Sift > 实例探究 > Stopping credit card fraud, saving time and money

Stopping credit card fraud, saving time and money

公司规模

200-1,000

地区

America

国家

United States

产品

Sift

技术栈

Machine Learning

实施规模

Enterprise-wide Deployment

影响指标

Cost Savings
Productivity Improvements

技术

分析与建模 - 机器学习

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

数据科学服务

关于客户

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.

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挑战

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.

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解决方案

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.

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运营影响

With Sift, StackCommerce has reduced their chargeback loss rate by 25%, saving more than $2,000 per month on chargeback fees.
Despite a 30% increase in monthly order volume since implementing Sift, the StackCommerce team hasn’t had to hire additional staff to manage fraud.
They are now down to a single employee spending no more than two hours per day on manual review.
The insights provided by the Sift Console have also helped StackCommerce learn more about what types of deals attract fraudsters.
The Formulas and Actions integration has led to even more accurate and powerful fraud prevention.

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数量效益