Leverage personal relationships – not credit scores – to lend and borrow money

Zirtue is a mobile relationship-based lending application that simplifies loans between family, friends, and trusted relationships with automatic ACH loan payments. Both parties agree to a loan repayment schedule, the payments are made automatically via the payer’s bank account, and the awkwardness of asking to be repaid becomes a thing of the past. Based in the US, Zirtue grows 60% month-over-month as they work to create a more financially inclusive world.


Friendly fraud and limited resources to fight it

Zirtue’s automated payments and streamlined platform make repayment easy – but dishonest loan recipients make the process difficult. The Data Analytics team found that growing numbers of users were contacting their banks to dispute their loan payments, making false claims that they had not authorized the transactions, despite having agreed to the terms of the repayment plan. This issue was compounded by the fact that Zirtue had access to a very limited amount of user data, preventing them from proactively recognizing suspicious behaviors and stopping the fraud before it happened.

Additionally, the vetting process for taking out a loan was lengthy and required tedious and time-consuming email exchanges between Zirtue and the borrower, to ensure the borrower could confirm their identity. This manual work frequently delayed loans, creating headaches for the Data Analytics team and borrowers alike, and it was looking as though another team member would need to be hired to help handle the workload. Zirtue needed a solution that would stop friendly fraud before it became an issue, and streamline the vetting process to create better user experiences and optimize their data analysts’ time.


A game-changing wealth of data

Thanks to the breadth of Sift’s global network, the Payment Protection product opened the door to data that Zirtue didn’t have, completely revolutionizing the way the Data Analytics team managed fraud. Gone were the days of spending 15-20 hours a week on vetting new loans; instead, the team implemented Workflows that automatically blocked users with certain Sift Scores (risk score based on behavioral attributes), eliminating the need to pore over documents and send multiple emails to ensure users were trustworthy. With the Network feature, Zirtue’s team has been able to identify additional accounts connected with fraudulent users, preventing fraud rings from transacting on the platform.

Having more information and data than ever before has made it possible for the Data Analytics team to learn not just about fraudulent users, but about trusted users, as well. This full-picture understanding of their user base has made the team more knowledgeable overall, and better equipped to spot suspicious behavior and activities.

Will Traweek
“Sift absolutely knocked our socks off. I remember the smiles that went around the room when we clicked on an account and saw the amount of data that was on that dashboard.”


Significant drop in fraud losses and time spent vetting users

After three months of using Sift, Zirtue’s fraud loss dollars were reduced to 1.6% of what they were prior to implementing Payment Protection. They were also able to trim 14-18 hours off of their manual reviews every week, eliminating the need to hire another data analyst to focus on fraud. Now the team is able to shift their attention to creating delightful customer experiences, and making transactions between family, friends, and trusted connections easier than ever.

Will Traweek
“Sift prevented us from having to make an extra hire, saved us manpower hours weekly, and dropped our fraud loss dollars to 1.6% of what they were prior to using Sift. Any one of those three would have sold us, but Sift gave us all three.”