Bank Credit Risk Rating Process: Is There a Difference Between Developed and Developing Country Banks?
Bibliographic record
Abstract
The purpose of this article is to study empirically the bank credit risk rating (BCRR) process across country groups (developed countries ‘DdC’ against developing countries ‘DgC’) after the 2012 revision of their methodologies as a response to the global and European crisis. We use the S&P’s ratings of 231 banks from 36 EMENA countries which of 18 are developed. We made this comparison based on the CAMELS model with a proposed ‘S’ to BCRR. We perform ‘ordered logit’ regression for the rating classes and complete our analysis by ‘linear multiple’ regression for the rating grades. The results show that the entire rating process, including the weight of components, the important factors and the relevant variables, of DdC banks differs partly from this of DgC. The intrinsic credit quality component of the rating has more weight for the allocation of rating grades of DdC banks and the environment supports component has more weight for those of DgC. Some important factors represented by relevant variables are specific to each bank group and others are the same for both groups, but with a difference in the influence on the rating assigned. Sovereign rating has become more relevant to define bank groups than the country level of development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".