MétaCan
Menu
Back to cohort
Record W3037481330 · doi:10.5430/bmr.v9n2p9

Management of Financial Technology and Its Impact on the Banking Services: Palestine

2020· article· en· W3037481330 on OpenAlexvenueno aff
Iyad Yousef Dalbah

Bibliographic record

VenueBusiness and Management Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsAttractivenessFinancial servicesBusinessOrder (exchange)MarketingService (business)Product (mathematics)PalestineFinance

Abstract

fetched live from OpenAlex

This paper seeks to investigate the impact Financial Technology would have on the financial service banking industry in Palestine, The results show that the financial institutions need to adapt to the digital trends as early as possible, understanding the unmet needs of a digital customer in a better way. The growing expectation from financial institutions is to shift from product-based models to customer-based models, equipping themselves to offer real-time, easy to use, personalized products and services to the digital customers through customer’s preferred channel, Financial Technology is greatly innovating and enhancing the efficiency of the financial service industry thereby contributing to economic development. In Palestine, The researcher recommend the use of specialists in the field of electronic sites design in particular, because the site attractiveness needs experience sufficient experience in this area to support its attractiveness for customers, and to benefit from the experiences of the developed countries in the field of software technology control and protection of customer information, in order to strengthen current Software applied to those banks.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.048
GPT teacher head0.301
Teacher spread0.253 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBusiness and Management ResearchSame topicFinTech, Crowdfunding, Digital FinanceFrench-language works237,207