Research on Enterprise Financial Risk Prediction Based on LSTM
Yan Mu · 2023
This paper embarks on the exploration of leveraging the Long Short-Term Memory (LSTM) neural networks for enterprise financial risk prediction, aiming to bolster the precision and dependability of financial decision-making. Setting the stage, we delve into the backdrop and paramountcy of enterprise financial risks, underlining the indispensability of swift and precise financial risk foretelling for the robustness and longevity of businesses. Diving deeper, we unpack the mechanics of LSTM neural networks, highlighting their unparalleled prowess in dissecting time series data. Methodologically, we channel enterprise financial data into the LSTM model, training it to pinpoint impending financial peril. By harnessing historical financial datasets, we construct and validate our LSTM-based model. Empirically speaking, our model outperformed traditional forecasting techniques by a considerable margin, boasting an accuracy rate of 92% in detecting financial anomalies. Moreover, in a head-to-head comparison with conventional financial risk prediction methodologies, the LSTM model emerged as the clear frontrunner, underscoring its superiority in both accuracy and reliability.