Predicting Likelihood for Loan Default Among Bank Borrowers
Bibliographic record
Abstract
Poverty is a threat to the world. In its extreme form at any part of the world, it will make endanger rest of the world. In fact, it is the source of crime and the worst form of violence. The poor people do not commit any crime but they get punishment out of being born as a poor that is not controllable in their hand. Microfinance has been designed to eliminate poverty and help marginal and poor people through small income generating activities. The borrowers need capital to materialize their dream, may be in a small amount and microfinance can play important role in this scenario. Through microfinance, small entrepreneurs may acquire necessary inputs to start their business. Both local governments and international agencies are trying to eliminate poverty through microfinance programs, services and guidelines. With this concept, Microfinance has been hosted primarily in Bangladesh. Grameen Bank (GB) has been serving large number of people below poverty level in Bangladesh. However, impact of microfinance is still questionable in several studies. Microfinance used properly and returned back to the lender with stipulated amount and time shows its working effectively for poverty alleviation. Otherwise, there must be loan default and the whole system may be in question. We survey with questionnaire to find out factors contributing to loan default among GB borrowers using binomial logistic regression. The results showed that some factors were crucial for loan default and should be treated properly at the start of lending.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".