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Revolut unveils new fleet of machine learning technology

by LLB Reporter
13th Nov 18 7:56 am

Revolut, Europe’s fastest growing financial technology company, has today announced their intention to become a world leader in customer protection, and unveiled a new fleet machine learning technology that has already seen a fourfold reduction in card fraud.

Instead of being completely reliant on timely manual processes, Revolut is determined to tackle card fraud and money laundering with algorithms, leveraging the power of machine learning and computational techniques to better protect their customers.

Over the last 12 months, Revolut has heavily invested in scaling its data science and engineering functions in line with its vision to automate, accelerate and improve the quality of decision-making when it comes to card fraud and money laundering.

Revolut’s state of the art technology is able to develop deep insights and predictions around customer behaviour so that they can dynamically identify new card fraud patterns without explicit human intervention. These systems work by applying complex mathematical models to large sets of data in order to identify anomalies, offering a greater degree of accuracy when it comes to decision-making, and saving their customers valuable time.

Over the last two months, Revolut has been testing their new anti-fraud system which is able to detect suspicious activity in real-time, based on abnormal spending behaviour. If a customer’s payments drastically deviate from their usual spending habits, Revolut is able to automatically freeze card payments until a customer verifies from within the app that it is actually them. This process cuts out the fat and avoids the customer having to go through a lengthy security process in order to get their account unblocked.

Since early August, which is when Revolut officially put this new system in place, the company has seen a fourfold reduction in card fraud levels, primarily tackling common cases such as e-commerce payments, card cloning and card theft.

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