Does COVID-19 Pandemic Affect Bank Credit Risk?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to examine the impact of the COVID-19 pandemic on banking credit risk in Indonesia, namely conventional banks and Islamic banks which are proxied through NPL and NPF variables. This study used a sample of 12 conventional commercial banks and 12 Islamic commercial banks in Indonesia. The data used is quarterly data, namely from the 1st quarter of 2017 to the 4th quarter of 2020. Furthermore, in this paper, dummy variables are used to describe the period before and after the COVID-19 pandemic that caused various declines in the economy. The method in this study uses a panel data analysis approach. The results show that COVID-19 significantly affects credit risk in the overall model and conventional bank models. Meanwhile, no correlation was found between the COVID-19 pandemic and the Islamic bank model. Furthermore, the variables found to have a significant relationship with credit risk are bank capital, total loans, and bank profitability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it