Learning from the Malaysian Experience: Overcoming the Regulatory Challenges in the Nascent Takaful Practice Innigeria
Bibliographic record
Abstract
The Islamic insurance (Takaful) introduced in March, 2013, was specifically meant to bridge the endemic insurance gap in Nigeria by engendering deepening insurance penetration and financial inclusion of the hitherto underserved and uninsured huge Muslim clientele. However, the Takaful Operational Guidelines and a host of other enabling insurance instruments are caught up in a web of regulatory conflict and ambiguity. The legal effect of this is a huge regulatory vacuum that is bound to impact negatively on capital investment climate, breed mistrust and uncertainty and discourage participation in the nascent Takaful industry. Nigeria would need to draw from the vast experiences of Malaysia in order to overcome these challenges. Nigeria and Malaysia are both former British colonies with diverse ethnic and socio-cultural backgrounds. They both practice divergent legal systems in a secular setting. Both have sizeable numbers of Muslim population. While Malaysia is considered the hub of Takaful practice in the world, Nigeria is just an emerging market in the now trending Islamic financial revolution. This paper examines the enormous general and regulatory challenges the nascent Nigerian Takaful practice will encounter in its quest to attain sustainability and vibrancy. The methodology of the study is both doctrinal and qualitative whilst employing non-random sampling technique. The study employs both primary and secondary sources of information and interviews where appropriate. The study finds the need for a review and harmonization of all the enabling insurance instruments in Nigeria, transforming the current business models and improving practices in the insurance sector to enhance the application of Takaful. The study recommends the enactment of a Takaful Act like that of Malaysia.
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 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".