International

AI Powers Mastercard’s New Authentication Process

Mastercard

Have you ever had a legitimate credit card transaction declined on the Web or your smartphone? It’s a real hassle, to be sure. But now, thanks to a new payment protocol being introduced by Mastercard that relies on deep learning to authenticate purchasers, you should see fewer false positives, less fraud, and faster approvals to boot.

Billions of dollars are on the line in the battle for control of the nation’s electronic payment byways. Fraudsters are eager to exploit any technological weakness to steal others’ hard-earned money, while banks, retailers, and consumers just want to make transactions as simple and secure as possible.

Mastercard
Mastercard

The latest round of battle in the United States saw the introduction of chip and PIN technologies in the 2016 timeframe. This technology (which has become more chip and less PIN) made it much easier to identify cards that were stolen or fake cards loaded with legitimate data from stolen cards. Thanks to the greater cryptographic horsepower embedded into the cards themselves, fraud rates for “card present” transactions plummeted, and the good guys were happy (provided they didn’t have to remember that dreadful PIN).

But the fraudsters weren’t done. As the good guys clamped down with chips and PINs, cyber thieves figured out new and nefarious ways of exploiting weaknesses in “card not present” transactions, such as those conducted electronically over the Web, using a mobile phone, or purchased in an application, where the chip offers no protection.

In response, folks in the electronic payment industry put their heads together and came up with a new way to thwart the bad guys and keep the world safe for online commerce. EMVCo, the consortium that was originally founded by Europay, Mastercard, and Visa, created 3D-Secure 2.0, which is an updated iteration of the three-domain secure (3DS) protocol that has been in place for some time.

>> Read : AI Powers Mastercard’s New Authentication Process

Source : Datanami

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