Towards Optimal Free Trade Agreement Utilization through Deep Learning Techniques

Johannes Lahann; Martin Scheid; Peter Fettke

In: Tung Bui; Ralph Sprague (Hrsg.). Proceedings of the 53rd Annual Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences (HICSS-2020), January 7-10, Maui, HI, USA, ISBN 978-0-9981331-3-3, IEEE Computer Society, 2020.


In recent years, deep learning based methods achieved new state of the art in various domains such as image recognition, speech recognition and natural language processing. However, in the context of tax and customs, the amount of existing applications of artificial intelligence and more specifically deep learning is limited. In this paper, we investigate the potentials of deep learning techniques to improve the Free Trade Agreement (FTA) utilization of trade transactions. We show that supervised learning models can be trained to decide on the basis of transaction characteristics such as import country, export country, product type, etc. whether FTA can be utilized. We apply a specific architecture with multiple embeddings to efficiently capture the dynamics of tabular data. The experiments were evaluated on real-world data generated by Enterprise Resource Planning (ERP) systems of an international chemical and consumer goods company.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence