Islamic FinTech and artificial intelligence (AI) for assessing creditworthiness
Bibliographic record
Abstract
The potential value of the Muslim market worldwide is huge, reaching out to a quarter of the world’s population, yet it receives little attention from industry players. The data showed that the median age for Muslims globally was just 24 years old, making a majority of them digital-savvy. They become more sophisticated and well-informed. The existence of Financial Technology (FinTech) plays a significant role as a potential market due to the demand for an Islamic Financial System. Peer to Peer Lending (P2P lending) is one of FinTech’s products that also enhances financial inclusion and the financial well-being of the population to access financial services. The paper discusses P2P Lending that accordance with the Islamic principle. Islamic P2P lending promotes an interest-free mechanism based on a profit and loss partnership. An Islamic P2P lending platform could be developed and they must not involve Riba (interest/usury), Gharar (uncertainty), Maisir (gambling), and Non halal (prohibited) activities. The challenge of the system comes from the area of technology to improve the overall financial system efficiencies, especially in mitigating any risks. This paper proposes AI-based Islamic P2P lending with instant credit scoring that will speed up the process of measuring creditworthiness. The credit score engines are AI-enabled and can be expected to generate a good decision.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".