Factors Influencing Non-Performing Financing (NPF) In Sharia Banking
Bibliographic record
Abstract
The risk of financing is the risk caused by the failure of the custom[1]ers to fulfill their obligations. Non-performing financing (NPF) is a representation of financing risk that is channeled and has a direct impact on bank profitability. The value of NPF tends to increase annually with a value that is already close to the maximum limit set by Bank Indonesia of 5 percent. This condition is able to lead to the inefficiency of the banking system and in the long run, will have an impact on the sustainability of the bank. Therefore, the analysis of NPF factors should be conducted as a preventive mea[1]sure and a risks controller of business activities. This research an[1]alyzes the factors influencing NPF at sharia banking (BUS) using a quarterly datafrom first quarter of 2012 until third quarter 2016. Method used in this research is panel data analysis. The result of analysis shows that the factors influencing NPF negatively and sig[1]nificantly are ratio of revenue sharing financing (RR), Return on ssets (ROA), inflation, Capital Adequacy Ratio (CAR) and Bank[1]size while Gross Domestic Product (GDP) and Operating Cost to Operating Income (BOPO) have a significant positive effect.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".