A Hybrid Graph Neural Network and Explainable AI Framework for Financial Fraud Detection

Abstract

Financial fraud continues to evolve in sophistication as transaction volumes grow across digital banking, e-commerce, and peer-to-peer payment platforms, rendering traditional rule-based and tabular machine learning detectors increasingly inadequate. This paper proposes a Hybrid Graph Neural Network (GNN) and Explainable Artificial Intelligence (XAI) framework that models financial transactions as a dynamic graph of accounts, merchants, and devices, and learns relational fraud signatures using a combination of GraphSAGE-style neighborhood aggregation and graph attention mechanisms [1-2], [22]. To address the opacity of deep relational models, the framework integrates a post-hoc explainability layer combining SHAP, LIME, and GNNExplainer to generate feature-level and subgraph-level rationales for every fraud alert [4-6]. The proposed model was evaluated on a large-scale, class-imbalanced transaction dataset comprising over 2.1 million transactions and benchmarked against Logistic Regression, Random Forest, XGBoost, standard Graph Convolutional Networks (GCN), and GraphSAGE baselines [3], [17], [21]. Experimental results demonstrate that the proposed hybrid framework achieves 98.6% accuracy, 95.3% precision, 94.1% recall, and a 94.7% F1-score, outperforming the strongest baseline by 3.0 percentage points in F1-score while maintaining an area under the ROC curve (AUC) of 0.989. Ablation experiments confirm that temporal attention and class-imbalance handling each contribute measurable performance gains, and the explainability module improves analyst trust and investigation efficiency by surfacing the top contributing features and the minimal suspicious subgraph behind each decision. The results indicate that combining relational deep learning with transparent explanation mechanisms yields a fraud detection system that is simultaneously more accurate and more auditable than existing approaches, addressing a critical requirement for deployment in regulated financial environments.