A comprehensive fraudulent email risk scoring is essential to protecting your business from phishing attacks, identity theft and other fraudulent activity. While fraudulent activity may look different in context, a consistent pattern of behaviour can reveal red flags that can help you detect and prevent it. The best way to detect such patterns is to utilize email risk scoring.
Fraudulent email risk scoring uses a series of signals to identify the likelihood that a user is engaging in fraudulent activity. The system then translates these risks into an easy-to-understand score. Whether you choose a whitebox model with transparent logic or blackbox machine learning, this approach helps you uncover correlations and emerging fraud trends that static rule sets can miss.
A Beginner’s Guide to Using a Fraud Prevention API
When a user attempts to perform a task on your digital platform, the fraud scoring engine gathers submission data like email address, device ID and IP location. An automated enrichment process then expands the raw data via digital footprint analysis, revealing insights like linked social media accounts, device reputation and more. This data is then cross-referenced against external sources to uncover risk markers like the email’s domain quality, presence on blacklists or historical usage across platforms. The enriched data then undergoes rigorous analysis by FOCAL’s predictive rules engine, where complex algorithms assess various risk factors. The result is a fraud score, which provides a clear, actionable metric that indicates the level of risk associated with the user’s actions and helps you decide whether to approve, review or block them.
