Empirical Analysis of Non Performing Assets Related to Private Banks of India
Bibliographic record
Abstract
In present scenario, Indian banks are struggling with challenges related with NPA’s. Some years before these banks were in Flourishing heights.but health of these banks deteriorated because of non performing assets. Many Indian banks have been controlled their non performing assets up to a level, but some banks still have been failed to control their NPA’s status, as a result, NPA hitting the profitability of these banks. Through this research paper we have examined the trend of NPA’s over the past 8 years and the relationship between NPA’s and profitability of private sector banks. According to the Reserve bank of India priority sector lending must be promoted so that those sectors who can’t approach the organized market for lending purposes and can’t afford the higher commercial rate of interest, can get loans in an easy way. RBI specified the percentage of loans to priority sectors out of the total money lent by the banks. This paper examines the NPA in Priority Sector Lending and the impact of priority sector lending on the gross NPA of private sector banks. The result showed the significant impact of priority sector lending on gross NPA of private Sector banks. This study revolves between the period 2005 and 2012.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".