Gradient Descent Decision Tree Algorithm and Nonlinear Programming for Credit Risk Assessment and Credit Strategy
Bibliographic record
Abstract
This research aimed to develop a scientific and accurate credit risk assessment model for small and medium-sized enterprises (SMEs) to support banks in credit decision-making. An improved decision tree model is proposed by integrating regularization to control complexity and employing an ensemble learning approach to enhance prediction accuracy. Multiple weak classifiers are iteratively refined using gradient descent optimization to form a robust, strong classifier. The model is trained through supervised learning, with the default probability of SMEs as the objective function, enabling a quantitative assessment of credit risk. The findings show that the proposed gradient descent decision tree algorithm achieves an AUC of 0.99 under 70% and 80% training set ratios, outperforming Adaptive Boosting (AUC = 0.97), Random Forest (AUC = 0.91), and traditional decision trees (AUC = 0.82). To further optimize bank loan strategies, this paper constructs a nonlinear multi-objective programming model that maximizes expected loan returns while considering risk constraints. The proposed approach not only improves credit risk prediction but also assists banks in formulating optimal lending strategies. This study advances credit risk modelling by integrating regularization and ensemble learning, offering a novel and practical solution for SME credit assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".