The global e-commerce market size grew to a record $2.2 trillion in 2017, and there areno signs of a slowdown in 2018, as more and more people turn to the virtual marketplace for their shopping needs.
But this tremendous exponential growth also has a dark side. As digital channels are opening, and international brands are diversifying their offerings, fraudsters are seeking new, or rediscovering old, ways to capitalize on global opportunities.
When it comes to fraud detection, finding proxies is a big topic. But why? Fraud detection begins with thinking intelligently about the IP address associated with a transaction. Where is that IP address, and how does that location relate to other transaction data? Whereas most IP addresses inspire confidence, those associated with a proxy generate suspicion.
The ‘riskScore’ is the most actionable piece of data returned by MaxMind’s minFraud service. The ‘riskScore’ simplifies the accept/reject/review decision for online orders, helping merchants to prevent fraud and reduce time spent on manual review. This blog post will explain why minFraud service users should use the ‘riskScore’ instead of the ‘score’ to catch fraud.
Prior to February 2007, before the ‘riskScore’ was introduced, the only risk estimation element the minFraud service returned was the ‘score’ value. The ‘score’ ranges from 0-10 and is calculated by a static risk model formula that uses previously observed risk factors. This return value is deprecated and the risk model behind it is no longer updated. Continue reading →