The Future of Insurance Intermediation in the Age of the Digital Platform Economy
Bibliographic record
Abstract
Today most insurance is sold by over a million brokers and independent agents acting as intermediaries between the insurance companies and their customers. Digitalization and changing customer behavior have fostered the development of insurtech businesses, and, more recently, multi-sided platforms are emerging as new market forms for insurance intermediation. This paper aims to provide a better understanding of how the emergence of the platform economy, with a market dominated by multi-sided platforms, will potentially impact insurance intermediation in the future. Using inductive content analysis on the results of a systematic literature review of the body of research on insurance intermediation, we identify the key functional roles fulfilled by insurance intermediaries. Applying these roles to a literature review on multi-sided platforms allows us to compare how different market forms and players embody the functional roles of intermediaries. Our findings suggest that multi-sided platforms are better able to perform certain roles in terms of agility, scale and scope, and we discuss the future role of platforms in insurance intermediation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".