Tag Archives: minFraud

Using Custom Inputs to Refine Your Response to Fraud

Online fraud is a complex, hard to detect, and constantly evolving type of crime with serious business consequences. While many e-commerce merchants are looking for new ways to engage with customers, fraudsters are also looking for new ways to exploit them. In a way, every touchpoint you create – from buy online/pick up in store options to social click-to-buy ads, mobile shopping to loyalty rewards programs – is another opportunity for cybercriminals to bypass your fraud screening.

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Proxy Detection – Why Fraudsters Give Proxies a Bad Name

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.

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MaxMind Speaking at MRC. March 20, 2014 – Mark Your Calendar!

MaxMind, the industry-leading provider of IP intelligence and online fraud detection tools, has been invited to present at the 2014 Merchant Risk Council (MRC) eCommerce Payments & Risk Conference in Las Vegas. We are excited to be joined by a top Risk Analyst from our merchant partner, Shopify, the leading commerce platform that allows anyone to easily sell online, at their retail location, and everywhere in between. Continue reading

Why Should I Use the minFraud Service’s ‘riskScore’ Instead of ‘score’?

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 This return value is deprecated and the risk model behind it is no longer updated.
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