用生成对抗网络解决金融监管数据不平衡问题，提升高风险事件预测准确率。
Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision
- 用GAN生成与高风险事件特征相似的合成数据以平衡样本。
- 相比传统方法，模型在识别少数风险事件上的准确率显著提升。
- 适用于证券、银行等监管机构的实时风险监控场景。
本研究探讨了生成对抗网络在金融市场监管中的应用，重点解决数据不平衡问题以提升风险预测精度。由于金融数据中高风险事件（如市场操纵、系统性风险）发生频率较低，传统模型难以有效识别这些少数类样本。本文提出通过GAN生成与少数类事件特征相似的合成数据，以平衡数据集，从而提升模型在金融监管中的预测性能。实验表明，相较于传统的过采样和欠采样方法，GAN生成的数据在处理数据不平衡问题上具有明显优势，显著提高了模型对风险事件的识别准确率。该方法在美国证券交易委员会（SEC）、金融业监管局（FINRA）、联邦存款保险公司（FDIC）及美联储等监管机构中具有广泛的应用潜力。
原文摘要 · Abstract (English)
This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.
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