摘要
近十年来，数字技术在全球金融行业加速渗透并呈现出持续深化态势，目前
大数据、人工智能、云计算、区块链等许多数字技术已被广泛应用到金融领域中
去，这使得数字技术与金融业务相互交织、交互作用。另一方面，金融监管也面
临日益严峻的挑战，传统金融监管手段愈发不能适应新兴金融业态发展形势，“金
融科技”丰富了监管手段，运用科技力量提升金融监管水平已是大势所趋。为此
本文围绕金融科技如何作用于监管领域并在何种程度上赋能监管展开研究。目前
金融业内科学技术全方位渗透，以大数据、人工智能、云计算等为代表的几项主
要技术在助力金融监管方面已经崭露头角，这为本文的研究提供了理论、案例和
数据等支撑。本文从提高我国金融监管水平的角度出发，评估金融科技对金融监
管的赋能与促推效果，具有一定的研究价值。
支持向量机属于机器学习和数据挖掘的范畴，该理论及模型具有强大的训练
学习和数据预测的能力。本文主要是借助支持向量机的 SVR模型，分别建立对
应金融科技和金融监管的相关指标体系作为模型的输入、输出向量，在用样本数
据对模型进行重复训练和测试后，模拟预测了在金融科技赋能背景下我国金融监
管有效性指数在未来一段时间的变化趋势。实证分析的结果表明，金融科技的持
续纵深发展，将在未来一定时期内推动我国金融监管有效性水平加速提升。
由此本文得到基本结论：（1）SVR回归模型本身获得了较好的预测效果，
预测数据的准确率较高；（2）2020年后我国金融监管有效性水平开始进入快速
提升时期，未来我国金融监管有效性水平将加速提升。
综上所述，本文提出的 SVR模型在预测结果上是令人满意的，并且具有一
定的推广性。通过运用支持向量机模型进行数据评估与预测这种模式，可以对现
有的金融监管的大环境和科技赋能监管的作用机制进行深入分析。最后，本文根
据结论提出了促进监管技术发展以及深化监管技术应用实践相关建议，并做出未
来展望。
关键词：金融科技；金融监管；支持向量机；SVR
III

Abstract
In the past decade, digital technology has accelerated penetration in the global
financial industry and has shown a continuous deepening trend. At present, big data,
artificial intelligence, cloud computing, blockchain and many other digital
technologies have been widely used in the financial field, which makes digital
technology and financial business intertwine and interact with each other. On the
other hand, financial supervision is also facing increasingly serious challenges, and
traditional financial supervision methods are increasingly unable to adapt to the
development of the new financial industry, "financial technology" has enriched the
means of supervision, the use of technology to enhance the level of financial
supervision is the trend. To this end, this paper focuses on how financial technology
can be used in the regulatory field and to what extent it can empower regulation. At
present, technology in the financial industry is penetrating in all aspects, and several
major technologies represented by big data, artificial intelligence, cloud computing,
etc. have emerged to help financial regulation, which provides theoretical, case and
data support for the study of this paper. This paper is of research value in assessing
the enabling and facilitating effects of financial technology on financial regulation
from the perspective of improving financial regulation in China.
Support vector machine belongs to the category of machine learning and data
mining, and the theory and model have powerful training learning and data prediction
capabilities. In this paper, mainly with the support vector machine SVR model, we
establish the relevant index systems corresponding to fintech and financial regulation
as the input and output vectors of the model respectively. After repeated training and
testing of the model with sample data, we simulate and predict the change trend of
China's financial regulation effectiveness index in a period of time in the future under
the background of fintech empowerment. The results of the empirical analysis show
that the continuous longitudinal development of fintech will promote the accelerated
improvement of the level of financial regulatory effectiveness in China in a certain
period of time in the future.
From this paper, we get the basic conclusions that (1) the SVR regression model
II

itself obtains a good prediction effect and the accuracy rate of the prediction data is
high; (2) the level of financial regulatory effectiveness in China starts to enter a period
of rapid improvement after 2020, and the level of financial regulatory effectiveness in
China will accelerate in the future.
In summary, the SVR model proposed in this paper is satisfactory in terms of
prediction results and has certain generalizability. By using the support vector
machine model for data evaluation and prediction of this model, the existing general
environment of financial regulation and the mechanism of the role of
technology-enabled regulation can be analyzed in depth.Finally, based on the
conclusions, this paper puts forward recommendations related to promoting the
development of regulatory technology and deepening the practice of regulatory
technology application, and makes future prospects.
Key words: Fintech; Financial supervision; Support vector machine;SVR
III
。。。以下略