用深度学习预测银行流动性比率，提升风控精度
Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
- 基于门控循环单元（GRU）自动挖掘历史数据模式
- 相比传统方法，平均绝对误差显著降低
- 适合金融机构与监管机构用于风险预警
在全球经济一体化和金融市场高度互联的背景下，金融机构面临前所未有的流动性风险挑战。本文提出一种基于门控循环单元（GRU）网络的流动性覆盖率（LCR）预测模型，利用深度学习技术自动学习历史数据中的复杂模式，实现对未来一段时间内LCR的精准预测。实验结果表明，相较于传统方法，该GRU模型在平均绝对误差（MAE）指标上表现出显著优势，验证了其更高的预测精度与鲁棒性。该模型不仅为金融机构提供了更可靠的流动性风险管理工具，也为监管机构制定更科学合理的政策提供支持，有助于提升整个金融系统的稳定性。
原文摘要 · Abstract (English)
With the global economic integration and the high interconnection of financial markets, financial institutions are facing unprecedented challenges, especially liquidity risk. This paper proposes a liquidity coverage ratio (LCR) prediction model based on the gated recurrent unit (GRU) network to help financial institutions manage their liquidity risk more effectively. By utilizing the GRU network in deep learning technology, the model can automatically learn complex patterns from historical data and accurately predict LCR for a period of time in the future. The experimental results show that compared with traditional methods, the GRU model proposed in this study shows significant advantages in mean absolute error (MAE), proving its higher accuracy and robustness. This not only provides financial institutions with a more reliable liquidity risk management tool but also provides support for regulators to formulate more scientific and reasonable policies, which helps to improve the stability of the entire financial system.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。