Sustainability of Financial Soundness of Banks: An Evidence Form Public and Private Sector Banks
Bibliographic record
Abstract
The banking sector plays a vital role in the growth and development of the economy of any nation. Nowadays, NPAs are great challenges for banks and harm profitability, and financial soundness, and hinder the operational activities of the banks. The Non-Performing Assets (NPAs) refer to the loans and advances of which payment of interest or principal amount is delayed, or missed as per the bank’s schedule. The purpose of the study is to find out the comparative financial soundness of the leading Indian public and private banks to get the hedging factors against the NPAs. Possibly, the hedging factors against the NPAs will be helpful to control and reduce the level of NPAs in Indian banks. Financial ratios are the base to measure financial soundness. The graphical presentation and ANOVA (Analysis of Variance) were applied to get the comparative growth trend and disparity among the financial soundness measures of the leading Indian private and public sector banks. The analysis reveals that there is a significant difference in the financial soundness of leading Indian private and public sector banks. The NIM (net interest margin) of leading Indian public sectors is significantly different and the public banks with higher NIM utilize their profitability to write off their NPAs. Based on the study is advised to enhance the CASA (current account and saving account to total deposits) for hedging against NPAs and the profitability in public sector banks.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".