Evaluation of Financial Credit Risk Management Models Based on Gradient Descent and Meta-Heuristic Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
An efficient credit risk management model is a promising technique that provides Financial Institutions or Banks the ability to determine a creditworthy customer from a non-worthy customer.The fact remains that no country's economy can survive or improve without credit using historically available data.This paper presents an evaluation of several gradient descent techniques, and metaheuristic optimization algorithms implemented in Machine Learning and Multi-layer perceptron for better credit risk prediction.It also handles imbalanced dataset using smote Edited Nearest Neighbour.The study provided various architectures and advantages of the algorithms while addressing how the limitations can be improved to build a better credit risk model and improve model accuracy.The study showed MLP WOA achieved accuracy of 98.56% based on Adam gradient descent to achieve faster convergence and exploration compared to MLP PSO with 98.39%.
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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 | 0.001 | 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 | 0.002 |
| 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 it