Improving manual review efficiency while reducing content fraud
公司规模
1,000+
地区
- America
国家
- United States
产品
- Sift
技术栈
- Not mentioned
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
适用功能
- 商业运营
用例
- 欺诈识别
关于客户
KSL started off as a television company within Deseret Management Corporation, gaining affiliation with NBC in the 1990s. As part of the overall brand, KSL Classifieds was built in the late 1990s to offer Utah residents a local online classifieds platform to complement the community news product. In 2009, Deseret Digital Media was formed to separate digital properties from traditional radio and TV media properties. As part of this move, the DDM Marketplace business unit (within which KSL Marketplace resides) was created to further distinguish local commerce marketplace entities from the online media entities. The Marketplace currently sees over 235 million page views per month and is growing rapidly as KSL aspires to become a national brand. Unfortunately, with all of the success and attention, KSL’s classifieds platform also became a popular target for fraudsters. In response, they hired Eric Bright as Vice President of E-commerce and charged him with not only growing revenue, but also stamping out KSL’s fraud challenges.
挑战
KSL.com, a user-driven platform of both buyers and sellers, was suffering from an existential problem due to a growing percentage of fraudulent postings. 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 autoban 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.
解决方案
Hiring five additional people just to review fraud is expensive, so Eric started evaluating Sift’s capabilities as an alternative. He found that trialing the Sift solution was easy; the full integration took just six weeks and they started to see results immediately. With just a few weeks of labeling, the KSL team trained their customized model to be so accurate that they could confidently rely on Sift Score ranges to automatically approve, reject, and review transactions. Sift’s accuracy in pinpointing fraudsters allowed KSL to identify and shut down fraud faster than ever before. That meant the KSL team could focus their energies on the truly suspicious users, keeping good customers happy with quick approvals.
运营影响
数量效益
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