The Amazing Ways How Mastercard Uses Artificial Intelligence To Stop Fraud And Reduce False Declines
Aside from the inconvenience it causes us, the cost to businesses and the wider economy of these false declines is around $118 billion – an amount 13 times higher than the cost of actual card fraud.
But fear not because, once again, AI has come to the rescue. Through its Decision Intelligence and AI Express platforms, Mastercard has used predictive analytics powered by machine learning to cut the rate that this happens by 50%.
I had the chance to speak to Ajay Bhalla, the company’s president for global enterprise, risk and security, about how this technology works and how AI is now helping Mastercard achieve more of its strategic objectives.
Bhalla tells me that the quantum leap in the ability to both detect fraud and reduce false declines has come about through its acquisition of California-based artificial intelligence specialists Brighterion.
Technology developed with Brighterion has enabled it to move to analysing data in real time. Machine learning algorithms must be incredibly efficient to handle the 75 billion transactions per year happening at 45 million global locations, which are processed by the Mastercard network.
Today, the decisions of whether or not to decline a transaction are based on a constantly flowing stream of data, and self-teaching algorithms, rather than a static sample dataset and fixed rules, which has had impressive results.
Bhalla tells me that the artificial intelligence systems, because they are self-learning, are always current and there is no longer a learning lag happening.
He states: “What it does is goes through billions of transactions and figures out what is the propensity of the transaction being fraudulent, and it gives this advice to the bank in the system, when the transaction goes through for authorisation.
“It’s helped us to catch billions of dollars’ worth of fraud.”
The system uses a real time stream of transactional data, along with external data including anonymised and aggregated customer information, and geographical information.
Geographical information is highly useful because not only does it give an overview of the types of transactions which are “normal” for a particular area, it also reveals what patterns of fraudulent activity are associated with it. Again, all of this information is aggregated in real time as it happens.
This means that patterns of fraud – which is often carried out at large scale by organised gangs, who will target businesses in a particular location, or attempt to “cash out” at ATMs spread across a city – can be detected, tracked and stopped.
“This is really good from a consumer standpoint because it means faster approval for the consumer, and it means more genuine transactions get approved. And merchants love it because for merchants, more approvals mean more business,” says Bhalla.
Building smart, automated systems has been a core strategy at Mastercard for many years, Bhalla tells me, but the acquisition of Brighterion and the incorporation of its technology into Mastercard systems has been a move towards “pure” AI. Many areas of its business, from customer service to anti-money-laundering measures, are set to benefit from an AI overhaul.
One key challenge has been ensuring a consistently high quality of data – as errors in transaction records or other data stores will inevitably lead to even the smartest machines making bad decisions.
Bhalla puts his company’s success with this down to the more than 50 years’ experience it has at generating and verifying transactional records – “We have been doing it for many, many years,” he tells me, “but that’s generally the challenge – you have to make sure your data is very, very good.”
A second challenge is determining the priorities when it comes to making decisions on where in the business to deploy potentially costly AI infrastructure.
A decision was made early on that increasing customer satisfaction levels was most likely to bring about the biggest long-term benefits.
