Financial Technology Risk Supervision Method Based on Big Data
- DOI
- 10.2991/978-94-6463-064-0_86How to use a DOI?
- Keywords
- Financial technology; Risk supervision; Accuracy
- Abstract
In order to promote the stable development of economy and avoid the risk of economic recession, the risk supervision of financial technology is very important. The traditional financial technology risk supervision methods are not comprehensive in risk early warning, and the accuracy of financial risk supervision is not high, which can not effectively supervise the financial technology risk. Therefore, this paper puts forward the supervision method of financial technology risk based on big data. This method obtains the performance index of financial risk by constructing the early warning system of financial technology risk; LSSVM financial technology risk calculation based on big data can minimize and maximize the structural risk. According to the structural risk, improve the classification and prevention of financial technology risk, and finally achieve the effective supervision of financial technology risk. Experiments show that: compared with the traditional methods, this risk supervision method has higher risk supervision efficiency, and can more effectively supervise the risks brought by financial technology.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - He Zhang PY - 2022 DA - 2022/12/27 TI - Financial Technology Risk Supervision Method Based on Big Data BT - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) PB - Atlantis Press SP - 843 EP - 851 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-064-0_86 DO - 10.2991/978-94-6463-064-0_86 ID - Zhang2022 ER -