How Studypool proactively prevents fraudsters from cheating the system
Reduced fraud and false positives
Streamlined fraud detection process
Improved efficiency
OVERVIEW
Global online marketplace connects students with tutors
Studypool is a two-sided online marketplace that connects students with questions with tutors that can answer them. The ed-tech platform is on a mission to provide students with access to high-quality tutors, regardless of their time, location, or budget constraints. Studypool has adopted an innovative microtutoring concept, which connects students with thousands of verified tutors to help them with specific academic questions through on-demand tutoring sessions. Targeted specifically to college students, the platform offers 24/7 study help for topics ranging from business and programming to writing and humanities.
CHALLENGE
Chargebacks, fake accounts, and unreliable tools
When Studypool first launched, the platform saw users who were taking advantage of tutors by posting questions and later filing chargebacks, in an attempt to get free study help. Some users also tried to game the system by creating fake student accounts so they could pay themselves and later file a chargeback, ultimately getting their money back and a payout from Studypool. At the time, their internal fraud prevention tools couldn’t keep up with the types of fraud surfacing on the platform. The tools were only able to track IP addresses and weren’t accurate or reliable, so Studypool decided to look for a better solution—Sift.
SOLUTION
Tapping into Sift for case management
To mitigate fraud on the platform, Studypool’s trust and safety priorities include both identifying fraudulent activity and accurately anticipating ill-willed schemes. This is where Sift comes in, providing the necessary tools and information Studypool relies on for its fraud-fighting processes. The Studypool team uses Sift specifically for case management to study user behavior and aid in their decision-making process. Studypool relies on Sift to identify user geolocation, which accounts are linked to each other, and see user activity including if those users have been flagged in the past under different accounts.
RESULTS
Lowered fraud rates and boosted efficiency with Sift network visibility
Using Sift, Studypool has learned how to apply rules efficiently and lower false positives by pinpointing fraudulent behavior with reliable accuracy. After initially using Sift to lower chargebacks, their disputes are now under control at a low and steady rate, and have also seen significant improvements in operational efficiency.
Implementing Sift has also allowed Studypool to extend fraud detection across touchpoints and protect some of the features offered to users, such as their partner program. Sift helped surface users who were spoofing their IP address to cheat their way into the program and receive benefits. By leveraging Sift’s machine learning and network visibility, Studypool was able to filter out fraudulent users from joining their partner program and costing them time and money.
