Research Article
Exploration on Financial Risk Management under Machine Learning Algorithms
@INPROCEEDINGS{10.4108/eai.17-11-2023.2342821, author={Yimei Cao and Di Zhao and Chaoying Xiao}, title={Exploration on Financial Risk Management under Machine Learning Algorithms}, proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India}, publisher={EAI}, proceedings_a={ICSETPSD}, year={2024}, month={1}, keywords={machine learning algorithms financial risks random forests principal component analysis risk warning}, doi={10.4108/eai.17-11-2023.2342821} }- Yimei Cao
Di Zhao
Chaoying Xiao
Year: 2024
Exploration on Financial Risk Management under Machine Learning Algorithms
ICSETPSD
EAI
DOI: 10.4108/eai.17-11-2023.2342821
Abstract
Financial crises have a cyclical nature. In the context of economic globalization and the new normal, trade, industry, financial markets, capital markets, foreign exchange markets, real estate, etc., among countries have become interconnected through multiple channels. At the same time, it also leads to a very complex transmission chain of financial crises. If a financial crisis occurs, it would have a significant negative impact on the country, society, and people's lives. On the basis of establishing a system of financial risk warning indicators, this article used principal component analysis to reduce the dimensionality of the selected economic and financial index data. The main factors affecting the system's financial risk were extracted, and the comprehensive indicator values obtained through K-means clustering analysis were used to divide the risk level, thereby determining whether the risk warning state has been reached. In the statistical results of financial risk using the random forest method, the probability of financial risk in 2019 was 19%; the probability of financial risk in 2020 was 8%, and the probability of financial risk in 2021 was 5%. By strengthening the identification and prevention of financial risks, this article can provide a more comprehensive and accurate monitoring and evaluation of financial risks.
