Fraud scoring is a quantitative, analytical process used by payment processors and acquiring banks to evaluate the risk of a prospective or existing merchant. It involves assigning a numerical value (a score) that represents the statistical probability that the merchant will engage in fraudulent activity or incur high chargebacks.
How Do Fraud Scores Work?
Traditional fraud scoring relies on rigid, rules-based engines that analyze basic data points, such as credit history, business location, and matching names against sanctions lists. While helpful, legacy scoring often produces a high volume of false positives because it lacks contextual awareness.
The Shift to AI-Driven Scoring
Modern fraud scoring utilizes machine learning and fuzzy logic. Instead of just looking at isolated data points, AI evaluates the relationships between data—entity correlation—to determine true risk. It analyzes digital footprints, device IDs, and external web presence to build a highly accurate risk profile.
Dynamic AI Scoring with Onlayer
Onlayer replaces slow, manual guessing with precise, data-driven decisions. The platform generates transparent audit logs and real-time AI scoring for every decision. By applying custom AI-driven decision rules, you can auto-classify merchants instantly while providing clear, recommended remediation paths tailored specifically for "Pass with Notes" case outcomes.
