Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Ramli, Muhammad Sukri Bin
Topic: Global trade and economics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4416233970
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