Comparison of different individual credit risk assessment models
Bibliographic record
Abstract
Personal credit risk is increasing in the background of continuous expansion of bank credit business. Previous researchers use many algorithms of machine learning to assess personal credit risk, while these models differ in real scenarios and accuracy. Based on this situation, this research first analyzes the influencing factors of individual credit risk through searching data, and then shows the relationship between them in figures. Meanwhile, this research compares three machine learning models in the predicting the accuracy. These models are Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR). Learning from the previous studies on individual credit risk assessment model, this research compares the final average value of Area Under Curve (AUC), which is obtained by calculating AUC one and AUC two. The results show that XGBoost has better performance than the other models for a high AUC value. This research provides an idea for banks to select and individual credit risk models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".