Assessment of the Competitiveness of Islamic Fintech Implementation: A Composite Indicator for Cross-Country Analysis
Bibliographic record
Abstract
Islamic fintech is growing fast, especially in the Organisation of Islamic Cooperation (OOIC) member countries. In recent years, it has become one of the driving forces for the Islamic financial industry. Though the pandemic negatively affected global financial business, including conventional and Islamic segments, Islamic fintech has continued its steady development. i-Fintech increases access to Islamic financial services and financial inclusion in general to provide ESG-rich investment opportunities. The rise of Islamic fintech can help countries become financial hubs and promote sustainable development goals. This paper is aimed at designing an original composite indicator of the competitiveness of Islamic fintech adoption in order to perform a comprehensive assessment of the competitive advantages that are being used across various countries. The research methodology includes data for 65 countries where Islamic fintech companies are represented. We analysed 31 variables describing the development of Islamic financial technologies in each country and combined them into five categories included in the composite indicator. Key factors that determine the development of Islamic financial technologies in different countries around the globe are singled out. The economies with the highest scores are analysed to define their strengths and weaknesses. The practices of the leading countries that address identified vulnerabilities are described.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".