Mastercard expands AI technology to help banks detect scams as they happen
24 September 2024, 00:04
The service has been extended to help receiving banks identify potential ‘money mule’ accounts being used by fraudsters.
Mastercard has expanded an initiative that helps banks to detect payment scams as they are happening.
Since early 2023, the Mastercard scheme has helped UK banks identify and stop potentially fraudulent payments before the funds leave the victim’s account.
The initiative, which uses artificial intelligence, works by scanning multiple data points associated with a transaction, providing a risk score in real time to the sender’s bank.
The scams, known as authorised push payment (APP) fraud, are sparked when someone thinks they are paying a legitimate person or organisation but they have been tricked by a fake website, email, text or phone call.
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Mastercard said the consumer fraud risk service has been extended to receiving banks so it can help them to detect when a payment may be destined for an account used by fraudsters.
Criminals use “money mule” accounts to move cash around. Money mules often keep a cut of the cash in return for allowing their bank account to be used by criminals.
Initial tests of the additional “inbound risk” alerts have seen improvements in the ability to identify high-risk mule accounts early, within the base of bank accounts, Mastercard said.
Johan Gerber, executive vice-president for security solutions at Mastercard, said banks are being provided with “additional lines of defence – helping them better identify and stop scams in their tracks”.
A voluntary reimbursement code is in place when people are tricked into transferring money to a fraudster. Many banks have signed up to it and some also offer their own fraud refund guarantees.
But next month, a mandatory code will come into force overseen by the Payment Systems Regulator (PSR).
From October 7, the rules will require banks to reimburse customers who are victims of bank transfer scams unless the customer has been grossly negligent.
