How Favor Delivery achieved growth while reducing risk
公司规模
Mid-size Company
地区
- America
国家
- United States
产品
- Sift Payment Protection
技术栈
- Sift Workflows
- Sift Insights
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 应用基础设施与中间件 - API 集成与管理
适用行业
- 零售
适用功能
- 销售与市场营销
用例
- 欺诈识别
服务
- 系统集成
关于客户
Favor Delivery is a Texas-based on-demand delivery service. Via the mobile app or desktop site, users can place orders for anything from takeout food to last-minute needs from the drugstore, and Runners (delivery assistants) make the delivery in under an hour while keeping users updated every step of the way. Users get what they need in good time and at a great price, while Runners have the freedom to either supplement their income or replace a traditional job by delivering with Favor Delivery. Favor Delivery operates within all major cities in Texas and is continuing to expand throughout the state.
挑战
As Favor Delivery expanded, they experienced an increase in the number of chargebacks. The growth of fraudulent accounts and account takeover (ATO) attempts were becoming more frequent. Favor Delivery was using their internal heuristic system to manually search for fraud, which wasn’t scalable and couldn’t keep up with the volume of incoming orders. They needed a proactive solution that could automate and keep them ahead of fraud – not struggling to keep up with it.
解决方案
Favor Delivery turned to Sift Payment Protection; the integration took less than two months, and within a month after integration Favor Delivery started to see powerful results. Favor Delivery’s Account Review Team began utilizing Sift Workflows to manage their fraud logic, auto-accepting most orders and auto-blocking the riskiest. With Sift Insights, the team used the Routes metrics to determine how many orders were hitting a given route and whether that was effective or causing too many false positives. And the Network and Activity features within an order accelerated manual reviews, as the team could determine whether an order shared risky attributes with other fraudulent orders and quickly decide whether they wanted to accept or reject the order.
运营影响
数量效益
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