Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
Leveraging graph-based topological features and LSTM networks to outsmart modern financial fraud evasion tactics.
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 framewor...
ArXiv CS.AI
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This hybrid approach bridges the gap between temporal transaction monitoring and structural pattern recognition to combat complex money laundering schemes. By integrating graph topology with deep learning, institutions can identify sophisticated fraud tactics that traditional isolated models consistently overlook.
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