Dec 28, 2023, 02:50 PM
Credit card companies have been using artificial intelligence for quite some time, and one major provider is now looking at generative AI for additional solutions.
MasterCard International Inc. is the second largest credit card network by purchasing volume, and each transaction is scored at lightning-fast speed to determine the probability of fraud using AI. MasterCard’s familiarity with AI has been a boon for the merchants it serves.
“We’ve been doing this for a very long time, for a number of years; and on an ongoing basis, about 2% of the transactions are denied, which ends up saving merchants billions of dollars,” said Manu Thapar (pictured, left), chief technology officer of MasterCard. “Some of the fundamental concepts of large language models, which can be used for language generation, can also be used for code generation, and we are using that, looking at that to improve developer productivity. We are also looking at ways to extend generative AI for improving the rest of the business.”
Thapar spoke with theCUBE industry analyst John Furrier at the “Supercloud 5: The Battle for AI Supremacy” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Drew Jenkins (right), head of the AWS alliance at Persistent Systems Inc., and they discussed how MasterCard, Persistent and Amazon Web Services Inc. are collaborating on a set of new solutions for generative AI. (* Disclosure below.)
Leveraging global data
MasterCard uses AI and machine learning services from AWS to improve its fraud detection capabilities globally. The credit card giant is also working with Persistent to develop ways to improve the speed with which it can deploy software in the cloud.
“The actual code the models run is not that large in terms of lines of code, but it’s the data that makes the difference,” Thapar said. “The fortunate part is that MasterCard has a huge amount of data worldwide that helps us get better, learn better, and machine learning is all about having good data and being able to leverage that successfully.”
For Persistent, a collaboration with AWS and its work with MasterCard provide an opportunity to fund new solutions in advancing the use of generative AI in the enterprise.
“We’re trying to drive experimentation with our customers when it comes to generative AI,” Jenkins said. “We just signed a strategic collaboration agreement with AWS specific to generative AI. What that means is they are providing increased investment in Persistent in exchange for us making commitments to grow that business, and a part of that is funding proof of concept for customers like MasterCard and others.”
A proof of concept could provide advances in how generative AI will be used in the future by major credit card networks, such as MasterCard for fraud monitoring.
“If we look at how large language models work, they basically take a sequence of words and predict the next word,” Thapar explained. “Once that’s predicted, that sequence is taken and the next word is predicted. One can move from there to taking a sequence of transactions and then predicting the next transaction. If the actual transaction is far away in terms of vector space from the predicted transaction, we know that it has some probability of fraud.”
https://siliconangle[.]com/2023/12/08/mastercard-extends-its-fraud-detection-capabilities-through-partnership-with-persistent-systems-and-aws-supercloud5/
MasterCard International Inc. is the second largest credit card network by purchasing volume, and each transaction is scored at lightning-fast speed to determine the probability of fraud using AI. MasterCard’s familiarity with AI has been a boon for the merchants it serves.
“We’ve been doing this for a very long time, for a number of years; and on an ongoing basis, about 2% of the transactions are denied, which ends up saving merchants billions of dollars,” said Manu Thapar (pictured, left), chief technology officer of MasterCard. “Some of the fundamental concepts of large language models, which can be used for language generation, can also be used for code generation, and we are using that, looking at that to improve developer productivity. We are also looking at ways to extend generative AI for improving the rest of the business.”
Thapar spoke with theCUBE industry analyst John Furrier at the “Supercloud 5: The Battle for AI Supremacy” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Drew Jenkins (right), head of the AWS alliance at Persistent Systems Inc., and they discussed how MasterCard, Persistent and Amazon Web Services Inc. are collaborating on a set of new solutions for generative AI. (* Disclosure below.)
Leveraging global data
MasterCard uses AI and machine learning services from AWS to improve its fraud detection capabilities globally. The credit card giant is also working with Persistent to develop ways to improve the speed with which it can deploy software in the cloud.
“The actual code the models run is not that large in terms of lines of code, but it’s the data that makes the difference,” Thapar said. “The fortunate part is that MasterCard has a huge amount of data worldwide that helps us get better, learn better, and machine learning is all about having good data and being able to leverage that successfully.”
For Persistent, a collaboration with AWS and its work with MasterCard provide an opportunity to fund new solutions in advancing the use of generative AI in the enterprise.
“We’re trying to drive experimentation with our customers when it comes to generative AI,” Jenkins said. “We just signed a strategic collaboration agreement with AWS specific to generative AI. What that means is they are providing increased investment in Persistent in exchange for us making commitments to grow that business, and a part of that is funding proof of concept for customers like MasterCard and others.”
A proof of concept could provide advances in how generative AI will be used in the future by major credit card networks, such as MasterCard for fraud monitoring.
“If we look at how large language models work, they basically take a sequence of words and predict the next word,” Thapar explained. “Once that’s predicted, that sequence is taken and the next word is predicted. One can move from there to taking a sequence of transactions and then predicting the next transaction. If the actual transaction is far away in terms of vector space from the predicted transaction, we know that it has some probability of fraud.”
https://siliconangle[.]com/2023/12/08/mastercard-extends-its-fraud-detection-capabilities-through-partnership-with-persistent-systems-and-aws-supercloud5/
