Characterization and modelling of a customs operation

Abstract

Effective risk management is a prerequisite to find an acceptable balance between the objectives of a customs operation and the streamlined flow of goods. This requires the use of well-designed customs risk management models that scrutinize all cargo consignments in cyber space based on the analysis of rich data sets that can be used to accurately determine the risk represented by a cargo consignment without physically stopping it. The use of such models results in much reduced physical inspections without increasing the risk to customs of either losing income or allowing the influx of illegal contraband. It therefore represents a much more optimal compromise between the interests of customs and those of trade, reducing the economic cost to the region and making the region more attractive to global economic partners. In this paper we utilize different classification techniques to recognize patterns in electronic data transacted between customs and trade that characterize the risk attributes of cargo consignments. We subsequently extract models that can be applied in real time to minimize disruption of trade flows while reducing customs risks to below set thresholds. We quantify the impact of a variety in input factors and demonstrate how an optimal set of inputs can be selected to arrive at an effective risk management model. The diagnostic abilities of linear regression, neural network and classification tree techniques to predict both customs stops and infractions before they occur are compared