Fair pricing: A framework towards sustainable life insurance products
Bibliographic record
Abstract
This research aims to fill the gap in sustainable insurance product study. The central research question of this research is how to develop a fair pricing framework in order to design a sustainable financial product. Current profit testing method is arguably lack of policyholder considerations. The profitability decision under current method only considers profit margin for company. There is no profitability measurement for policyholder. To improve fairness under current pricing, the proposed study proposes a concept of equity in risk between company and policyholder. In order to establish equity in risk, profitability for policyholder needs to be defined and risk measure Conditional Tail Expectation (CTE) for company and policyholder is proposed as a solution. Fairness is achieved if CTE between company and policyholder falls within certain range. CTE generated under new framework could be used as a reference point to all stakeholders to assess the fairness of Unit Linked price. The target population for the study was any regular premium Unit Link product. This research used simple random sampling. From the population consisting of 34 companies 20 samples were drawn. Data is taken from the Indonesian Financial Service Authority. The data used is from the time period between 1 January 2015 and 30 June 2019. Using the CTE, this study finds that most of the Unit Linked pricing are far from fair. It is recommended that companies could be more efficient in their operating and distribution cost in order to be fairer to policyholder and therefore will make the product more sustainable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 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".