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Keeping fraudulent ticket buyers off the platform
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用功能
- 销售与市场营销
用例
- 欺诈识别
服务
- 数据科学服务
挑战
Etix, the largest independent ticketing company in North America, was facing a growing problem of fraudulent transactions as their online and mobile business scaled. These fraudulent transactions resulted in chargebacks, costing the company money and the invaluable time of fraud analysts who had to respond to fraud attempts. The challenge of discovering fraud through manual review was daunting and unsustainable. Chargebacks often were not reported until after events, making it even more difficult to track and prevent fraud. Etix needed a solution that could respond in real time to potential fraud and prevent fraudulent orders before they were processed.
关于客户
Etix is the largest independent ticketing company in North America, with headquarters in the U.S. and offices in Europe and Asia. The company sees millions of unique users visit their website and mobile app every month, selling 50 million tickets per year via their ticketing platform. Etix aims to ensure a flexible, secure, and premium pre-event experience for their partners and customers. Their suite of products extends beyond online ticket sales to include marketing solutions, ads, and analytics, providing venues and promoters with a full arsenal of tools to make every event premium. Founded in 2000, Etix has grown significantly and continues to innovate in the ticketing industry.
解决方案
Etix decided to implement Sift’s fraud prevention solution after exploring its intuitive interface and easy-to-understand pricing plans. The solution was fully implemented and running in three weeks by a single engineer. Sift's machine learning solution allowed Etix to keep up with their order volume, while the global model’s predictive analytics provided insights to prevent fraudulent orders before they were processed. Leveraging the data of all of Sift’s users empowered the Etix team to block bad users and orders, significantly reducing the volume of orders in their review queues. The Etix team can now automate on Sift Scores, making for a more efficient review process.
运营影响
数量效益
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