The impact of risk management on revenue protection: an empirical evidence from Jordan customs

Abstract

Purpose Customs risk management has been widely recognized as a powerful tool to balance between trade facilitation and revenue maximization. However, most customs administrations worldwide, particularly in developing countries, are suffering from a lack of experience and knowledge to assess their risk management systems for revenue protection (RP). Customs risk management has a very limited legacy in the literature. Academic research is quite scarce and very limited, although its relevance to customs administrations. This paper aims to identify the key risk profiles and indicators that contribute to the protection of customs revenue and investigate the role of these risk profiles and indicators on customs RP using the case of Jordan Customs. Design/methodology/approach This study adopts a panel data approach by using the case of Jordan Customs. Data were collected from the risk targeting and selectivity system at Jordan Customs for the year 2019, a total of 600 observations. Findings The findings show that all risk targeting criteria except random selectivity (RS) and HS code have a significant positive association with RP. The findings also revealed that RS is an effective tool to prevent traders with fraud and offenses history from a prediction of targeting patterns and to assess the traders’ compliance and make sure their declarations are free from fraud or offenses. Moreover, the findings of this study indicate that customs administrations should adopt alternative programs such as authorized economic operator and post clearance audit as an effective means to measure and improve compliance. Research limitations/implications The main contribution of this study lies in proposing a model to assist customs administrations in assessing the performance of risk management systems to protect revenue. This model provides a comprehensive conceptualization and explanations necessary for numerous aspects of risk management projects and it assists to predict the outcomes based on formulated indicators. Practical implications This study provides guidelines for risk analysts on how to identify and assess the key risk profiles and indicators that effect on maximizing the detection of revenue leakage and to obtain interpretable and predictable results. In addition, the findings of this study will assist customs administrations in supporting revenue collection, minimizing uncertainty, allocating resources more effectively to target high-risk consignments, while simplifying the procedures for the safe consignments. Originality/value This paper is of significant value because it is one of the preliminary studies that empirically identify the risk indicators/profiles that contribute to the protection of revenue and investigate the predictive power of these risk indicators/profiles as a key predictor to protect customs revenue.