Insurtech - Overview and Influence of Business Model Innovation in the Insurance Industry - A structured analysis
Bibliographic record
Abstract
Almost two billion dollars in venture capital investment between the third quarter of 2015 and 2016 for Insurtechs and headlines promising the needed shakeup in the insurance industry, Insurtech is a new phenomenon for financial services, similar to Fintech, but exclusive to insurance. While hopes and promises are high, thorough analysis of the landscape of this new kind of insurance companies remain scarce. This work aims to enable an overview of how Insurtechs are categorized into distinct fields and what potential impact they could have on the insurer’s value chain. For this, 253 Insurtechs across 13 categories are analyzed. The thorough analysis reveals that Insurtechs potentially have an impact in almost all activities of the insurer’s value chain through business model innovation. However, it also concludes, that their business models on distribution, customer service, data analytics and digitalization are more an opportunity than a threat for incumbents as 70% act as digital brokers, 25% as service providers through software and only 5% are insurers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".