Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer
- URL: http://arxiv.org/abs/2307.05121v1
- Date: Tue, 11 Jul 2023 08:56:53 GMT
- Title: Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer
- Authors: Yue Tian, Guanjun Liu
- Abstract summary: We propose a novel graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems.
Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework.
We introduce a transformer module to learn local and global information.
- Score: 5.043422340181098
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: How to obtain informative representations of transactions and then perform
the identification of fraudulent transactions is a crucial part of ensuring
financial security. Recent studies apply Graph Neural Networks (GNNs) to the
transaction fraud detection problem. Nevertheless, they encounter challenges in
effectively learning spatial-temporal information due to structural
limitations. Moreover, few prior GNN-based detectors have recognized the
significance of incorporating global information, which encompasses similar
behavioral patterns and offers valuable insights for discriminative
representation learning. Therefore, we propose a novel heterogeneous graph
neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for
transaction fraud detection problems. Specifically, we design a temporal
encoding strategy to capture temporal dependencies and incorporate it into the
graph neural network framework, enhancing spatial-temporal information modeling
and improving expressive ability. Furthermore, we introduce a transformer
module to learn local and global information. Pairwise node-node interactions
overcome the limitation of the GNN structure and build up the interactions with
the target node and long-distance ones. Experimental results on two financial
datasets compared to general GNN models and GNN-based fraud detectors
demonstrate that our proposed method STA-GT is effective on the transaction
fraud detection task.
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