Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
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...
| Authors | Mariam Zakaria Moussa Ali |
|---|---|
| Primary category | cs.AI |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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