With the growth of online shopping and payments, IP address fraud detection actors are constantly looking for new ways to commit payment fraud. Ecommerce losses due to payment fraud are estimated to be $41 billion in 2022, which means businesses need robust tools to protect themselves from the risks of fraud. One of the most important tools is an IP fraud score, also called a FraudScore. Many online services claim to check an IP address’s Fraud Score, but they are not reliable and often provide false positives (safe assessment for suspicious or “clean” IP addresses) or false negatives (low Fraud Scores for clean IPs).
To calculate an IP fraud score, fraud prevention software takes data relating to a user’s IP address and feeds it through risk rules. These rules are then tallied to generate an overall IP fraud score. Factors can include a user’s location consistency, the presence of an ISP or TOR connection, whether it is a residential or business IP address, whether it has been blacklisted, and more.
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Viewing an IP fraud score allows you to quickly identify suspicious activity and prevent financial loss. Fraud detection using machine learning can help to minimize the risk of fake behaviors entering your site by analyzing multiple data points to detect red flags like a sudden change in geographic location, a high number of transactions coming from the same IP, or other patterns that have been linked to fraudulent behavior. By leveraging this technology, businesses can protect customers and improve efficiency, security measures, and compliance processes.