What To Know
- The latest upgrade to the anti-fraud system of Mastercard is the use of artificial intelligence, which should allow for the identification of patterns in stolen card data faster.
- “With the help of the generative AI, it is possible to determine where your credentials might have been stolen, how it could occur, and how it can be fixed not only for you but for other customers who do not even know that their information has been stolen,” said Johan Gerber, EVP, Security and Cyber Innovation at Mastercard.
- It can go unnoticed for years and only surface when payment networks look for stolen card data, a merchant reports a breach, or the card is used for unlawful purpose.
NEW YORK —Mastercard declared that it expects to ramp up the capacity to identify stolen credit or debit card numbers, which could detect them before they are controlled by fraudulent individuals.
The latest upgrade to the anti-fraud system of Mastercard is the use of artificial intelligence, which should allow for the identification of patterns in stolen card data faster. This will give banks the opportunity to replace the lost or hacked cards before the criminals can use them.
“With the help of the generative AI, it is possible to determine where your credentials might have been stolen, how it could occur, and how it can be fixed not only for you but for other customers who do not even know that their information has been stolen,” said Johan Gerber, EVP, Security and Cyber Innovation at Mastercard.
Mastercard Based in Purchase, New York, the firm will now employ artificial intelligence al to assess various patterns and contextual information inclusive of geography, time, as well as incomplete yet compromised card numbers that appear in databases. This approach makes it possible to inform cardholders early enough so that they can change their compromised cards at an earlier date.
Furthermore, the improved ability to recognize patterns may also be used to find suspects, such as hacked merchants or payment processors, by analyzing batches of bad cards. Gerber pointed out that this capability far exceeds what humans are capable of doing with regular database searches or conventional techniques.
Currently, the dark web hosts billions of credit and debit card numbers that have been stolen and sold to the criminals. Such figures are usually obtained either from merchants during heists or consumers when they use their cards at susceptible outlets. It can go unnoticed for years and only surface when payment networks look for stolen card data, a merchant reports a breach, or the card is used for unlawful purpose.
“The good news is that we can now contact banks to make certain that those consumers are serviced and receive new cards as soon as possible, thereby disrupting their lives as little as we can,” concluded Gerber.
Currently, payment networks are in a process of migrating from static credit and debit card numbers to an identification for certain transaction. Yet this transition can take years, and even more so in the United States where change in payment technologies has been slow.
