Whether you’re an eCommerce merchant, payment processor, or financial institution, Transaction Scoring explained is key to minimizing losses and maintaining compliance. But it’s not as simple as analyzing some data and declaring a transaction fraudulent or not. Transaction scoring involves a complex, data-driven process that evaluates multiple deterministic and probabilistic criteria to produce an overall score for each individual transaction.

To calculate a risk score, machine learning technology compares each transaction to models of known fraudulent behavior, assigning a numeric value based on how closely the transaction matches those characteristics. Depending on the rules you set, this score can trigger specific actions such as adding a customer to a blocklist, flagging for manual review, or automatically rejecting a transaction.

Transaction Scoring Explained: Detect Suspicious Behavior Instantly

For example, let’s say a buyer’s email address is on a list of known blacklists and they appear to be using an IP associated with Tor nodes. Typically, these would be clear fraud indicators that raise red flags. However, since the buyer’s overall profile checks out, their transaction could be deemed legitimate by your rules.

Keeping your fraud detection tools up-to-date with new tactics is crucial to preventing false declines and avoiding costly delays that can frustrate customers. Fortunately, a streamlined risk scoring process can help you find the right balance between fraud protection and delivering a great customer experience.