Stopping credit card fraud, saving time and money
Company Size
200-1,000
Region
- America
Country
- United States
Product
- Sift
Tech Stack
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
StackCommerce is the leading native commerce platform for online publishers, communities, and brands. They power deal stores for the world’s top tech and lifestyle publishers by offering curated product recommendations tailored to each client’s audience. A fast-growing business in a thriving market, StackCommerce has more than 1,500 vendors offering products and services to over 200 million monthly users across more than 750 publishers’ websites. As part of their service, StackCommerce handles fraud management for any orders placed on their platform.
The Challenge
StackCommerce, a leading native commerce platform, was dealing with a significant amount of fraud involving purchases made using stolen credit cards. The most impactful type of fraud was the loss of digital goods that are distributed instantly. This not only hurt cardholders but also the merchants. StackCommerce needed to stop these transactions as quickly as possible and sought a solution that could prevent them in the first place. They were using a legacy, rules-based solution that didn’t include any machine learning. As the company’s order volume grew, they discovered the shortcomings of rules-based systems: they don’t learn and they don’t scale. The team found themselves reviewing hundreds – or even thousands – of orders per day, and fraud review became unmanageable.
The Solution
StackCommerce began looking for a tool they could confidently rely on to prevent fraud, and which also had automation capabilities. After extensive online research – and a recommendation by their payment gateway, Stripe – they landed on Sift. Using Sift’s extensive online documentation, they were able to get up and running in less than two weeks. The team saw accurate results immediately, but the results were even more striking after they trained their machine learning model by labeling users. The StackCommerce team uses Lists to efficiently manage their fraud review process, making instant decisions or flagging orders for additional verification. They also use Sift’s automation tools – Formulas and Actions – to automate fraud decisions, saving even more of the team’s precious time.
Operational Impact
Quantitative Benefit
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