Designing a new methodology for customs risk models

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. The customs operations in many developing countries are characterised by high levels of physical inspections, with resulting disruption of trade flows, but with little positive impact for the regional economy. Most developed economies have moved towards customs risk management models based on the analysis of rich datasets that can be used to accurately determine the risk represented by a cargo consignment without physically stopping it. The use of such models can result in reduced physical inspections without increasing the risk to Customs of either losing income or allowing the influx of illegal contraband. It, therefore, represents a 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 develop a rigorous methodology that utilises electronic data transacted between Customs and trade to characterise the risk attributes of cargo consignments and then extract a model that can be applied in real time to minimise disruption of trade flows while reducing Customs risks to levels that are below set thresholds. This paper builds on previous work of Laporte (2011) and others but extends their results by developing a more detailed methodology to quantify the impact of a variety of input factors and demonstrating how an optimal set of inputs can be selected to arrive at an effective risk management model