Research on Financial Early Warning Indicator System Constructed by LSTM Recursive Algorithm in Enterprise Capital Chain Risk Control Empowered by Digital Transformation
Bibliographic record
Abstract
In order to improve the accuracy of enterprise financial risk early warning and realize the risk control of enterprise capital chain under digital transformation, this paper adopts the Long Short-Term Memory (LSTM) neural network algorithm to establish the enterprise financial risk early warning model.First analyze the enterprise financial risk early warning indicators, use factor analysis for indicator screening, determine the indicator weights through the Delphi method and the improved hierarchical analysis method, and select the indicators with high importance to construct the enterprise financial risk early warning feature samples.Then after LSTM neural network training, the enterprise financial risk early warning model is obtained, and the model is evaluated for performance and practical use.The experiment proves that the accuracy of the LSTM neural network model on the training set and test set is 91.48% and 88.62% respectively, which indicates that the model can effectively predict the enterprise financial risk.By comparing with the commonly used enterprise financial risk warning algorithms, the algorithm in this paper has the highest warning accuracy, shorter prediction time, and better warning performance in dealing with large-scale enterprise samples.This study provides an effective financial risk early warning method for enterprises, which can help them better carry out capital chain wind control in the process of digital transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".