Sift > 实例探究 > How Studypool proactively prevents fraudsters from cheating the system

How Studypool proactively prevents fraudsters from cheating the system

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公司规模
1,000+
地区
  • America
国家
  • United States
产品
  • Sift
技术栈
  • Machine Learning
  • IP Tracking
实施规模
  • Enterprise-wide Deployment
影响指标
  • Cost Savings
  • Productivity Improvements
技术
  • 分析与建模 - 机器学习
  • 应用基础设施与中间件 - API 集成与管理
适用行业
  • 教育
适用功能
  • 商业运营
用例
  • 欺诈识别
服务
  • 网络安全服务
关于客户
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.
挑战
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.
解决方案
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.
运营影响
  • 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.
数量效益
  • Chargebacks are no longer a concern
  • Invaluable insights into users
  • Maintaining a fraud-free platform

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