{"ID":2843797,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08638","arxiv_id":"2511.08638","title":"Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data","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.","short_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 ac...","url_abs":"https://arxiv.org/abs/2511.08638","url_pdf":"https://arxiv.org/pdf/2511.08638v1","authors":"[\"Muhammad Sukri Bin Ramli\"]","published":"2025-11-10T07:40:56Z","proceeding":"econ.GN","tasks":"[\"econ.GN\",\"cs.LG\"]","methods":"[]","has_code":false}