Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data

• URL: http://arxiv.org/abs/2511.08638v1
• Date: Thu, 13 Nov 2025 01:01:16 GMT
• Title: Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data
• Authors: Muhammad Sukri Bin Ramli,
• Abstract summary: Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease.<n>The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data.<n>This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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