Research on Financial Loan Default Prediction Based on Multi-Model Ensemble and Custom Thresholds
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
Loan defaults pose significant threats to financial institutions' financial stability and reputation. Although existing risk assessment models have addressed this issue to some extent, they exhibit significant limitations when dealing with large-scale, high-dimensional data. Therefore, developing an advanced model that can predict loan defaults with higher accuracy is crucial. This paper aims to optimize loan default prediction by combining innovative algorithms and models to enhance the risk management capabilities of financial institutions and reduce economic losses. This study proposes a loan default prediction model based on the LendingClub dataset. The model integrates multiple machine learning algorithms, including Logistic Regression, Random Forest, Gradient Boosting, LightGBM, and CatBoost, as well as ensemble learning methods, aiming to improve the prediction accuracy and stability of the model. Through a comprehensive analysis of the model's precision, recall, and custom evaluation metrics, this paper establishes an optimized comprehensive model, improving recall from 60% to 80% and precision from 28% to 29%. By optimizing thresholds, the model significantly enhances the identification of bad loans while balancing precision and recall, providing an effective solution for loan default prediction.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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 it