How Studypool proactively prevents fraudsters from cheating the system
Company Size
1,000+
Region
- America
Country
- United States
Product
- Sift
Tech Stack
- Machine Learning
- IP Tracking
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- Education
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Cybersecurity Services
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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