The Future of Customs

In this paper, we look at three proposed regulatory developments in the EU (regulation on fluorinated gasses, the Carbon Border Adjustment Mechanism and the Regulation to Prohibit Products made with Forced Labour) to assess the role these envisage for Customs and the application of elements from the Customs enforcement toolbox. We find that the expected roles of Customs in our three cases are quite different, and that their reliance on the standard customs tools is rather minimal. Therefore, a thorough discussion is required regarding whether Customs should be involved in new enforcement activities that demand different, enhanced or new elements in the enforcement toolbox, or that maybe another authority, at another time and place in the supply chain, should become competent. In any case, for regulations that require enforcement based on information of the entire supply chain, customs agencies need to upgrade their competencies in their role as an enforcement agency at the border. These competencies revolve, at least, around cooperation with other competent authorities, as well as the integration of system-based and transaction-based supervision

Detecting Suspicious Timber Trades

Developing algorithms that identify potentially illegal trade shipments is a non-trivial task, exacerbated by the size of shipment data as well as the unavailability of positive training data. In collaboration with conservation organizations, we develop a framework that incorporates machine learning and domain knowledge to tackle this challenge. Modeling the task as anomaly detection, we propose a simple and effective embedding-based anomaly detection approach for categorical data that provides better performance and scalability than the current state-of-art, along with a negative sampling approach that can efficiently train the proposed model. Additionally, we show how our model aids the interpretability of results which is crucial for the task. Domain knowledge, though sparse and scattered across multiple open data sources, is ingested with input of domain experts to create rules that highlight actionable results. The application framework demonstrates the applicability of our proposed approach on real world trade data. An interface combined with the framework presents a complete system that can ingest, detect and aid in the analysis of suspicious timber trades