When it comes to online fraud, the more you know, the better you can protect yourself. As part of the team behind a popular e-commerce platform, I had the opportunity to work on creating a plugin that integrates with Sift‘s fraud detection service – a project that fundamentally changed how merchants could protect themselves from fraudulent orders.
Before our integration, there was a significant gap in the data available to detect fraud in this part of our platform. When operating in isolation like this, our fraud team was only able to deal with the fallout post-transaction, without the benefit of collective intelligence. This meant that even moderately sophisticated fraud rings could have taken advantage of our exposed transaction flow.
The power of Sift’s system lies in its machine learning capabilities, but machine learning is only as good as the data it’s fed. The idea was pretty simple; create a seamless way to gather relevant transaction data across hundreds of transactions while maintaining privacy and security standards.
The integration we built focused on capturing key data points throughout the customer journey. When a customer creates an account, Sift gathers information about sign-up patterns and account details. During checkout, it collects data on payment methods, billing and shipping address discrepancies, device IDs, geolocation information, and order characteristics. Finally, post-purchase, it tracks order status changes, and chargeback information.
What makes this particularly valuable is the breadth of data. Before our integration, Sift had limited visibility into the specific patterns of fraud within this particular e-commerce ecosystem. Each platform has its own unique fraud fingerprints – the behaviors that indicate potential fraud in one environment might be perfectly normal in another. By creating this plugin, we opened up an entirely new data stream that allowed Sift’s algorithms to identify platform-specific fraud patterns. In addition to platform-specific patterns, the anonymized data will also get used in training the larger collective algorithm used across the entire Sift service, so that even unrelated merchants can benefit from learning.
For example, we discovered that certain product purchase cadences were highly correlated with fraudulent activity. This wouldn’t have been apparent without aggregating and tracking this data. We also found that how much time passes between when someone creates an account and when they make their first purchase can be a strong clue about whether the order might be fraudulent.
The development process wasn’t without challenges. We had to find the right balance between collecting enough data to be effective against fraud while respecting privacy and maintaining fast checkout speeds. Collecting too much information could slow down purchases and frustrate customers, while collecting too little would leave gaps in fraud protection. This took a lot of cross-team discussion and iteration, but proved worth the trouble in the end.
We also needed to make the plugin accessible to merchants with varying levels of technical expertise. The interface had to be intuitive enough for a non-technical store owner to configure while providing enough flexibility for more advanced users to fine-tune their fraud prevention strategies.
Perhaps the most rewarding aspect of this project was seeing how the collective benefit has the potential to grow over time. With more data flowing into Sift, the quality of fraud detection improved for everyone in the ecosystem. New patterns emerged that wouldn’t have been visible with a smaller data set, and the system became (and will continue to become) increasingly adept at distinguishing legitimate transactions from fraudulent ones.
For our platform, the impact is proving to be substantial. Beyond the reduction in fraudulent orders, they gained back valuable time previously spent manually reviewing suspicious transactions and responding to chargebacks post-transaction. Not only does this save money in labor costs, it also meant that fees associated with chargebacks were decreased, and negative consequences from card networks are lessened.
Working on this project highlighted the power of collective data in fighting fraud. In the digital commerce landscape, isolation makes merchants vulnerable. By creating channels for secure, privacy-conscious data sharing, we helped build a stronger defense system for our platform that continues to improve as it learns.
If you’re running an online store today, fraud protection isn’t optional; it’s essential. By leveraging collective data through solutions like Sift, you’re not only protecting your own business but contributing to a stronger defense system for the entire e-commerce community. The days of fighting fraud in isolation are behind us. With the right tools and enough data, we can stay one step ahead of those looking to exploit online businesses, allowing you to focus on what matters most: growing your store and serving your legitimate customers.