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Record W2085040518 · doi:10.5539/ibr.v6n5p180

Comparative Analysis of Risk Management in Conventional and Islamic Banks: The Case of Bosnia and Herzegovina

2013· article· en· W2085040518 on OpenAlexvenueno aff
Emira Kozarević, Senija Nuhanović, Mirnesa Baraković Nurikić

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamIslamic bankingBusinessRisk managementAccountingFinancial systemProcess (computing)FinanceComputer scienceGeography

Abstract

fetched live from OpenAlex

Any banking activity involves a certain level of risk. Regardless of the fact that risk has always been present in banks, active risk management in conventional banking started only in the 1990s, specially after the collapse of Barings PLC, the bank with more than 200-year-old tradition, while such practice is still underdeveloped in Islamic (interest-free) banking whose practical implementation in the world started only in the 1970s. However, in the times of the recent sub-prime mortgage crisis and a large number of collapses and near collapses, multibillion losses and write-offs all banks, whether conventional or Islamic, saw the necessity for active risk management. Specific features of banks in Bosnia and Herzegovina and their way of managing risks are conditioned by a particular legal framework. This framework regulates primarily the conventional banks, but it indirectly affects the Islamic bank as well, since it does not allow the bank to offer all types of products and services which are generally present in Islamic banking. Since, to our knowledge, not a single specific comparative research into conventional and Islamic banks has been done in this part of the world, the aim of this paper is to provide an insight into risk management practiced by BiH banks, and to determine the dependence of their financial performance on the process of active risk management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.425
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.335
Teacher spread0.299 · 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 teacher head, 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

Citations15
Published2013
Admission routes1
Has abstractyes

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