Design of Financial Risk Assessment System for Listed Companies: A Perspective Based on Deep Learning

Financial risk assessment is very important for the development of enterprises. It can detect potential risks in advance, help enterprises to rationally plan funds, optimize resource allocation, reduce losses, and ensure stable operation. Aiming at the problems of traditional financial risk assessment methods relying on subjective experience and single data dimension, this study develops a deep learning-driven framework for evaluating financial risks in publicly traded firms. Drawing upon the historical financial data of A-share listed companies, this study constructs a risk assessment system through a neural network model based on deep learning, effectively captures the temporal correlation of financial indicators, and introduces interdisciplinary analysis methods to reduce the subjective bias in traditional assessment. The experimental results demonstrate that the MAE of the model is about 0.3, and the evaluation accuracy is 97%. The accuracy and stability of the risk classification show promising advantages in terms of the traditional methods. The research results provide a new technical path and theoretical support for data-driven enterprise risk quantitative management.