Given the huge volume of cross-border flows, effective and efficient control of trades becomes more crucial in protecting people and society from illicit trades while facilitating legitimate trades. However, limited accessibility of the transaction-level trade datasets hinders the progress of open research, and lots of customs administrations have not benefited from the recent progress in data-based risk management. In this paper, we introduce an import declarations dataset to facilitate the collaboration between the domain experts in customs administrations and data science researchers. The dataset contains 54,000 artificially generated trades with 22 key attributes, and it is synthesized with CTGAN while maintaining correlated features. Synthetic data has several advantages. First, releasing the dataset is free from restrictions that do not allow disclosing the original import data. Second, the fabrication step minimizes the possible identity risk which may exist in trade statistics. Lastly, the published data follow a similar distribution to the source data so that it can be used in various downstream tasks. With the provision of data and its generation process, we open baseline codes for fraud detection tasks, as we empirically show that more advanced algorithms can better detect frauds.
翻译:鉴于跨界流动量巨大,有效、高效地控制贸易对于保护人民和社会免受非法贸易的影响,同时促进合法贸易,变得更加重要。然而,交易一级贸易数据集的可获取性有限,阻碍了公开研究的进展,而且许多海关行政部门没有从数据风险管理方面的最新进展中受益。在本文件中,我们引入进口申报数据集,以便利海关管理部门的域专家与数据科学研究人员之间的合作。数据集包含54 000个人工生成的贸易,具有22个关键属性,在保持相关特征的同时与CTGAN合成。合成数据有若干优点。首先,发布数据集不受不允许披露原始进口数据的限制。第二,制造步骤最大限度地减少了贸易统计中可能存在的身份风险。最后,公布的数据与源数据相似,以便用于各种下游任务。随着数据的提供及其生成过程,我们打开了欺诈侦查工作的基线代码,因为我们的经验显示,更先进的算法可以更好地检测欺诈。