DIGITAL CONTROL MECHANISMS FOR ACCURATE CLASSIFICATION OF GOODS BY HS CODES IN FOOD IMPORTS

Abstract

This research paper examines the digital transformation of the classification system for food imports according to the Harmonized System (HS) codes of Foreign Economic Activity. The relevance of the study is driven by the increasing cases of customs duty evasion through the manipulation of the composition and physicochemical properties of food products, as well as the circumvention of non-tariff regulatory measures such as certification, phytosanitary, and veterinary controls. The author proposes a "Smart Classification" mechanism based on Artificial Intelligence and Big Data analytics for the automated verification of HS codes. Digital algorithms have been developed to identify discrepancies between text descriptions of goods, laboratory findings, and international reference data. Furthermore, the study provides practical recommendations for creating a "digital passport" for food products and integrating customs laboratory results into the Risk Management System (RMS) in real-time to minimize human error and ensure the stability of state budget revenues.