MANAGEMENT OF CUSTOMS PROCESSES WITH THE HELP OF MACHINE LEARNING: OPTIMIZATION OF CLASSIFICATION AND AUTOMATION OF DATA PROCESSING - KRYVENCHUK, YURII; KRUPA, STEPAN (Herald of Khmelnytskyi National University. Technical sciences, 2025)
The accuracy of HS code classification plays a key role in ensuring effective customs clearance and international trade regulation. Incorrect code determination can cause significant financial losses for businesses due to incorrectly calculated customs payments, penalties, additional inspections, and delays in logistics. It can also complicate customs control processes and create risks for compliance with international trade regulation standards. Given the increasing volume and complexity of international trade, optimizing the HS code classification process is becoming an urgent task for businesses, customs authorities, and logistics service providers. The use of advanced technologies, such as machine learning algorithms, artificial intelligence (AI), natural language processing (NLP), and computer vision, allows for a significant increase in the level of automation of the goods classification process. These technologies enable the development of intelligent systems capable of analyzing large datasets, identifying patterns, and making accurate classification decisions with minimal human intervention. This helps to minimize the human factor, accelerate customs clearance, and reduce the number of errors, ultimately leading to more efficient trade operations. Studies show that the integration of these technologies into customs systems provides a more accurate classification of goods, which positively impacts the overall efficiency of logistics operations and reduces costs associated with erroneous classifications. The article pays special attention to analyzing the effectiveness of automated goods classification systems compared to traditional methods of determining HS codes. It presents statistical data demonstrating the dynamics of changes in classification accuracy after the implementation of automated systems. Additionally, it explores potential challenges, such as data inconsistencies, the need for continuous model updates, and the adaptation of AI-based classifiers to evolving trade regulations. The findings emphasize the necessity for further research in this field to refine existing classification models, enhance algorithmic performance, and ensure seamless integration with global trade platforms. Future studies should also consider the role of blockchain and big data analytics in improving transparency and reliability in HS code classification, as well as the implications of AI-driven decision-making in regulatory compliance and risk management.
Keywords: HS codes; artificial intelligence (AI); machine learning (ML); classification algorithms; natural language processing (NLP)