Credit Risk Assessment Models of Retail Microfinancing: The Case of a Malaysian National Savings Bank’s Branch
Bibliographic record
Abstract
Internal ratings have been used by banks to evaluate the creditworthiness of their borrowers with diverse practices. This research aims to analyse the practice of assessing (or predicting) the credit performance of microfinancing loans of a Malaysian bank and to suggest how the existing performance of credit assessment model can be improved. Logistic regression was used to investigate the predictive ability of information on business operators’ management and accounting skills as factors to predict default risk of borrowers. The combination of these information formed the three (3) models that were used in the analysis. The accuracy rate of each model was then measured. A sample of respondents was selected among microfinance borrowers in a national savings bank’s branch in Malaysia. A total of 106 questionnaires were used for data analysis. The findings suggest that good credit rating, business experience, business financial and forecasting capability are factors associated with whether SMEs will default or not in their payments. The combination of credit score used currently by the bank and the new information produced by this research increases the bank’s ability to predict default.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".