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In this study, the key areas and current trends in the field of big data applications in the insurance industry are identified, along with suggestions for future research initiatives. We identified the most prominent authors, journals, organizations, and countries based on their total publications and citations, showing their significance within the network, using bibliometric analysis on a sample of 191 articles retrieved from Scopus from 1976 to 2021. VOSviewer and R-Biblioshiny tools were used to generate the bibliometric output on these retrieved papers. The findings showed that although while a good number of writers from other parts of the world contributed to the literature on big data applications in the insurance industry, during this time, most research papers have listed the United States, India, and China as their affiliated countries. The yearly publication was either one or two, with some discontinuity, from 1976 to 2011, but since 2012, it has increased, exhibiting an exponential growth tendency. The three journals “Risks,” “Applied Stochastic Models in Business and Industry,” and “Expert Systems with Applications” are the most popular for including a sizable number of papers in the field of big data technologies in the insurance sector. Each of the top 10 authors in this field published two research papers during these 46 years. Seven areas, including fraud detection and prevention, risk assessment, pricing & rate making, technology utilization, risk management, claim processing & prediction, and finally digitalization, were the major focus of research papers on bigdata applications in the insurance business. The human-centered AI system development, adoption of wearable technology, personalization, and other topics were found to have received very little attention in this study. As a result, the researchers may now direct future research in this area. This study is completely new of its kind in the domain of insurance though few documents are available on the broad concept of finance.
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Stored only when both papers are in the corpus — 19 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.