Gelişen ve Gelişmiş Ekonomilerde NPA/NPL Yönetiminin Eleştirel Değerlendirmesi: Hindistan Bağlamında Bir Çalışma
Bibliographic record
Abstract
Introduction- In a financial system-based economy, the banking system's performance and sound health are vital for developing the economy as financial intermediaries. After the global economic crisis, the financial system of advanced and emerging economies has suffered a growing volume of Nonperforming Assets (NPA) or Nonperforming Loans (NPL). Purpose- This paper explores the present status and management of NPA/NPL in advanced and emerging economies. In line with the international level in different nations of NPA/NPL’s the performance of the Indian banking system was evaluated. Research Methodology- Yearly time series data from 2011 to 2018 has been employed of 21 diverse nations, eleven are developed, and ten are developing. For picking the countries to compare and discover the measures to lower down the NPL, Malaysia, the US and Canada are chosen based on average performance and Compound Annual Growth Rate (CAGR). Findings- India's performance in NPA of the Banks has been very critical, which creates an urge for other financial sector reforms. Originality/Value- Data is has been mainly collected from the official websites of the Central Banks of various nations, thus it covers a wide countries group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 0.002 |
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".