In the battle against e-commerce fraud, it is incumbent upon online merchants to know the enemy and the tactics they employ. When it comes to account takeover, online merchants face the added challenge of recognizing a fraudster masquerading as a valued and trusted customer.
In this blog post, we will provide you with information on what account takeover is, and how to combat it.
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.
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.
Welcome to our first installment of MaxMind’s Best Practices Blog Series!
In this post, we discuss using the minFraud Service in conjunction with your AVS and CVV declined transactions in order to help you increase your conversion rate and stop more fraud. Continue reading →
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 →
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 →