Research of Risk Management Assessment Methods within the Customs Service Based on Big Data Analysis

Abstract

In the digital age, the abundance of data presents new avenues for risk assessment in various sectors. This article delves into risk assessment methods within the Customs Service that leverage big data analytics, aiming to provide a more comprehensive understanding of risks. It reviews machine learning techniques, predictive analytics algorithms, and neural network models, highlighting their strengths and limitations. The study also addresses the ethical aspects and challenges related to the confidentiality of commercial or personal data and the influence of these technologies on decision-making. The findings underscore the necessity for robust regulatory frameworks and transparent practices to harness the benefits of big data analytics in Customs risk management while safeguarding business-critical data. JEL: C55, G32