Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
The paper proposes FraudShield AI, a hybrid framework combining LSTM networks with hand-crafted graph topological features to address extreme data imbalance (0.13% fraud rate) and evolving adversarial evasion in financial fraud detection. By engineering network-centric features such as PageRank centrality, in-degree dynamics, and a custom flow ratio, the system shifts from isolated transaction analysis to network-level forensics. Focal loss handles class imbalance, and a dynamic thresholding mechanism improves resilience against low-value smurfing attacks. Experiments on the PaySim dataset show the hybrid model substantially outperforms Logistic Regression and XGBoost in precision, recall, and F1-score, especially on micro-transaction fraud patterns. An ablation study confirms the complementary contributions of temporal and topological components.
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[Submitted on 2 May 2026]
Title:Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
View a PDF of the paper titled Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience, by Mariam Zakaria Moussa Ali
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Abstract:Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.13% fraud rate) and evolving adversarial evasion tactics. This paper proposes FraudShield AI, a hybrid framework that integrates Long Short-Term Memory (LSTM) networks with hand-crafted Graph Topological Features to capture both temporal sequences and structural relational context. By engineering network-centric features including PageRank Centrality, In-Degree dynamics, and a custom Flow Ratio, the system shifts the detection paradigm from isolated transaction analysis to network-level forensics. A Focal Loss objective is used to address class imbalance, and a dynamic thresholding mechanism is introduced to improve resilience against low-value smurfing attacks. Experimental evaluation on the PaySim dataset shows that the proposed hybrid model substantially outperforms Logistic Regression and XGBoost baselines in Precision, Recall, and F1-Score, particularly on hard-to-detect micro-transaction fraud patterns. An ablation study confirms the complementary contribution of both the temporal and topological components.
Comments: 6 pages, 12 figures
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.19350 [cs.AI]
(or arXiv:2607.19350v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.19350
arXiv-issued DOI via DataCite
Submission history
From: Mariam Ali [view email] [v1] Sat, 2 May 2026 02:10:20 UTC (599 KB)
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