Pattern Recognition of Aluminium Arbitrage in Global Trade Data
Автор: MUHAMMAD SUKRI RAMLI
Загружено: 2026-01-06
Просмотров: 6
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These research papers investigate the use of unsupervised machine learning to detect sophisticated arbitrage and fraud within the global aluminium trade from 2020 to 2024. The authors identify how environmental policies like the Carbon Border Adjustment Mechanism have created "tax wedges" that incentivise illicit activities such as trade-based money laundering and greenwashing. By applying a four-layer analytical pipeline, the study uncovers "hardware masking" strategies where scrap is misclassified as finished goods to exploit tariff discrepancies. The research also highlights the emergence of shadow hubs and a technique called void-shoring, where traders deliberately suppress destination data to vanish from official statistics. Ultimately, the findings advocate for a shift in customs enforcement toward algorithmic valuation auditing to counter these evolving financial distortions.
Author: Muhammad Sukri Bin Ramli
Read the research here:
https://arxiv.org/abs/2512.14410
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