Exploring the Trade-off Between Accuracy and Transparency in Credit Risk Prediction Models
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates four widely used models for credit risk prediction, Logistic Regression, Random Forest, XGBoost, and LightGBM, focusing on their ability to detect loan defaults under imbalanced and complex data conditions. Model performance was assessed using confusion matrices and key metrics including recall, precision and F-scores and analyzing the metric that best aligns with the purpose of lending institutions to evaluate a model’s performance. This study analyzes the rationale for moving from Ordinary Least Squares (OLS) regression to logistic regression. The results indicate that while logistic regression provides transparency, it struggles with non-linear relationships and class imbalance. Random Forest, built on decision trees, improves stability but sacrifices interpretability. Two boosting methods, XGBoost and LightGBM, achieve superior predictive ability and efficiency with even lower transparency. Also introduces the evolution of decision trees into ensemble methods such as Random Forest, XGBoost, and LightGBM, and analyzes the structural differences of the decision trees employed within these models. Overall, the findings highlight the trade-off between ability to identify target customer of leading institutions and interpretability in credit risk modeling.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| 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