Ranking insurance firms using AHP and Factor Analysis
Bibliographic record
Abstract
Insurance industry includes a significant part of economy and it is important to learn more about the capabilities of different firms, which are active in this industry. In this paper, we present an empirical study to rank the insurance firms using analytical hierarchy process as well as factor analysis. The study considers four criteria including capital adequacy, quality of earning, quality of cash flow and quality of firms' assets. The results of the implementation of factor analysis (FA) have been verified using Kaiser-Meyer-Olkin (KMO=0.573) and Bartlett's Chi-Square (443.267 P-value=0.000) tests. According to the results FA, the first important factor, capital adequacy, represents 21.557% of total variance, the second factor, quality of income, represents 20.958% of total variance. In addition, the third factor, quality of cash flow, represents 19.417% of total variance and the last factor, quality of assets, represents 18.641% of total variance. The study has also used analytical hierarchy process (AHP) to rank insurance firms. The results of our survey indicate that capital adequacy (0.559) is accounted as the most important factor followed by quality of income (0.235), quality of cash flow (0.144) and quality of assets (0.061). The results of AHP are consistent with the results of FA, which somewhat validates the overall study.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".