<ul><li>This study explores the application of generative adversarial networks in financial market supervision to address data imbalance and improve risk prediction accuracy.</li><li>Traditional models struggle to identify minority events in imbalanced financial market data.</li><li>The study proposes using GAN to generate synthetic data that resembles these minority events.</li><li>Experimental results indicate that GAN-generated data outperforms traditional oversampling and undersampling methods, showing potential for use in financial regulatory agencies.</li></ul>

Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision

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