Fraud rings and repeat scammers threaten the marketplace
Before Sift, SEOClerks’ approach to fraud prevention was largely reactionary. They would receive a chargeback from a fraudulent account, then ban that user from the site. However, more often than not, that user would come straight back to the site and create another account for committing fraud. Not only was SEOClerks feeling the pain of chargeback fees, but they were concerned about bad users threatening good users’ trust in the community.
Despite having a legacy IP-based fraud-detection tool in place, the SEOClerks marketplace was hit with multiple types of fraudulent activity, including money laundering, referral fraud, account abuse, and friendly fraud. The main problem that didn’t seem to have an easy solution was money laundering using stolen credit card or PayPal, since they could see no clear relationship between multiple bad users—and their existing fraud tool didn’t give them any intelligence for spotting fraud rings or repeat abusers.
Sift has been amazing at keeping our fraud down. The system is easy to use and helps agents make a clear-cut decision. Much of the truly high-risk fraud was being missed by our previous methods. It's amazing!
Jordan DeLozier, CEO
Connecting the dots between fraudsters
Battling a 4% chargeback rate, SEOClerks met with PayPal (their credit card merchant) to discuss their options. From a list of fraud detection vendors suggested by PayPal, SEOClerks chose Sift and successfully integrated within two days. They saw results immediately, identifying previously undetected fraud rings that their previous tool had missed. By the end of their trial, Sift had become an integral part of SEOClerks’ daily workflow.
Now fraud management at SEOClerks—which was previously a “hit or miss” activity shared across all members of the support staff—has turned into a more strategic endeavor. Armed with machine learning-based intelligence from the Sift Console, the SEOClerks team easily uncovers hidden links between fraudulent buyers and sellers. The team uses Lists based on custom criteria to quickly analyze high-risk users and decide whether they should be approved or blocked.
Not only does SEOClerks regularly discover and ban large fraud networks from the site, but their previous problem of banned users returning and creating new accounts has disappeared. Sift’s machine learning detects those repeat offenders immediately, so SEOClerks can automatically ban them, preventing them from placing orders or messaging other users.
SEOClerks also uses Sift to dispute chargebacks involving friendly fraud—cases where a legitimate customer makes a purchase and then claims it wasn’t them—by presenting evidence from Sift to show that the purchase was made by the rightful owner of the account.
With Sift, all the tools for fraud detection are so readily available, it's easy to detect fraud in just minutes. Now that many of the fraudulent sellers are gone, we have more real buyers enjoying our site, happy with the work provided and purchasing more.
Jordan DeLozier, CEO
Sales are up, fraud is down
Since implementing Sift, SEOClerks has seen sales on their platform rise, buoyed by enhanced trust that users have in the marketplace. With fraudsters and scammers prohibited from creating accounts, good users are having an even better experience with the SEOClerks community. Plus, by labeling users as “not bad,” SEOClerks has been able to train their machine learning model to recognize good, loyal customers.
Meanwhile, SEOClerks’ fraud rate—previously at 4%—has declined significantly. Because Sift’s predictions are so accurate, the amount of time the SEOClerks team dedicates to fraud management has also shrunk significantly, “minimized to minutes versus hours.” On average, they’re manually reviewing 70% fewer orders than before they used Sift.
And SEOClerks has been able to tailor a fraudster’s user experience based on their Sift Score—for example, not allowing a high-risk user to pay with a credit card, send messages, or create new content.
By the end of the first month, we could not live without Sift as an integral part of our business.
Jordan DeLozier, CEO
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