GraphFC: Customs Fraud Detection with Label Scarcity

TL;DR

GraphFC is a semi-supervised graph neural network model designed to detect customs fraud effectively under label scarcity, significantly outperforming existing methods on real-world data from multiple countries.

Contribution

The paper introduces GraphFC, a novel domain-specific semi-supervised graph neural network for customs fraud detection, addressing label scarcity and demonstrating superior performance.

Findings

Up to 252% increase in recall over state-of-the-art methods

Consistent outperformance across data from three different countries

Effective semi-supervised and inductive capabilities

Abstract

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.