Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data

TL;DR

This paper introduces an interpretable machine learning framework that detects trade data discrepancies, specifically inverse price-volume signatures, to improve customs inspections and support environmental policies.

Contribution

It presents a novel inverse price-volume pattern detection method validated on large-scale trade data, enhancing transparency and efficiency in trade discrepancy identification.

Findings

Achieved 93.75% accuracy in identifying trade discrepancies.

Validated risk signatures with UN and firm-level data.

Provided a scalable, transparent tool for customs authorities.

Abstract

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.