GraphFC: Customs Fraud Detection with Label Scarcity

Karandeep Singh, Yu Che Tsai, Cheng Te Li, Meeyoung Cha, Shou De Lin

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Customs officials across the world encounter huge volumes of transactions. Associated with customs transactions is customs fraud-the intentional manipulation of goods declarations to avoid taxes and duties. Due to limited manpower, the customs offices can only manually inspect a small number of declarations, necessitating the automation of customs fraud detection by machine learning (ML) techniques. The limited availability of manually inspected ground truth data makes it essential for the ML approach to generalize well on unseen data. However, current customs fraud detection models are not well suited or designed for this setting. In this work, we propose GraphFC (Graph neural networks for Customs Fraud), a model-agnostic, domain-specific, graph neural network based customs fraud detection model that is designed to work in a real-world setting with limited ground truth data. Extensive experimentation using real customs data from two countries demonstrates that GraphFC generalizes well over unseen data and outperforms various baselines and other models by a large margin.

原文English
主出版物標題CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
發行者Association for Computing Machinery
頁面4829-4835
頁數7
ISBN(電子)9798400701245
DOIs
出版狀態Published - 2023 10月 21
事件32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
持續時間: 2023 10月 212023 10月 25

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
國家/地區United Kingdom
城市Birmingham
期間23-10-2123-10-25

All Science Journal Classification (ASJC) codes

  • 一般商業,管理和會計
  • 一般決策科學

指紋

深入研究「GraphFC: Customs Fraud Detection with Label Scarcity」主題。共同形成了獨特的指紋。

引用此