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As a global payment processor with more than 2 billion cards in use around the world, Mastercard engages in a constant fight against fraud. The company processes around 165 million transactions per hour, and every one of those transactions must be examined in real time for signs of fraud.1

To accomplish this mind-boggling task, Mastercard relies on the power of high performance computing (HPC) systems driving machine learning algorithms. These algorithms apply 1.9 million rules to each transaction in a matter of milliseconds. These rules examine things like the cardholders’ buying habits, geographic locations and travel patterns, along with real-time data on card usage — such as what they are trying to buy and where they are trying to buy it.

None of this would be possible without machine learning algorithms.

So, what is machine learning? 
In a few words, machine learning is simply the process of training a system by feeding large amounts of data into an algorithm to help the system learn how to perform a task. Machine learning is one of the most fundamental building blocks for the AI solutions used in the fight against fraud, cybercrime and similar threats to the enterprise.

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