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Record W3126447520

Effect of Non-Performing Assets on Banking Sector: A Study on State Bank of India

2020· article· en· W3126447520 on OpenAlexvenueno aff
Jyotirmoy Koley

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBanking Sector Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexNon-performing assetPosition (finance)BusinessAsset (computer security)Private sectorFinancial systemPublic sectorState (computer science)Asset managementState ownedEconomyEconomicsFinanceMarket economyEconomic growthComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

Non-performing asset (NPA) is a critical challenge for the banking sector in India and the world as a whole now-a-days. It is negatively affecting the profitability of the banking industry over the period. The NPA is a drag on the banking sector. Various related studies, researches and banking statistics evidently show that the NPA is increasing more rapidly in the public sector banks than private sector banks in India scenario. It could only meet up by the vital and efficient asset management wings on the banks. Indian is holding 33rds position in respect of the world’s gross NPA ratio (10.3%). This paper has attempted to analyze the effect of NPA on the profitability and the trend of NPA of the State Bank of India over last ten years from 2009-10 to 2018-19. The study reveals that the NPA has a gradual increasing trend, but a sharp decline in year 2018-19. The paper also shows that the NPA has an inverse effect on the profitability of the State bank of India

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.016
GPT teacher head0.249
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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