Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia
Bibliographic record
Abstract
Since it was first operating in 2005 until 2017, Indonesia Deposit Insurance Corporation (IDIC) has liquidated 91 rural banks which were determined as failed banks by supervision authority. The cause of the failing of the bank is mainly due to the incapability of the bank to meet the minimum Capital Adequacy Ratio (CAR). Bank’s capital was shrunk by the loss caused by fraud. The fraud is mostly induced by the lack of good corporate governance implementation. By using the logistic regression, it can be concluded that (1) the incomplete of responsibility letter which will be used in the event of bank failure, submitted by the bank commissioners; (2) the incomplete of responsibility letter which will be used in the event of bank failure, submitted by the bank directors; (3) role duplication between shareholders and board of directors; and (4) bank had classified as special supervision, have impact on the increase of rural banks failure. At the same time, the compliance level of rural banks to a correct premium payment has impacted to decrease of rural banks failure possibilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".