Separating trusted users from the suspicious
KSL.com, like any classifieds marketplace, is a user-driven platform of both buyers and sellers, making trust a key ingredient to success. And with a growing percentage of fraudulent postings, KSL was suffering from an existential problem. Bad users were scamming legitimate users from all sides: publishing fake listings, taking over legitimate customer accounts, and running scams from hijacked accounts. Malicious users were also harassing the sellers of real listings, trying to scam them out of their goods and services.
The main challenge Eric faced was not only finding and eliminating existing fraud, but also blocking bad users as they tried to re-access the site after one device or account was banned. KSL needed the ability to auto-ban bad users and repeat offenders. Fighting an imposing fraud rate of 75-80% in some of the more popular sections of the site, KSL’s sole fraud analyst wasn’t able to keep up with the demands placed on their internal fraud tools and manual review process—so the team brought in a traditional fraud management vendor.
After two years of struggle, that fraud solution still wasn’t fully in place. KSL’s fraud analyst had to review every order to train the system and the solution was slow to integrate. After finally getting the product online, KSL discovered that not only was this solution inaccurate and ludicrously expensive, but it also wasn’t scaling. Instead of adapting to KSL’s needs, the vendor recommended the Marketplace team hire five more fraud analysts to overcome the solution’s deficiencies. After this painful experience, KSL was ready for a powerful and accurate solution that could drive automation and reduce (not increase) their investment in manual review.