Sharia Financial Technology in the Development of Bankable Micro Businesses
Bibliographic record
Abstract
Fintech is one of the drivers of the existence of a movement to improve MSME finance, especially the lower middle class through Islamic financial institutions. The development of digital technology, including in the Islamic financial industry, has had a major influence with the existence of financial technology (fintech), all forms of transactions are faster, easier, and more efficient, without the need to meet in person. Financial technology collaboration with Islamic financial institutions, especially Islamic banking can increase financial inclusion at MSMEs in Indonesia. The implementation of Fintech in the Islamic banking industry will facilitate and bring business players closer, especially MSMEs to access Islamic financial service products offered and apply for financing directly without having to go directly to the branch offices. Such a model, in addition to making it easier for MSME sector business people to gain financial access, can also improve financial inclusion and improve the performance of Islamic banks. Efforts to increase the capacity of micro businesses that were originally unbankable can be increased to bankable. Where the role of related institutions such as banking and fintech, which is currently becoming popular in the community, can contribute and build micro businesses to become more independent and encourage economic development in Indonesia with the collaboration of banking institutions and micro businesses in financing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".