Think you’ve seen the world’s dumbest criminals? You ain’t seen nothing yet!
Introducing the Sift Science Fraud-o-graphic, where we highlight some of the simplest ways to ID bad users. Online fraudsters can be just as feeble-minded as as those robbers that live-tweet their heists and thieves that leave behind their own drivers licenses.
Hundreds of newly hatched accounts on your site created on a single device? Chances are good it’s fraud. More than a family-sized number of users with a single billing address using your app? Hmm, sounds fishy. Old-school fraudsters have a hard time fooling the state-of-the-art systems that companies have in place to stop fraud. Need a good “no, duh” or face-palm moment? Check out the fraud-o-graphic! Read More
Protecting your online business from fraud has never been easy. With the rise of today’s on-demand economy, fighting fraud is even harder. While merchandise-focused e-commerce businesses have ample time to review transactions prior to shipping, orders in the on-demand world are placed and fulfilled in near real-time. The rise of mobile commerce and increasing popularity of app-based services have trained consumers to expect instant gratification. Without the luxury of manual-review time, companies like Uber, Instacart, EatStreet, RelayRides, andEatNow rely on Sift Science’s large-scale and customizable solutions to fight fraud for their on-demand offerings. Read More
For many of our customers, the Network Visualizations function in the Sift Science Console is a favorite tool to easily identify fraud rings, card testers, and fraudulent accounts. Because it's so important to our customers, we're proud to introduce a new version of Network Visualization that makes catching bad users even easier for your team (while also adding some often-requested features)!
We'll be rolling out the new Network Visualizations to all of our customers on Monday, June 15th. Because we're so excited about this update (and hope that you are too!), here's a sneak peek of what you can expect! Read More
In the first post of this series, we gave an overview of Sift Science’s architectural migration to React and Dropwizard. We followed up with some best practices for scaling React in a production setting and some tips on using React with D3. Today’s post will chronicle the front-end migration process of moving from Rails + Backbone + Marionette + Handlebars to a static Backbone + React console, and the challenges we encountered. Read More
Innovator. Leader. Influencer. Awesome-sauce. Yes, these are some of the labels that Sift Science gets, but not quite what I’m talking about. At Sift, labeling has pretty magical powers.
Pop quiz: how does machine learning learn? Good news, we have a study guide for you in the form of our new ebook on machine learning. Have a look and come on back when you're ready. Read More