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.

该论文介绍了一种合成的进口申报数据集，为海关部门中的领域专家和数据科学研究人员之间的合作提供便利并开放基准代码以优化欺诈检测。