机器学习解构区域金融风险防控研究进展

Progress on Machine Learning for Regional Financial Risk Prevention

区域金融风险防控(RFRP)无论在管理区域传统金融风险(TFR)还是坚守不发生区域金融系统风险(FSR)中都是不可或缺的。随着大数据规模的持续增长,金融风险形态变化的不确定性,传统计量方法模拟金融风险防控的效率、精度、应用等方面都面临着无法克服的困境。当下,越来越多的机器学习(ML)模拟RFRP防控的新方法和新技术受到研究者的重视。首先提出了RFRP防控新的科学分类和ML观念基础;其次总结了区域TFR防控的ML理论方法和应用技术,对各类代表性研究所论述区域TFR防控的关键逻辑、模型算法、学习效果进行了比对解析,对ML不同方法的优点、局限和传统场景进行了归类分析;然后梳理了区域FSR防控的ML理论方法和应用研究,对各类典型文献所解析区域FSR防控的关键脉络、ML算法、学习效果进行了对比研究,对ML不同模型的优势、缺陷和金融风险场景进行了阐述研究;最后提出了六个ML模拟RFRP防控的前景技术和新兴方向。

The regional financial risks prevention(RFRP) is indispensable in managing the regional traditional financial risks(TFR) or preventing the regional financial systemic risks(FSR). With the growing of big data and the uncertainty of financial risk types, traditional econometrics methods are facing insurmountable difficulties in terms of the efficiency, accuracy, and application of financial risk prevention modeling. Today, increasing future methods and technologies for machine learning(ML) for RFRP prevention have been paid much attention by researchers. A new scientific classification of RFRP prevention and conceptual basis of ML methods are firstly put forward.Secondly, this paper summarizes the ML methods and application of regional TFR prevention, compares their key logic, model algorithm and learning effect of the representative literature, and categorizes the advantages, limitations and traditional scenarios of ML methods. Thirdly, it combs the ML methods and application of regional FSR prevention, analyzes the key context, ML algorithm and learning effect of the seminal documents, and compares the benefits, disadvantages and financial risk contexts of ML methods. Finally, six promising technologies and emerging directions of ML methods for RFRP prevention are proposed.