Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the intentional manipulation of goods declarations to avoid the taxes and duties. With limited manpower, the custom offices can only undertake manual inspection of a limited number of declarations. This necessitates the need for automating the customs fraud detection by machine learning (ML) techniques. Due the limited manual inspection for labeling the new-incoming declarations, the ML approach should have robust performance subject to the scarcity of labeled data. However, current approaches for customs fraud detection are not well suited and designed for this real-world setting. In this work, we propose $\textbf{GraphFC}$ ($\textbf{Graph}$ neural networks for $\textbf{C}$ustoms $\textbf{F}$raud), a model-agnostic, domain-specific, semi-supervised graph neural network based customs fraud detection algorithm that has strong semi-supervised and inductive capabilities. With upto 252% relative increase in recall over the present state-of-the-art, extensive experimentation on real customs data from customs administrations of three different countries demonstrate that GraphFC consistently outperforms various baselines and the present state-of-art by a large margin.

翻译：全球海关官员每天面临海量交易。随着互联互通与全球化的发展，海关交易量逐年递增。海关交易伴随的欺诈行为——即故意篡改货物申报以逃避税费的现象日益突出。受限于人力不足，海关部门仅能对有限数量的申报进行人工查验。这使得采用机器学习技术实现海关欺诈检测自动化成为必要。由于可用于标注新到申报的人工查验受限，机器学习方法需在标注数据稀缺的情况下仍保持稳健性能。然而，现有海关欺诈检测方法未能针对这一实际场景进行优化设计。本文提出 $\textbf{GraphFC}$（$\textbf{Graph}$ neural networks for $\textbf{C}$ustoms $\textbf{F}$raud）——一种模型无关、领域特化的半监督图神经网络海关欺诈检测算法，具备强大的半监督与归纳学习能力。基于来自三个不同国家海关管理机构的真实海关数据的广泛实验表明，GraphFC在召回率上较现有最先进方法提升高达252%，且以显著优势持续超越各类基线模型及当前最优方法。