Sun Finance’s Fraud Challenges
We’re always looking for new ways to find potential fraud, and one of our biggest challenges is in confirming the customer identity.
A fast growing company always makes you a target for fraudsters, and this is particularly true in the world of online lending. Sun Finance’s challenges were the ones other companies in the vertical are struggling to fend off, namely: fake identity fraud, synthetic identity fraud and ATO (account takeover).
“We’re always looking for new ways to find potential fraud” Kaspars says. “One of our biggest challenge is in confirming the customer identity – if application says customer name is John Smith we must be sure that we deal with real John Smith not someone who pretends to be him”
To help improve their verification processes, Sun Finance already employed device intelligence tools, email verification tools, and data from credit bureaus. “But we still needed to improve our success rate,” Kaspars continues. “It was a must to scale the company without refusing valid applications due to false positives.”
SEON’s Social Media Profiling Seals the Deal
We tested SEON’s email analysis tool with our own emails, and the results were incredibly accurate. We’ve had other demos where it all looks good on paper, and the test case doesn’t really deliver.
After one of Sun Finance’s data scientists became aware of SEON, a presentation was set up. A number of features were immediately appealing to the whole team, such as the great documentation and ease of integration.
“We tested SEON’s email analysis tool with our own emails, and the results were incredibly accurate,” Kaspars says. “We’ve had other demos where it all looks good on paper, and the test case doesn’t really deliver.”
Moreover, SEON’s social media profiling was the feature the team was the most excited about. “We had a feeling social media presence could help validate a user identity,” Kaspars continues. “But SEON’s Intelligence Tool was really what we needed to establish that correlation.”
Better Risk Underwriting for Safer Loan
We now use the returned data [from SEON’s social media profiling] both to confirm identities, and as a debt collection tool to contact non-paying customers.
SEON is now an integral part of Sun Finance’s risk underwriting models. They rely on exposed variables to accurately predict if a customer will default or repay the loan. And the flexible scoring system makes it easy to segment customers and trigger manual review without increasing friction for other applicants.
“The social media profiling is really the top feature that helps our manual review process,” Kaspars says. “In fact, we now use the returned data both to confirm identities, and as a debt collection tool to contact non-paying customers.”
The first correlation is a simple one: if the profiling tool reveals that no social media presence is connected to an email account, the fraud score goes up, which can trigger additional KYC questions.
For manual reviews, the fraud team can also look directly at the connected social media accounts and ask relevant questions. We can ask verification questions related to publicly available data returned by SEON. The wrong answer means they’re probably dealing with fraudsters who haven’t done their research.
The results? Fewer false positives, a decrease in fraudulent applications, and better repayment rates, all of which helps Sun Finance grow steadily and safely.
“I only have good things to say about the friendly and open team at SEON,” Kaspars concludes. “Their email API is a great asset in fighting fraud, and I’m looking forward to using the upcoming Phone API.”