Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data
This provides customs authorities with a scalable, transparent tool to shift from conventional to priority-based inspection protocols, supporting international environmental policies.
The paper tackles the problem of detecting trade data discrepancies in scrap plastic misclassification by proposing an interpretable machine learning framework that identifies an inverse price-volume signature, achieving 0.9375 accuracy.
We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data, confirming that the risk signatures are consistent. This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols, translating complex data into actionable intelligence to support international environmental policies.