Revolut has developed a new machine learning technology for tackling card fraud and money laundering.

The challenger bank says its automated customer profiling technology detects patterns in financial behaviour and can spot fraud and money laundering in real-time, leading to a fourfold reduction in card fraud since it was launched in August.

The anti-fraud system uses machine learning methods to identify instances of drastic deviation in spending patterns and automatically freezes payments until the customer verifies that it is them.

Revolut found that the technology drove reductions in card fraud levels, primarily tackling common cases such as e-commerce payments, card cloning and card theft.

In a statement, the company said its machine learning technology has proved so effective that a number of legacy banks and financial institutions have requested to purchase and integrate it into their own systems.

Nik Storonsky, Revolut’s founder and chief executive, said: “What we can accurately display in 10 minutes would typically take a large bank over an hour to establish with their current manual processes.

“If you’re on a mission to reach tens of millions of customers and scale your business globally, then you cannot rely solely on manual human processes to effectively protect your customers against financial crime, especially as criminals are becoming more savvy in their tactics,” he concluded.

Revolut, which has more than two million app users, has also completed a wide-ranging data repository upgrade as it looks to scale up operations. The company’s lead data scientist told FStech its vision was to “automate” all of its banking processes.

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