# Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data

arXiv: https://arxiv.org/abs/2511.08638 · HTML: https://mlsanity.com/paper/2511.08638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2025.

## Abstract (arXiv)

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.