Researchers propose FraudShield AI, a hybrid LSTM-graph model for detecting financial fraud under extreme class imbalance
The framework combines temporal LSTM networks with graph topological features to improve detection of smurfing and layering tactics in synthetic transaction data.
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- A new arXiv preprint introduces FraudShield AI, a hybrid model combining LSTM networks and graph topological features to detect financial fraud.
- The framework addresses extreme class imbalance (0.13% fraud rate) and adversarial evasion tactics like smurfing and layering.
- Evaluated on the PaySim dataset, the model outperforms Logistic Regression and XGBoost baselines in Precision, Recall, and F1-Score.
- The approach shifts detection from isolated transactions to network-level forensics using features like PageRank Centrality and custom Flow Ratio.
Researchers from arXiv:2607.19350 propose FraudShield AI, a hybrid framework that integrates Long Short-Term Memory (LSTM) networks with hand-crafted graph topological features to detect sophisticated financial fraud patterns such as smurfing and layering.
The framework targets extreme class imbalance in fraud datasets, reporting a fraud rate of 0.13%, and adversarial evasion tactics that challenge traditional detection methods.
FraudShield AI introduces network-centric features including PageRank Centrality, In-Degree dynamics, and a custom Flow Ratio to shift detection from isolated transaction analysis to network-level forensics.
To address class imbalance, the model employs a Focal Loss objective, and a dynamic thresholding mechanism is used to improve resilience against low-value smurfing attacks.
Experimental evaluation on the PaySim dataset shows the proposed hybrid model substantially outperforms Logistic Regression and XGBoost baselines in Precision, Recall, and F1-Score, particularly for hard-to-detect micro-transaction fraud patterns.
An ablation study confirms the complementary contribution of both the temporal (LSTM) and topological (graph) components to overall performance.
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