Year: 2025
Type: conference-paper
Abstract: With the deepening of economic globalization and the continuous progress of information technology, enterprises in different industries and fields need to establish a more systematic and comprehensive financial risk analysis and early warning mechanism to cope with the complex and changeable external environment and ensure the sustainable development of enterprises. However, the current enterprise financial risk early warning methods mostly rely on static quantitative analysis and linear assumptions, and there are obvious shortcomings in the effectiveness and adaptability of the analysis. In view of these problems, this paper will deeply explore the feasibility of deep learning and artificial neural network in this field, and propose an enterprise financial risk early warning model based on long-term and short-term memory network (LSTM) to meet the practical application needs of enterprises. Practice has proved that the whole model can automatically extract useful features from data and capture the dynamic changes in time series data, so as to effectively improve the accuracy and timeliness of early warning analysis, and then help enterprise management to find potential financial risks in time and ensure the financial security and sustainable development of enterprises.
Authors: Ying Liu, Tingting Fan
Institution: Chongqing College of Architecture and Technology
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4412822367