Forgetting Prevention for Cross-regional Fraud Detection with Heterogeneous Trade Graph

TL;DR

This paper introduces HTG-CFD, a novel heterogeneous trade graph-based method that leverages continual learning to prevent knowledge forgetting in cross-regional fraud detection, improving performance across multiple scenarios.

Contribution

It proposes a new heterogeneous trade graph construction and a continual learning approach to prevent forgetting in cross-regional fraud detection with GNNs.

Findings

Enhanced cross-regional fraud detection performance

Significant reduction in knowledge forgetting

Improved single-regional detection accuracy

Abstract

With the booming growth of e-commerce, detecting financial fraud has become an urgent task to avoid transaction risks. Despite the successful applications of Graph Neural Networks (GNNs) in fraud detection, the existing solutions are only suitable for a narrow scope due to the limitation in data collection. Especially when expanding a business into new territory, e.g., new cities or new countries, developing a totally new model will bring the cost issue and result in forgetting previous knowledge. Moreover, recent works strive to devise GNNs to expose the implicit interactions behind financial transactions. However, most existing GNNs-based solutions concentrate on either homogeneous graphs or decomposing heterogeneous interactions into several homogeneous connections for convenience. To this end, this study proposes a novel solution based on heterogeneous trade graphs, namely HTG-CFD, to prevent knowledge forgetting of cross-regional fraud detection. In particular, the heterogeneous trade graph (HTG) is meticulously constructed from original transaction records to explore the complex semantics among different types of entities and relationships. And motivated by recent continual learning, we present a practical and task-oriented forgetting prevention method to alleviate knowledge forgetting in the context of cross-regional detection. Extensive experiments demonstrate that the proposed HTG-CFD not only promotes the performance in cross-regional scenarios but also significantly contributes to single-regional fraud detection.