Revolut’s frictionless onboarding, immediate access to financial services, and ease of use for managing money and trading are what make it so popular with customers. Fraud is an issue faced by the whole industry and Revolut has made significant investments in its systems, processes and people to ensure that it protects its customers as it scales and grows.
Dmitri Lihhatsov, Fincrime Product Owner at Revolut, explains how the company fights back:
And the key to better risk models? Access to better information. This is precisely why Dmitri and his team turned to SEON, exploring how enriched data could fit into their meticulous strategy.
“We use raw SEON data alongside our custom-developed features to train our machine-learning models. For instance, in addition to social and device signals, we started looking into the CNAME data to ensure we fully leverage SEON.”
Allow rapid decision making for a frictionless onboarding experience. Leaving your team more time to help your Neobank grow.
The results speak for themselves. Even with the company’s hyper-optimized models, plugging in extra data moves the needle toward a safer, more effective onboarding experience.
“SEON improves our accuracy by an extra 200 basis points. That doesn’t sound like a lot, but we are talking about 200 bpis of fraud cases we would be losing otherwise. And it’s often the last few points that are so hard to win as we fine-tune our models.”
More importantly, SEON allows Revolut to reach incredibly high rates of prevention accuracy while helping the company manage its OpEx costs.
“When I ask my team, do we really need that extra data? Is the ROI worth it for us? The answer is always yes. Yes, it adds value – especially when we look at SEON’s price per API request, it makes complete sense for Revolut.”
Find out how to use SEON’s raw data to enrich machine learning models for your own manual look-ups from the video below:
To learn more about Revolut, please visit www.revolut.com