IMPROVEMENT OF AUTOMATED HS CODE SELECTION USING MACHINE LEARNING METHODS FOR OPTIMIZATION OF THE CUSTOMS CLASSIFICATION PROCESS

Abstract

Improvement of modern approaches to the optimization of the customs classification process through the introduction of machine learning methods for the automated selection of HS codes. Against the background of growing volumes and complexity of international trade, the importance of quick and accurate determination of classifications becomes extremely urgent. Let's consider the impact of using machine learning methods on improving the processes of product classification according to the Harmonized System (HS). In particular, we will analyze the advantages of automating the selection of codes, such as increased accuracy, speed and efficiency compared to traditional methods. We will demonstrate practical examples of the implementation of machine learning in the customs sphere and highlight potential prospects for the development of this direction. Research will show the importance of improvement and a modern approach to the customs classification of goods for optimization and improvement of international trade processes.In the conditions of the rapid development of international trade and the expansion of the assortment of goods, customs services are increasingly directing their efforts to the improvement and optimization of processes. One of the key directions of this improvement is the introduction of automated HS code selection, which determines the classification of goods according to the Harmonized System (HS).The importance of improvement and a modern approach to the customs classification of goods for optimization and improvement of international trade processes becomes even greater in the context of the rapid development of this sector and the expansion of the range of goods. As part of this improvement, the use of machine learning methods for automated selection of HS codes comes to the fore.These advanced techniques can improve not only the accuracy but also the efficiency of the product classification process. Automated selection of HS codes using machine learning accelerates classification determination and provides a high degree of reliability compared to traditional methods.