CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks

• URL: http://arxiv.org/abs/2402.14708v2
• Date: Wed, 27 Nov 2024 12:15:06 GMT
• Title: CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
• Authors: Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang,
• Abstract summary: Credit card fraud poses a significant threat to the economy.<n>This paper introduces a novel method for credit card fraud detection, the textbfunderlineCausal textbfunderlineTemporal textbfunderlineGraph textbfunderlineNeural textbfNetwork (CaT-GNN)<n>CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model's robustness and interpretability.
• Score: 13.924812479551326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{\underline{Ca}}usal \textbf{\underline{T}}emporal \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{N}etwork (CaT-GNN), which leverages causal invariant learning to reveal inherent correlations within transaction data. By decomposing the problem into discovery and intervention phases, CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model's robustness and interpretability. CaT-GNN consists of two key components: Causal-Inspector and Causal-Intervener. The Causal-Inspector utilizes attention weights in the temporal attention mechanism to identify causal and environment nodes without introducing additional parameters. Subsequently, the Causal-Intervener performs a causal mixup enhancement on environment nodes based on the set of nodes. Evaluated on three datasets, including a private financial dataset and two public datasets, CaT-GNN demonstrates superior performance over existing state-of-the-art methods. Our findings highlight the potential of integrating causal reasoning with graph neural networks to improve fraud detection capabilities in financial transactions.

Related papers

• Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention [2.7002727600755883]
  This study proposes a credit card fraud detection method based on Heterogeneous Graph Neural Network (HGNN) By leveraging graph neural networks, the model captures higher-order transaction relationships. The model achieves notable improvements in both accuracy and OC-ROC.
  arXiv  Detail & Related papers  (2025-04-11T00:53:53Z)
• Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks [0.0]
  CgNN is a novel approach to mitigate hidden confounder bias and improve causal effect estimation. Our results demonstrate that CgNN effectively mitigates hidden confounder bias and offers a robust GNN-driven IV framework for causal inference in complex network data.
  arXiv  Detail & Related papers  (2024-09-13T05:39:00Z)
• Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks [50.87615167799367]
  We certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph.<n>Our framework provides fundamental insights into the role of graph structure and its connectivity on the worst-case behavior of convolution-based and PageRank-based GNNs.
  arXiv  Detail & Related papers  (2024-07-15T16:12:51Z)
• 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)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer [5.043422340181098]
  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.
  arXiv  Detail & Related papers  (2023-07-11T08:56:53Z)
• 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)
• The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection [17.254383007779616]
  We argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute. We propose a simple and effective method that uses the attention mechanism to adaptively fuse two views. Our model can significantly outperform stateof-the-art baselines on real-world fraud detection datasets.
  arXiv  Detail & Related papers  (2022-10-22T08:21:49Z)
• 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)
• Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method [114.48708191371524]
  Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. This work focuses on the local structure discrepancy of testing nodes, which has received little scrutiny. We analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.
  arXiv  Detail & Related papers  (2020-10-22T15:21:47Z)
• 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)
• Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection [78.88163190021798]
  We introduce a new GNN framework, $mathsfGraphConsis$, to tackle the inconsistency problem. Empirical analysis on four datasets indicates the inconsistency problem is crucial in a fraud detection task. We also released a GNN-based fraud detection toolbox with implementations of SOTA models.
  arXiv  Detail & Related papers  (2020-05-01T21:43:58Z)

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.