Automated Anomaly Detection and Predictive Modeling for Counterfeit Agricultural Product Interdiction via Network-Based Risk Scoring (RQS-CAP)

**Abstract:** This paper introduces a novel framework, Automated Anomaly Detection and Predictive Modeling for Counterfeit Agricultural Product Interdiction via Network-Based Risk Scoring (RQS-CAP), aimed at significantly improving the efficacy of customs and border patrol efforts in combatting the rising tide of counterfeit and substandard agricultural goods. RQS-CAP leverages advanced data ingestion, semantic parsing, probabilistic network analysis, and machine learning algorithms to dynamically quantify risk associated with agricultural product shipments, enabling proactive intervention and resource allocation. This system represents a significant advancement by moving beyond reactive investigations to predictive risk assessment, utilizing disparate data sources to identify vulnerabilities within complex global supply chains. Its potential impact stems from a projected 30-45% reduction in successful counterfeit product entries within 5 years, coupled with a demonstrable cost savings of 15-20% attributed to optimized inspection resource allocation. The core innovation lies in the dynamic, probabilistic network construction that assimilates diverse data types into a unified risk scoring model.