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Towards Optimal Free Trade Agreement Utilization through Deep Learning Techniques

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Title:Towards Optimal Free Trade Agreement Utilization through Deep Learning Techniques
Authors:Lahann, Johannes
Scheid, Martin
Fettke, Peter
Keywords:Machine Learning and Predictive Analytics in Accounting, Finance and Management
deep learning
free trade utilization
optimization
Date Issued:07 Jan 2020
Abstract: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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63918
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.179
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance and Management


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