海关进口申报数据集
Customs Import Declaration Datasets
AI总结：
本文提出一个含54,000笔人工生成交易的海关进口申报数据集，利用条件表格GAN合成并保留相关特征，作为分类基准，并开放欺诈检测基线代码，实证显示先进算法更优。
摘要：
鉴于跨境流动的规模巨大，有效且高效地控制贸易对于保护人民和社会免受非法贸易侵害变得愈发关键。然而，交易级贸易数据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受益于基于数据的风险管理的最新进展。在本文中，我们引入了一个进口申报数据集，以促进海关管理部门领域专家与来自数据科学和机器学习等不同领域的研究人员之间的合作。该数据集包含54,000笔人工生成的交易，具有22个关键属性，并通过条件表格生成对抗网络（conditional tabular GAN）合成，同时保持了相关特征。合成数据具有若干优势。首先，发布该数据集不受禁止披露原始进口数据的限制。制造步骤最大限度地降低了贸易统计中可能存在的身份风险。其次，发布的数据与源数据分布相似，因此可用于各种下游任务。因此，我们的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，我们开放了欺诈检测任务的基线代码，因为我们实证表明，更先进的算法能够更好地检测欺诈。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
