Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
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.
- ▪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.
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Computer Science > Artificial Intelligence arXiv:2607.19350 (cs) [Submitted on 2 May 2026] Title:Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience Authors:Mariam Zakaria Moussa Ali 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 View PDF HTML (experimental) 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.
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