Developing a Model of Insurance Securitisation in Iranian Environmental Conditions
Bibliographic record
Abstract
As a growing industry in Iran, the insurance industry has dramatically grasped researchers’ and managers’ attention. Among the various issues in this industry, measuring and evaluating the efficiency and performance of its units and branches has always been considered by relevant experts because such evaluation can help us take adequate steps to improve this area. Through securitisation, insurance companies may mitigate the cost of their capital, increase the return on equity, and improve other metrics that affect their operating performance. Securitisation increases capital productivity in the insurance industry. Therefore, the present study was conducted in 2020 to review and develop a model of insurance securitisation in Iran. The present study is exploratory research. Thus, 13 experts and commentators in insurance securities were interviewed. Second, based on the theme analysis, the content of the interviews was analysed, and a proposed model was developed. Then, according to the obtained model, a questionnaire was designed and distributed among insurance industry experts. Two concepts of validity and reliability were used to validate the questionnaire. Based on the model, 10 main factors were identified as influencing insurance securitisation. Insurance securitisation, management of Iran’s environmental conditions, the role of the capital market in insurance, financing, economic development, optimal risk management, risk transfer process in insurance securitisation, investment culture, support of regulatory bodies and facilities in the securities issuance process, utilisation of technical knowledge and specialised human resources are the factors identified in the research. The results showed that all these factors identified from the interviews were confirmed, and the model was sufficiently valid.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".