MACHINE LEARNING MODELS FOR PREDICTING HS CODE: PROSPECTS AND EFFECTIVENESS OF USE
Abstract
The article examines various machine learning models for predicting GN FEA codes based on product descriptions entered into customs declarations. GN FEA codes are widely used by all customs services due to a number of advantages, including a more convenient and simplified approach to calculating duties and preventing potential revenue loss. This study is based on a cross -industry process to develop a data mining methodology. The results demonstrate that machine learning models are effective tools for predicting GN FEA codes based on input data. 38
Keywords
machine learning, GN FEA codes, predictive models, customs services, revenue loss prevention, trade
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