- New York-based online learning platform for teachers and students to gather
- Global community of eight million students and thousands of classes
- Student/teacher collusion to artificially boost teacher earnings
- SEO spam threatened to send users off platform to risky, spammy sites
- Automated removal of spam and risky users using Workflows
- Network tab reveals connected users colluding to commit fraud
- 8-10 hours a week in manual review eliminated
- Thousands of dollars in fraud losses prevented annually
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Learn with others, teach what you love
Skillshare is an online learning community for creators, where teachers and students gather to learn and teach anything they’re passionate about. Teachers can make money based on the minutes students have spent watching their classes, and students have the opportunity to pick up new skills – just look up a subject and start watching a class. In addition to the desktop site, on-the-go students can watch classes remotely using Skillshare’s iOS app. With over eight million students and thousands of classes in art, design, animation, photography, creative writing and more, Skillshare, based in New York City, is a global community with students and teachers from around the world.
Fraud, fake accounts, and spam
Skillshare pays teachers based on the minutes students spend watching their classes. Skillshare is an open platform, so anyone who meets the platform’s guidelines can teach. In rare cases teachers were creating fake student accounts and watching their own classes to bolster their earnings. The company also offers a referral program, in which teachers get paid every time someone signs up on the platform using their code. Skillshare’s fraud manager discovered collusion happening between teachers and students, with fraudsters using stolen credit cards to create many fake student accounts, and then redeeming the same teacher referral code across those accounts to get the fraudulent teacher referral bonuses.
Fraudsters were also using Skillshare to engage in SEO spam by creating landing pages on the platform for products they were selling. This was in an attempt to get spammy sites ranked higher in Google searches. Not only was this risky, as the landing pages could take users off-platform to questionable sites, but it was also detrimental to Skillshare’s reputation.
Unfortunately, Skillshare’s fraud management was primarily via SQL queries, and these schemes were only discovered after they had already happened. They needed a way to proactively detect and remove networks of colluding users, and to keep SEO spammers off of the platform.
Automation and proactive fraud detection
Skillshare turned to Sift’s Content Integrity product to get ahead of the fraudsters and their schemes. Trust and Safety Manager Susannah Page-Katz implemented Sift and, within two weeks, saw significant, accurate results from the machine learning model.
Susannah used Workflows to automate blocking and deleting risky accounts based on Sift Score (risk score based on behavioral attributes), eliminating the need to review accounts manually, and stopping problem users at sign up before they even made it onto the platform. She was also able to automate the removal of spammy SEO advertisement pages via Workflows, without having to spend valuable time tracking down these pages and manually removing them.
One of the most powerful Sift features for Skillshare has been the Network view, which was a game-changer for the company. It was a difficult, time-consuming process to try and unearth connected users via SQL queries; once Skillshare was able to visualize the entire web of students and teachers that were colluding to commit fraud, they not only better understood the scope of the problem, but were able to quickly remove those users from the platform and stop them from returning.
With Sift Lists, Susannah now has the ability to keep an eye on sleeper accounts: inactive users who’ve created accounts they’re not doing anything with, but who may later try to use the account to engage in fraud on the platform. Depending on how risky Sift has determined one of these accounts to be, Susannah can add them to a Watch List to ensure they don’t become a problem later, or can choose to ban the account.
In addition to Sift, Skillshare uses a third-party payment processor, but user information they were able to glean from that tool was limited. In using it in tandem with Sift, Sift was able to reveal more information on Skillshare’s accounts than ever before, allowing them to more clearly identify fraudsters.
Fraud prevention that remains scalable
Since using Sift, Skillshare has prevented thousands of dollars in fraud losses annually. They’ve also shaved 8-10 hours a week off of manual review time, which is critical for Susannah as a team of one. She’s been able to rely on Sift – even during seasonal spikes in activity – without having to hire an additional team member. As Skillshare continues to grow, Sift scales in tandem, leaving Susannah free to focus on things other than manual review and playing catch up to fraud.
“I would’ve needed another full-time employee by now without Sift. Sift is a real strength – it scales with us, which helps us maximize productivity.”