Emerging AI Trends Shaping the Insurance Industry
Bibliographic record
Abstract
Artificial Intelligence infuses its essence into Indian Insurance. The Exhaustive Analysis of AI Adoption in 61 Articles from 2015 to 2025 from Scopus, IEEE Xplore, Google Scholar, and Web of Science for Assessments of Innovations in Insurance by AI. Innovations in underwriting, claims management, fraud detection, and customer engagement were identified in the study, all of which illustrated how AI improved operational efficiency and risk assessment. InsurTech startups and legacy firms are, hence, increasingly caught up in predictive analytics, chatbot interaction, and smart contracts based on blockchain for automating policy-fraud contracts and deterrents. The emphasis of this study would be on the ethical use of AI and governance mechanisms for adopting it to ensure the sustainability of growth. Such Treatment Will Make Possible the Use of AI by Indian Insurers to Plug the Protection Gap, Build Customer Trust, and Fit Within the Emerging Regulatory Dynamics of the Indian Environment to Ensure Responsible and Effective AI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".