Mastercard has announced the implementation of generative AI technology to significantly accelerate the detection of compromised cards, enhancing protection for cardholders and securing the payment ecosystem.
Fraudsters frequently steal millions of payment card numbers through methods like spyware, malware, and card skimming, often selling partial card details on illegal websites.
Mastercard is now leveraging generative AI to predict the full details of these compromised cards, enabling banks to block them more quickly than before.
The new technology scans transaction data across billions of cards and millions of merchants at unprecedented speeds.
This enhanced capability allows Mastercard to detect complex fraud patterns and protect future transactions against emerging threats.
The generative AI-based predictive technology has doubled the detection rate of compromised cards while simultaneously reducing false positives in fraud detection by up to 200%.
Furthermore, it has increased the speed of identifying at-risk or compromised merchants by 300%.
As a result, Mastercard can alert banks more swiftly and accurately when a card is likely compromised, allowing for timely blocking and reissuance of cards.
Continuous monitoring of attempted transactions on compromised cards further mitigates fraud and enhances cybersecurity.
This latest advancement builds on Mastercard’s existing suite of security solutions, such as Cyber Secure, which has been available since 2020.
Cyber Secure integrates technology to provide transparent cybersecurity information on bank and merchant profiles, including details of suspected compromised cards.
Johan Gerber
“Until now fraudsters may have thought they were operating in obscurity, seeking to launder the card details of millions of unsuspecting victims.
Thanks to our world-leading cyber technology we can now piece together the jigsaw – enhancing trust to banks, their customers and the digital ecosystem as a whole,”
said Johan Gerber at Mastercard.
Featured image credit: Edited from Freepik
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