If It’s a Circle, It’s a Problem

Here at SpecTrust, we like to talk a lot about the impact we’re driving for our customers. The focus on their fraud problems and subsequent needs drives our understanding of what’s happening at a particular company, as well as more broadly across industries. With these insights, we’re able to provide better customer support, better prioritize our product roadmap, and help decode the black box of fraud for shared learnings within and outside of the company. 

Here’s our first nugget of shared wisdom: if it’s a circle, it’s a problem.
The graph above shows how our simple visualizations of network traffic can help a fraud fighter instantly spot a bad actor and drill down to investigate their bad actions. Information has been redacted for privacy and the customer’s infrastructure has been hardened since this early snapshot was taken.

When our team built the SpecTrust Hub, their primary mission was to make it easy and fast to identify fraud. (Technically the visualization above shows a semi-circle, but you get the idea.) This real example of a bad actor crawling through a SpecTrust customer’s website shows a single device dynamically cycling through 11 IP addresses, starting a different web browsing session with each one, and even switching out emails across the sessions in an attempt to take over customer accounts with different login/password combinations. 

It is unclear whether this was a bot or a human, as the sessions occurred over ~22 hours and just about 30 login attempts were made. Given the low frequency of these attempts and the cycling of IP addresses and browser sessions, some systems would very well have let this bad actor continue attempting to hack into the system. 

Seeing visualizations such as the one above, where everything can be traced back to a single device, enables fraud fighters to 1) identify fraud, and 2) change access rules to prevent such account takeover attempts in the future. 

SpecTrust’s platform made this visualization possible by combing through comprehensive customer data and stitching together relevant pieces to ultimately flag a high-risk user and fraud in the making.

Want to see more circles? Read about collusion between buyers and sellers in our marketplaces whitepaper here.

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