Pasabi Fraud Prevention Platform is an AI-driven fraud detection and prevention solution that uses machine learning, behavioral analytics, and network analysis to identify and action bad actor activity on digital platforms. The platform operates across three core analytical dimensions: - Reputational analysis: Evaluates IPs, emails, and cross-references a repository of known bad actors shared across customers - Network analysis: Clusters data to surface fraud rings, suspicious behavior patterns, and connections between bad actors - Behavioral analysis: Scores entities against ML models to detect non-genuine behavior, including AI-generated harmful content Key use cases include fake account detection, fake review detection, counterfeit goods detection, and scam detection (e.g., romance fraud, recruitment scams). The platform provides risk scoring with human-readable labels to support enforcement decisions. It offers continual monitoring to detect emerging threats in real time, and supports automation to reduce manual moderation workloads. A network effect benefit is derived from data accumulation and feedback loops across the customer base. Results are accessible via a dashboard with cluster views, reporting, audit trails, data exports, commenting, and tagging. Integration into existing moderation and enforcement workflows is supported to enable end-to-end automation. Pricing is based on a Platform Risk Assessment model, with annual ($20,000/year) or quarterly ($30,000/year) assessment tiers, each covering up to 1 million records.