Fraud.net

The platform tracks over 600 distinct fraud patterns spanning payment fraud, account takeovers, synthetic identity fraud, credential stuffing, money mule schemes, insider threats, and more, covering the breadth of fraud vectors that modern financial organizations face across both digital and traditional channels.

The core detection engine combines custom machine learning models, anomaly detection algorithms, and graph analytics including Graph Neural Networks to analyze every interaction and compute high-fidelity risk scores in milliseconds.

This multi-model approach addresses the limitation of single-method fraud detection, where sophisticated fraudsters who understand one detection methodology can craft attacks that evade it.

The combination of behavioral ML, anomaly detection, and graph-based relationship analysis creates overlapping detection coverage that is harder for fraud operators to systematically circumvent.

Entity screening at onboarding evaluates the risk profile of new customers, counterparties, or transactions at the point of entry before they are accepted into the system.

Continuous entity monitoring then tracks previously onboarded entities over time for changes in risk profile, catching fraud that emerges after initial screening rather than assuming that passing onboarding screening means permanent low risk.

Transaction monitoring provides real-time protection against fraud at the point of financial transactions, applying the full detection stack to each event.