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A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems
🤖AI Summary
Researchers propose a dual-path AI framework combining Variational Autoencoders and Wasserstein GANs for real-time fraud detection in banking systems. The system achieves sub-50ms detection latency while maintaining GDPR compliance through selective explainability mechanisms for high-uncertainty transactions.
Key Takeaways
- →New AI framework decouples real-time fraud detection from offline adversarial training to handle zero-day banking attacks.
- →System uses VAE for legitimate transaction pattern recognition with under 50ms inference latency.
- →Wasserstein GAN generates synthetic fraudulent scenarios to improve detection boundaries.
- →Gumbel-Softmax estimator addresses challenges with discrete banking data like merchant category codes.
- →Trigger-based SHAP explanations balance computational efficiency with regulatory compliance requirements.
#fraud-detection#banking-ai#vae#gan#fintech#real-time-ai#gdpr-compliance#anomaly-detection#machine-learning#financial-security
Read Original →via arXiv – CS AI
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