Building a trustworthy bitcoin marketplace
公司规模
11-200
地区
- America
国家
- United States
产品
- Purse
技术栈
- Bitcoin
- Amazon
- Sift
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
- 电子商务
- 零售
适用功能
- 商业运营
- 销售与市场营销
用例
- 欺诈识别
服务
- 软件设计与工程服务
关于客户
Purse is a San Francisco-based company that is working to create the world’s largest online marketplace. They offer the lowest prices around, fast and frictionless commerce, and bitcoin payments. Purse has reached a global audience by matching bitcoin-holders who wish to purchase goods from Amazon with individuals seeking to liquidate their Amazon gift card balances to fulfill orders and receive bitcoin in return. They manage this multi-step transaction through their escrow system to hold bitcoin funds. Their user base of 150,000+ is truly international, and they process millions in bitcoins every month. Business runs around the clock, with 77% of visitors coming from desktop, and 23% mobile. With a native app launching, mobile traffic is expected to grow.
挑战
Purse, a San Francisco-based company, is working to create the world’s largest online marketplace using bitcoin. They offer the lowest prices and fast, frictionless commerce, processing millions in bitcoins every month. However, they faced a unique challenge with payment fraud. Since bitcoin transactions are final and irreversible, Purse had to detect and remove malicious actors attempting to game their bitcoin escrow system by purchasing items for Purse shoppers with fraudulent/hacked Amazon accounts. Initially, they combated fraud with internal tools, requiring three full-time support staff committed to fraud management and review. However, this was unscalable as the site grew.
解决方案
Purse decided to look into a machine learning solution to help them speed up the review process and scale with them as they grew. They collaborated closely with the Sift team and within two weeks, the solution was fully integrated via webhook, allowing Purse to pull Sift’s findings and data points directly into their order management system. Automating on these findings allows for a more efficient team. For instance, using the Sift Score to auto-ban users over a certain risk threshold gave the Purse Customer Support team the ability to focus on the good customers instead. Since keeping the experience frictionless is key, a non-obtrusive fraud solution is essential to the continuation of Purse’s growth in the U.S. and abroad.
运营影响
数量效益
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