  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.
