School of Economics and Management, Chifeng University, Inner Mongolia, Chifeng, China.
Comput Intell Neurosci. 2022 Sep 9;2022:7964123. doi: 10.1155/2022/7964123. eCollection 2022.
With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management.
随着经济的快速发展，金融业悄然成为产业的龙头，是推动经济发展的核心和命脉。与此同时，各种金融服务和管理平台也如雨后春笋般涌现。然而，金融服务和管理平台的出现并不能有效缓解当前的金融危机。面对日益复杂的金融风险，传统的金融服务和管理平台无法实现有效的信息共享，导致服务和管理效率持续低下，金融风险问题频繁发生。支持向量机是一种基于监督的数据分析算法，它可以实现数据共享，提高数据处理效率。本文首先重新调整了金融服务和管理平台的基础架构，以突破数据交互的障碍。在此基础上，本文进一步结合支持向量机算法，将其从二进制数据分类扩展到多元分类。最后，本文考虑支持向量机重新设计了金融服务和管理平台。经过一系列实验，可以发现基于支持向量机算法的金融服务和管理平台可以降低 17.2%的金融风险，提高 30.2%的金融服务水平，并提高 45.2%的金融综合服务水平。同时，由于信息共享和交互，金融服务和管理平台可以有效地预测金融风险，预测的准确率基本达到 78.9%。这表明，考虑支持向量机算法的金融服务和管理平台可以有效地防范金融风险，提高金融服务和管理的效率。
