A novel framework for Graph Fraud Detection with Grouped attribute completion and Confidence-aware Contrastive learning, named GFD-GC, which introduces a confidence-aware supervised contrastive learning strategy to augment scarce labeled fraud nodes with high confidence pseudo-fraud nodes, which enhances the compactness of fraud representations and their separability from non-fraud nodes.
Abstract
Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection. However, the practical performance of existing GNN-based detectors is severely hindered by incomplete node attributes and extreme class imbalance within graphs. To mitigate these limitations, this paper proposes a novel framework for Graph Fraud Detection with Grouped attribute completion and Confidence-aware Contrastive learning, named GFD-GC. Specifically, it first imitates heterogeneous neighborhood structures to implement group-wise aggregation, which obtains informative complete node features by capturing fine-grained graph contextual patterns. Further, it introduces a confidence-aware supervised contrastive learning strategy to augment scarce labeled fraud nodes with high confidence pseudo-fraud nodes, which enhances the compactness of fraud representations and their separability from non-fraud nodes. Extensive experiments demonstrate the superiority of the proposed GFD-GC over state-of-the-art baselines on the graph fraud detection task, thereby providing an effective solution for real-world fraud scenarios.
Transaction fraud detection on digital payment platforms poses three inter-twined challenges: sparse supervision, severe class imbalance, and complex relational dependencies among accounts and transaction events. This paper studies fraud detection on a heterogeneous transaction graph constructed from the public PaySim simulator. To this end, we propose ProtoHGC, a framework that integrates heterogeneous graph contrastive pre-training with prototype-regularized classification. ProtoHGC employs a multi-scale en-coder that aggregates first-order neighborhood features and second-order meta-path-based context, coupled with node-level and subgraph-level con-trastive objectives to enhance representation quality under limited supervi-sion. Building on the learned embeddings, class-specific prototypes for normal and fraudulent behaviors enforce intra-class compactness and inter-class separation. Experiments on PaySim demonstrate that ProtoHGC con-sistently outperforms GCN, GAT, GraphSAGE, and MLP baselines across all tested hidden dimensions in terms of AUC-ROC, PR-AUC, accuracy, re-call, and F1-score. We explicitly note that the current evaluation is limited to a single synthetic dataset; few-shot adaptation, cross-domain generaliza-tion, and multimedia-specific fraud scenarios remain unvalidated.
Qi Hu· Poster Volume 0008 The 2026...· 0 citations
HND-GFD is proposed, a novel fraud detection framework integrating high-order hypergraph modeling and dynamic neighborhood aggregation that consistently outperforms state-of-the-art baselines and adaptively aggregates neighborhood information from benign and fraudulent perspectives.
Junzheng Li, Hongtao Yu, Ruiyang Huang et al.· Journal of King Saud Univers...· 0 citations
Financial fraud is becoming increasingly complex alongside the growth of digital financial ecosystems, causing conventional fraud detection approaches based on handcrafted rules and tabular machine learning models to face limitations in identifying coordinated fraudulent activities. This study aims to analyze the application of Graph Neural Networks (GNNs) for financial fraud detection by utilizing transaction attributes and graph structural information. A quantitative experimental approach was conducted using the publicly available Elliptic Bitcoin Transaction Dataset, where transactions were modeled as graph nodes and relationships between transactions as graph edges. The proposed method implemented a Graph Attention Network (GAT) and compared its performance with Graph Convolutional Networks (GCN), GraphSAGE, and Graph Isomorphism Networks (GIN). Data preprocessing involved feature normalization, graph construction, and supervised learning on labeled transaction data. Model evaluation was performed using Accuracy, Precision, Recall, F1-score, ROC-AUC, and PR-AUC metrics. The results show that attention-based graph learning provides superior performance in detecting fraudulent transactions by assigning adaptive importance to relevant neighboring nodes during information propagation. Furthermore, graph representation learning effectively captures interconnected fraud patterns that are difficult to identify using conventional machine learning methods. These findings highlight the potential of GNNs as an intelligent approach for improving financial fraud detection and supporting risk management systems in increasingly complex digital financial environments.
E. Setiawati· JURNAL ILMIAH SAINS TEKNOLOG...· 5 citations· ⚡1
Financial fraud detection in transaction networks is challenging due to evolving attack strategies, complex relational structures, and extreme class imbalance. We propose a hybrid deep learning model that fuses Graph Convolutional Neural Networks (GCNNs) with bidirectional LSTMs enhanced by temporal attention, enabling joint modeling of structural dependencies and sequential transaction patterns. To improve class discrimination, the framework incorporates Triplet Loss, enhancing prior contrastive approaches, which enhances embedding separability under highly imbalanced conditions. Furthermore, we introduce graph augmentation strategies, including edge perturbation, node feature masking, and adaptive subgraph sampling, to increase robustness against noise and incomplete networks. Experiments on the IEEE-CIS and synthetic datasets with varied patterns demonstrate that the proposed model achieves up to 6.2% improvement in F1-score with a modest precision-recall trade-off favoring high-recall scenarios and 12% higher recall compared to strong baselines. Ablation studies confirm the complementary roles of the graph, temporal, and metric learning components. These consistent improvements, demonstrate that incorporating Triplet Loss within graph–temporal modeling provides a principled and effective approach to imbalanced fraud detection, establishing our model as a scalable and robust solution for fraud detection in financial transaction networks.
Ofonime Dominic Okon, Imo Enang, B. Stephen et al.· E3S Web of Conferences· 0 citations
Financial fraud now spreads through coordinated accounts whose risk is visible mainly in how they connect rather than in any single record. Tree based classifiers that score transactions in isolation miss this relational signal. This paper reviews and consolidates how graph neural networks (GNNs) cast fraud and anomaly detection as node and edge classification over transaction graphs, where message passing propagates evidence among accounts, devices, and merchants. We describe the main architectures used in practice, namely graph convolutional networks, GraphSAGE, and graph attention networks, and explain how each aggregates neighbourhood information. Two problems dominate the fraud setting: extreme class imbalance, since genuine fraud is rare, and camouflage, where fraudsters attach to honest neighbours to dilute their signature. We summarise label aware and neighbour selecting designs such as CARE-GNN and PC-GNN that address both. Heterogeneous and temporal graph variants are discussed for settings with several node types and evolving relations, alongside sampling methods that keep training tractable on large graphs. Using illustrative experiments patterned on public benchmarks (Elliptic, Yelp and Amazon review fraud, and IEEE-CIS), we report AUC, AUPRC, F1, and recall at k, and show that imbalance aware GNNs improve minority recall over a gradient boosted baseline. We close with notes on explainability and the engineering needed for production scoring.
Raji N· Eduschool International Jour...· 0 citations
Graph topology and model architecture are routinely co-designed in GNN-based fraud detection, making it impossible to attribute performance gains to either component. We address this by fixing the training loop, features, and evaluation protocol while independently varying the graph construction strategy and GNN architecture. Three strategies are evaluated: multi-relation temporal, hybrid structural similarity, and intra-group, each evaluated across three GNN architectures (GATv2, GCN, and GraphSAGE). A feature-identical MLP with no graph structure serves as an empirical anchor. On the Sparkov dataset, all three GNN strategies exceed the MLP by 5.0–9.7 F1 points, confirming that topology contributes genuine discriminative value when per-cardholder histories are dense. On the IBM dataset, the intra-group strategy collapses 6.9 F1 points below the MLP, a consequence of near-empty neighbourhoods (mean degree 1.1) following down-sampling, while multi-relation retains ranking advantages in AUC (Area Under the Curve; 0.985) and Average Precision (0.908) despite marginal F1 parity. Across both datasets, multi-relation temporal construction is the highest-performing strategy, achieving F1 0.908 and AUC 0.994 on Sparkov and F1 0.831 on IBM, though this ranking is not entirely architecture-independent: GraphSAGE narrowly inverts the multi-relation/intra-group order on Sparkov. These results suggest that graph construction quality, rather than mere graph presence, matters more for GNN performance than connectivity alone under the conditions evaluated here.
Roya Amiri, Sardar F. Jaf· Big Data and Cognitive Compu...· 0 citations
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