Research on financial risk assessment algorithm based on graph neural network
Haotian Sun · 2024
With the rapid development of financial technology and the advent of the era of big data, financial risk assessment has become an important means to ensure financial security and promote the steady development of financial markets. However, the traditional financial risk assessment methods are often limited by data sparsity, complex correlation and other problems, and it is difficult to effectively deal with the complex and changeable financial market environment. The purpose of this study is to explore the financial risk assessment algorithm based on graph neural network in order to improve the accuracy and efficiency of risk assessment. By constructing a comprehensive model of fusion graph neural network and a variety of risk assessment indicators, we conduct a comprehensive quantitative analysis of multi-level risks of financial institutions, financial markets and financial products. This study not only overcomes the limitations of traditional methods, but also more effectively reveals the network structure and dynamic evolution process of financial risk transmission, and provides accurate risk early warning and decision support for financial institutions. The experimental results show that the financial risk assessment algorithm based on graph neural network is superior to the existing methods in both accuracy and stability, and provides a new perspective and solution path for the intelligent management and control of financial risks.