Risk Early Warning of Enterprise Financial Management Risk Based on CNN and BiLSTM under Accounting Informatization
Bibliographic record
Abstract
Based on accounting informatization, this paper constructs a inancial risk prediction system by applying the CNNs (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory)-Attention model to accurately identify and classify various risk types in enterprise FM ( inancial management), and improve the accuracy and ef iciency of inancial risk prediction.CNN was used to extract local features in inancial data, BiLSTM was used to capture time dependencies, and inally the importance of inancial indicators was weighted and fused through the Attention mechanism.During the training process, the Adam optimizer and cross entropy loss function are used for optimization, and appropriate learning rates and training rounds are set to ensure the stability and performance of the model.The experimental results show that when the epochs is 50, the accuracy of risk classi ication is 98.9% and the loss value is 0.012.In the analysis of each data level, the average response time of the proposed system and the traditional system is 1.80s and 7.17s respectively.The system in this paper shows obvious advantages in response time and prediction accuracy.The response time is greatly shortened, and it can provide effective support in real-time decision-making.This paper model has signi icant application prospects in inancial risk prediction, and can provide enterprises with ef icient and accurate risk warnings, which has important theoretical signi icance and practical value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".