Scalable and interpretable fraud detection in FinTech: Evidence from a hybrid TabNet–Graph neural network framework

Résumé fourni par la source

This study proposes a fraud detection framework for FinTech that integrates tabular deep learning with relational graph reasoning. We address a key limitation of state-of-the-art tabular fraud models: their assumption that transactions are independent, despite evidence that modern fraud often arises from coordinated infrastructure reuse (e.g., shared devices, email domains, accounts, and addresses). Using the IEEE-CIS Fraud Detection dataset (≈590,540 e-commerce transactions), we develop a hybrid architecture that combines TabNet, chosen for its built-in interpretability on tabular data, with a GraphSAGE encoder trained on a leakage-free heterogeneous entity graph constructed strictly from historical training transactions. Unseen entities in validation and test splits are mapped to dedicated unknown nodes, mirroring deployment conditions and preventing optimistic bias. Class imbalance is handled using square-root class weighting, avoiding synthetic oversampling methods that could distort real infrastructure topology. Empirically, the standalone TabNet baseline achieves ROC–AUC = 0.759 and PR–AUC = 0.149 on a forward-in-time test split. Incorporating relational learning via the proposed TabNet–GraphSAGE fusion improves performance to ROC–AUC = 0.791 and PR–AUC = 0.206, representing a 38% relative gain in PR–AUC and substantially enhancing minority fraud retrieval. The graph encoder alone attains meaningful predictive power, and ablation results confirm that the largest gains emerge from learned message passing and fusion, indicating complementarity between behavioural and infrastructural representations. Overall, results demonstrate that fraud detection is fundamentally a hybrid problem: combining local transaction anomalies with global relational structure yields more operationally relevant ranking quality and improved robustness in high-volume FinTech environments.

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