Optimizer-Based Performance Evaluation of Deep Learning Models for Loan Eligibility Prediction
Bibliographic record
Abstract
Reliable for loan eligibility prediction is necessary for banks in order to optimize the way they lend and reduce the risk of loan default. Traditional verification techniques are not keeping up with the increasing number of applications and customer demand for quick, reliable evaluations. Deep Neural Network (DNNs), Convolutional Neural Network (CNNs), and DenseNet are the three deep learning architectures that are studied and compared in our study. To classify loan eligibility outcomes, by training models using various Optimizers, such as Adam, Stochastic Gradient Descent, RMSprop, and Adagrad.To provide an adequate evaluation, we evaluate the models using a variety of performance metrices, which include accuracy, precision, recall, F1-score, and ROC-AUC. Our goal is to highlight that CNN models with Adam regularly outperform different strategies, obtained better generalization and predictive accuracy. On other hand, DenseNet models performed well on every metric. These results highlight how deep learning approaches can be used to automate loan eligibility predictions, providing a scalable method for improving loan risk evaluations and increasing operational effectiveness in the financial services industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".