As an increasing volume of international trade activities around the world, the amount of cross-boarder import declarations grows rapidly, resulting in an unprecedented scale of potentially fraudulent transactions, in particular false commodity code (e.g., HS Code). The incorrect HS Code will cause duty risk and adversely impact the revenue collection. Physical investigation by the customs administrations is impractical due to the substantial quantity of declarations. This paper provides an automatic approach by harnessing the power of machine learning techniques to relief the burden of customs targeting officers. We introduced a novel model based on the off-the-shelf embedding encoder to identify the correctness of HS Code without any human effort. Determining whether the HS Code is correctly matched with commodity description is a classification task, so the labelled data is typically required. However, the lack of gold standard labelled data sets in customs domain limits the development of supervised-based approach. Our model is developed by the unsupervised mechanism and trained on the unlabelled historical declaration records, which is robust and able to be smoothly adapted by the different customs administrations. Rather than typically classifying whether the HS Code is correct or not, our model predicts the score to indicate the degree of the HS Code being correct. We have evaluated our proposed model on the ground-truth data set provided by Dutch customs officers. Results show promising performance of 71% overall accuracy.

https://cordis.europa.eu/project/id/786748/results
https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5f1716216&appId=PPGMS

eCommerce, Brexit, new safety and security concerns are only a few examples of the challenges that government organisations, in particular customs administrations, face today when controlling goods crossing borders. To deal with the enormous volumes of trade customs administrations rely more and more on information technology (IT) and risk assessment, and are starting to explore the possibilities that data analytics (DA) can offer to support their supervision tasks. Driven by customs as our empirical domain, we explore the use of DA to support the supervision role of government. Although data analytics is considered to be a technological breakthrough, there is so far only a limited understanding of how governments can translate this potential into actual value and what are barriers and trade-offs that need to be overcome to lead to value realisation. The main question that we explore in this paper is: How to identify the value of DA in a government supervision context, and what are barriers and trade-offs to be considered and overcome in order to realise this value? Building on leading models from the information system (IS) literature, and by using case studies from the customs domain, we developed the Value of Data Analytics in Government Supervision (VDAGS) framework. The framework can help managers and policy-makers to gain a better understanding of the benefits and trade-offs of using DA when developing DA strategies or when embarking on new DA projects. Future research can examine the applicability of the VDAGS framework in other domains of government supervision.

Big data and analytics have received a lot of attention in e-government research over the last decade and practitioners and researchers are looking into the transformative power of this technology to create for example competitive advantage, and increase transparency. Recent research points out that while parties are aware of the transformative power of this technology, understanding the value that this technology can bring for their specific organizations still remains a challenge. Data analytics is in particular interesting to support supervision tasks of governments. Here we take the customs supervision as a typical example where data analytics is used to support government in its supervision role. The main question addressed in this paper is: How to understand the value of big data analytics for government supervision? To address this question this research builds upon a case study where big data analytics solutions are developed and piloted as part of the PROFILE EU-funded research project. We adapt and utilize a recently published integrated model of big data value realization of Günther et al. [5] as a conceptual lens to structure the case findings. As a result we develop a more detailed model for analyzing value of big data specifically in the context of customs as an example of a specific domain of government supervision. This research contributes to the eGovernment literature on articulating value from big data analytics, particularly focusing on the role of government supervision.