Given the huge volume of cross-border flows, effective and efficient control of trade becomes more crucial in protecting people and society from illicit trade. 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 declaration dataset to facilitate the collaboration between domain experts in customs administrations and researchers from diverse domains, such as data science and machine learning. The dataset contains 54,000 artificially generated trades with 22 key attributes, and it is synthesized with conditional tabular GAN 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. The fabrication step minimizes the possible identity risk which may exist in trade statistics. Second, the published data follow a similar distribution to the source data so that it can be used in various downstream tasks. Hence, our dataset can be used as a benchmark for testing the performance of any classification algorithm. 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 fraud.

翻译：鉴于跨境流动的巨大规模，有效且高效的贸易管控对于保护人民和社会免受非法贸易侵害愈发重要。然而，交易级贸易数据集的有限可获取性阻碍了开放研究的进展，许多海关管理部门尚未从基于数据的风险管理的最新进展中获益。本文引入了一个进口报关数据集，以促进海关管理部门领域专家与数据科学、机器学习等不同领域研究者之间的协作。该数据集包含54,000条人工生成的贸易记录，涵盖22个关键属性，并通过条件表格生成对抗网络在保持特征相关性的同时进行合成。合成数据具有多重优势：首先，发布该数据集不受禁止披露原始进口数据的限制，其合成过程最大限度地降低了贸易统计中可能存在的身份识别风险；其次，发布数据与源数据分布相似，可用于多种下游任务。因此，本数据集可作为测试任何分类算法性能的基准。通过提供数据及其生成过程，我们为欺诈检测任务开放了基准代码，实验证明更先进的算法能够更有效地识别欺诈行为。