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.

Related papers

• Financial Fraud Detection using Jump-Attentive Graph Neural Networks [0.0]
  A significant portion of the financial services sector employs various machine learning algorithms, such as XGBoost, Random Forest, and neural networks, to model transaction data. We propose a novel algorithm that employs an efficient neighborhood sampling method, effective for camouflage detection and preserving crucial feature information from non-similar nodes.
  arXiv  Detail & Related papers  (2024-11-07T05:12:51Z)
• Advanced Financial Fraud Detection Using GNN-CL Model [13.5240775562349]
  The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection. It combines the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity.
  arXiv  Detail & Related papers  (2024-07-09T03:59:06Z)
• Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data [0.0]
  This paper explores the application of Generative Adversarial Networks (GANs) in fraud detection. GANs have shown promise in modeling complex data distributions, making them effective tools for anomaly detection. The study demonstrates the potential of GANs in enhancing transaction security through deep learning techniques.
  arXiv  Detail & Related papers  (2024-02-15T09:48:20Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Deep Fraud Detection on Non-attributed Graph [61.636677596161235]
  Graph Neural Networks (GNNs) have shown solid performance on fraud detection. labeled data is scarce in large-scale industrial problems, especially for fraud detection. We propose a novel graph pre-training strategy to leverage more unlabeled data.
  arXiv  Detail & Related papers  (2021-10-04T03:42:09Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• How effective are Graph Neural Networks in Fraud Detection for Network Data? [0.0]
  Graph-based Neural Networks (GNNs) are recent models created for learning representations of nodes (and graphs) Financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes. We conduct experiments to evaluate existing techniques for detecting network fraud, considering the two previous challenges.
  arXiv  Detail & Related papers  (2021-05-30T15:17:13Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.