Research on internal financial fraud identification model of enterprise based on ensemble learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, financial fraud cases have been on the rise, prompting numerous scholars to explore relevant fields and contribute significantly to the practical oversight of the economy. Integrated learning models have also gained widespread application in the realm of financial fraud detection, proving their efficacy in identification. This paper provides a summary of existing research and methodologies employed by scholars. After reviewing pertinent literature, the Logistic Regression model, a Single Decision Tree, Gradient Boosting Decision Trees, Random Forest model, XGBoost model, and LightGBM model were selected as candidate models for studying financial fraud detection. A comparative analysis of their respective identification accuracies was conducted. The research findings indicate that across the overall detection models, the identification rates of all models exceed 70%. Among these, the XGBoost model exhibits the best performance, achieving an identification accuracy of 87.77%. From the comparative results, it is evident that the accuracy of ensemble learning models generally surpasses that of traditional classification models and basic machine learning models, effectively enhancing the efficiency of financial fraud detection. Furthermore, in terms of identification speed, ensemble learning models demonstrate advantages such as shorter processing times and the ability to accommodate larger datasets.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it