Risk models based on uncertainty quantification for illicit traffic time series in customs context

Abstract

Due to the increase of the worldwide trade, the flow of goods crossing borders is increasing; at the same time, the threats faced by society are growing. Customs are encountering many challenges and looking for new tools of decision making for countering such risks. This paper develops models based on uncertainty quantification to analyse the behaviour of the risk time series in customs. We start by introducing the Hilbertian approach related to the representation of random variables and addressing these approximations and their applications in UQ. Then we discuss an extension where these models are applied to handle the seasonal components of risks. The models are fitted to the seized quantities of the illicit traffic on five sites using moment matching method. The results provide a good description of important properties of the data and a tool of decisions making on risk analysis in cases of threats in global supply chain.