Author(s)

Xu Zhang

Corresponding Author

Xu Zhang

ABSTRACT

As an important means of promoting sustainable development, the effective management of green finance product risks is of great significance in promoting the coordinated development of the economy and the environment. Due to the high uncertainty and complexity of green financial products, traditional risk prediction methods have obvious limitations in dealing with these characteristics. With the rapid development of machine learning technology, its application in financial risk prediction shows strong potential and advantages. This paper focuses on the risk prediction problem of green financial products and constructs a systematic risk prediction model framework based on machine learning technology. Through data preprocessing, model selection and construction, as well as performance evaluation and optimization, this paper proposes a risk management scheme that can improve prediction accuracy and reliability. Through empirical analysis, the applicability and advantages of the proposed model in different scenarios are verified. The research in this paper not only provides a new technical path for the risk management of green financial products, but also provides a reference for the expansion of the application of machine learning in the field of sustainable finance.

KEYWORDS

green finance, risk prediction, machine learning, model optimization, sustainability