draw on funds consumers already have in their bank accounts, and while this makes them reassuring to debt-wary consumers, it can have implications if these details are snatched by fraudsters. While fraud affects less than 1 percent of all card purchases, consumers who do lose funds from their bank accounts must go through lengthy and often stressful processes to get their money back.

The December Next-Gen Debit Tracker® examines how card issuers are working to sharpen their fraud-fighting tools and leverage innovative, machine learning (ML)-based strategies and technologies to keep shoppers safe.

Around The Next-Gen Debit World

Bad actors have ramped up their attacks against debit card holders in India, where such scams are reported to have risen 75 percent during the pandemic. Officials have struggled to stop or even detect these crimes, and fraudsters are making the task especially difficult by leveraging various schemes. One popular scam sees fraudsters pretending to be government officials and alleging that consumers need to hand over payments data to receive relief funds.


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