Author(s)

Xu Zhang

Corresponding Author

Xu Zhang

Abstract

Traditional financial risk assessment methods have limitations in the face of big data and nonlinear relationships. Therefore, this study introduces ML (Machine Learning) technology in order to improve the accuracy and timeliness of risk assessment. In terms of methods, this paper first combs the theoretical basis of financial risk assessment and makes clear the application advantages of ML algorithm in financial risk assessment. Then, a multi-level and multi-module system architecture including data collection, preprocessing, model training and evaluation, risk early warning and user interaction is designed, and financial data is deeply studied and analyzed by ML algorithm. Through the system test, the stability and accuracy of the system are verified, and its application potential in actual financial risk assessment is demonstrated. This study provides a new, more accurate and efficient risk assessment and early warning tool for financial institutions and investors, which is helpful to improve the stability and transparency of the whole financial market.

Keywords

Financial risk assessment; ML algorithm; Early warning system; System architecture; Risk management