Building a trustworthy bitcoin marketplace
Company Size
11-200
Region
- America
Country
- United States
Product
- Purse
Tech Stack
- Bitcoin
- Amazon
- Sift
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- E-Commerce
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Fraud Detection
Services
- Software Design & Engineering Services
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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