Digital Transformation in Taiwan’s Insurance Industries for MABAC Technology Based on Circular Modified Fuzzy Choquet Frank Network Data Envelopment Analysis
Bibliographic record
Abstract
Fuzzy set theory has significant and dominant applications in Taiwan’s insurance industry, especially in fields involving decision-making, uncertainty, and risk assessment. Providing the complexity and problems in assessing factors, for instance, natural disaster risks, customer creditworthiness, or health conditions, traditional binary logic often falls short. Taiwanese insurers have adopted fuzzy logic systems to enhance fraud detection, premium pricing, and privilege evaluations by catching the indistinctness characteristic in human ruling and imperfect data. The Taiwan insurance industry is a dynamic and spirited module of the commercial sector, contributing meaningfully to risk management and economic stability. For this, we study to propose an assessment of the proficiency of insurance enterprises using Network Data Envelopment Analysis. Toward this end, the frank operational laws for circular Pythagorean fuzzy (CPF) uncertainty are applied. Moreover, the CPF Choquet Frank averaging (CPFCFA) operator and CPF Choquet Frank geometric (CPFCFG) operator with three dominant properties for each operator have been studied. The study deliberates the multi-attributive border approximation area comparison (MABAC) model and verifies it with the help of numerical examples. This study enhances the industry’s efficiency to offer adapted insurance products and handle risks precisely, aligning with Taiwan’s push toward intelligent financial services and digital transformation. In the following, we establish the decision-making performance for assessing the proficiency of insurance enterprises using the network data envelopment analysis (NDEA) technique. Finally, we examine the ranking values of offered representations to compare them with the ranking values of prevailing models to show the capability and efficacy of the originated approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 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".