Explainable Graph Neural Network Framework for Digital Transaction Fraud Detection

Abstract

Detecting credit card fraud is a major financial security challenge. This is a result of shifting fraud patterns and extremely unbalanced transaction data. In order to enhance detection performance, this study presents a hybrid fraud detection framework that combines gradient boosting classification with graph-based representation learning. First, transaction features, including the Time attribute considered as a numerical variable, are preprocessed using standard scaling and Borderline-SMOTE to tackle class imbalance. A static k-nearest neighbor similarity graph is created from the scaled feature space. A Temporal Graph Attention Network (TGAT) architecture is used to learn distinct node embeddings from graph relationships, even without explicit temporal encoding. These embeddings are combined with the original transaction features and fed into an XGBoost classifier for final fraud prediction. Optimal decision thresholds are determined through precision-recall analysis. Additionally, SHAP-based explainable artificial intelligence is used to clarify global feature importance and local prediction behavior, which improves model transparency. Experimental evaluation with stratified cross-validation shows that the TGAT-XGBoost hybrid model performs strongly across Accuracy, Precision, Recall, F1-score, ROC-AUC, and PR-AUC metrics. This indicates its effectiveness for reliable and understandable credit card fraud detection.