Revolut reduces bank card fraud using machine learning to detect anomalies

Solution:

Revolut has been building machine learning systems to develop deep insights and predictions around customer behavior so that it can identify new fraud patterns in real time, 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 both the customer and the business valuable time. What this means is that Revolut is able to build extensive data profiles on their customers to better understand their spending behavior and patterns. Its anti-fraud systems will then be able to detect any suspicious activity in real-time, based on abnormal activity which deviates from their usual behavior.