Case Study: Studypool achieves lower fraud rates and streamlined detection with Sift

How Studypool proactively prevents fraudsters from cheating the system

Studypool is a global, two-sided ed‑tech marketplace that connects college students with verified tutors for on‑demand microtutoring. As the platform grew, it faced persistent fraud—chargebacks, fake or linked student accounts, and partner‑program abuse—and its initial IP‑based tools were neither accurate nor reliable enough to stop sophisticated schemes.

Studypool adopted Sift for case management and trust‑and‑safety workflows, leveraging geolocation, account‑linking, activity history, machine learning, and network visibility to proactively identify fraudulent behavior. The result: chargebacks are under control at a low, steady rate, false positives have declined, the fraud detection process is streamlined, partner‑program abuse is blocked, and operational efficiency and user insights have significantly improved.

Studypool

Chuck Lopez

Head of Operations