Credit card fraud is a problem that affects banks, merchants, credit card providers and individual consumers all at once, as fraudsters deploy a wide variety of different tactics to pay for goods and services or drain customer accounts of funds and data. Card providers have developed various defenses against card fraud, including multifactor authentication, biometrics and behavioral analytics. Still, many of these methods have proven ineffective or will quickly become so when fraudsters find loopholes or workarounds.
One promising step was the development of 3D Secure, an extra authentication layer to ensure card users were who they said they were, but this protocol faced criticism due to its requirement for additional customer authentication that caused friction and cart abandonment. Fixing these flaws is the primary goal of 3D Secure 2.0 (3DS2), which leverages authentication data, artificial intelligence (AI) and machine learning (ML) to review transactions made by payment cards. This additional layer is primarily seamless to customers but can reduce credit card fraud by up to 40% while approving as much as 95% of transactions instantly without any needed inputs from the consumer.
In the March edition of the Digital Fraud Tracker®, PYMNTS explores the latest in the world of fraud prevention, including the types of credit card frauds merchants face and how 3D Secure resulted in increased customer friction when trying to prevent fraud. It also explores how the…
