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https://doi.org/10.62836/jcmea.v6i1.0002
Wilson, A. (2026). Cross-Domain Graph Neural Network with SHAP-Based Interpretability for Financial Transaction Fraud Detection. Journal of Computational Methods in Engineering Applications, 6(1), 0002. https://doi.org/10.62836/jcmea.v6i1.0002
Volume 6, Issue 1, 2026

Cross-Domain Graph Neural Network with SHAP-Based Interpretability for Financial Transaction Fraud Detection

Financial transaction fraud continues to cause significant losses for institutions and individuals, and detecting it across different platforms and time periods remains a difficult problem. Most existing detection models treat transactions as independent records, missing the relational structure that fraud networks inherently exploit. High-performing models also tend to be opaque, which creates friction with regulatory requirements. In this paper, we propose a cross-domain Graph Neural Network (GNN) framework that combines Margin Disparity Discrepancy (MDD)-based domain adaptation with SHAP-based interpretability for financial transaction fraud detection. We construct a heterogeneous transaction graph encoding account behavior, merchant metadata, and transaction attributes as node and edge features, train a Relational GCN with MDD alignment to generalize across domains, and use DeepSHAP to explain individual predictions. On a cross-domain evaluation transferring from PaySim to the IEEE-CIS Fraud Detection dataset, the proposed framework achieves an F1-score of 0.8743 and AUC-ROC of 0.9412, substantially outperforming tabular and domain-agnostic GNN baselines. SHAP analysis points to transaction amount deviation, merchant fraud history, and nighttime activity as the strongest fraud signals—findings that map well to investigator intuition and are directly actionable.

financial transaction fraud detection graph neural network domain adaptation SHAP interpretable machine learning

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Supporting Agencies

  1. Funding: This research received no external funding.