The Applications of Big Data in the Insurance Industry: A Bibliometric and Systematic Review of Relevant Literature

[1]  Haitham Nobanee,et al.  What Do We Know About Meme Stocks? A Bibliometric and Systematic Review, Current Streams, Developments, and Directions for Future Research , 2023, SSRN Electronic Journal.

[2]  N. Ellili.  Is there any association between FinTech and sustainability? Evidence from bibliometric review and content analysis , 2022, Journal of Financial Services Marketing.

[3]  R. Sureka,et al.  Mapping the intellectual structure of corporate risk reporting research: a bibliometric analysis , 2022, International Journal of Disclosure and Governance.

[4]  Haitham Nobanee,et al.  Reputational Risk and Sustainability: A Bibliometric Analysis of Relevant Literature , 2021, Risks.

[5]  Haitham Nobanee,et al.  A Bibliometric Review of Big Data in Finance , 2021, Big Data.

[6]  Dong Wook Kim,et al.  Factors affecting the survival of early COVID-19 patients in South Korea: An observational study based on the Korean National Health Insurance big data , 2021, International Journal of Infectious Diseases.

[7]  Mohamed Hanafy,et al.  Machine Learning Approaches for Auto Insurance Big Data , 2021, Risks.

[8]  Ahmed A. Elamer,et al.  A bibliometric analysis of cash holdings literature: current status, development, and agenda for future research , 2021, Management Review Quarterly.

[9]  Mun-Taek Choi,et al.  Analysis of Health Insurance Big Data for Early Detection of Disabilities: Algorithm Development and Validation , 2020, JMIR medical informatics.

[10]  E. Frees,et al.  The Discriminating (Pricing) Actuary , 2020, SSRN Electronic Journal.

[11]  Satish Kumar,et al.  A bibliometric analysis of managerial finance: a retrospective , 2020 .

[12]  Laurence Barry.  Insurance, Big Data and Changing Conceptions of Fairness , 2020, European Journal of Sociology.

[13]  Dong Wook Kim,et al.  Compliance of Antihypertensive Medication and Risk of Coronavirus Disease 2019: a Cohort Study Using Big Data from the Korean National Health Insurance Service , 2020, Journal of Korean medical science.

[14]  Dong Wook Kim,et al.  The Correlation of Comorbidities on the Mortality in Patients with COVID-19: an Observational Study Based on the Korean National Health Insurance Big Data , 2020, Journal of Korean medical science.

[15]  Joseph Ali,et al.  Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance , 2020, Bulletin of the World Health Organization.

[16]  Hossein Hassani,et al.  Text Mining in Big Data Analytics , 2020, Big Data Cogn. Comput..

[17]  Arthur Charpentier,et al.  Personalization as a promise: Can Big Data change the practice of insurance? , 2020, Big Data Soc..

[18]  Subramanian Arumugam,et al.  A survey on driving behavior analysis in usage based insurance using big data , 2019, Journal of Big Data.

[19]  Peter B. Walker,et al.  Federated Learning for Healthcare Informatics , 2019, Journal of Healthcare Informatics Research.

[20]  Swu-Jane Lin,et al.  Taiwan’s National Health Insurance Research Database: past and future , 2019, Clinical epidemiology.

[21]  W. Price,et al.  Privacy in the age of medical big data , 2019, Nature Medicine.

[22]  Jae-Sung Lim,et al.  Building Linked Big Data for Stroke in Korea: Linkage of Stroke Registry and National Health Insurance Claims Data , 2018, Journal of Korean medical science.

[23]  Richard A. Bauder,et al.  Big Data fraud detection using multiple medicare data sources , 2018, J. Big Data.

[24]  Yong-Ha Kim,et al.  Breast reconstruction statistics in Korea from the Big Data Hub of the Health Insurance Review and Assessment Service , 2018, Archives of plastic surgery.

[25]  Holger Fröhlich,et al.  From hype to reality: data science enabling personalized medicine , 2018, BMC Medicine.

[26]  Jan vom Brocke,et al.  How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service , 2018, J. Manag. Inf. Syst..

[27]  Weiwei Lin,et al.  An Ensemble Random Forest Algorithm for Insurance Big Data Analysis , 2017, IEEE Access.

[28]  Branco Ponomariov,et al.  What is co-authorship? , 2016, Scientometrics.

[29]  Keke Gai,et al.  Intrusion detection techniques for mobile cloud computing in heterogeneous 5G , 2016, Secur. Commun. Networks.

[30]  S. Ang,et al.  A 3-D stacked wire bondless silicon carbide power module , 2016, 2016 IEEE 4th Workshop on Wide Bandgap Power Devices and Applications (WiPDA).

[31]  Malin Song,et al.  Customer profitability forecasting using Big Data analytics: A case study of the insurance industry , 2016, Comput. Ind. Eng..

[32]  Bruna de Paula Fonseca e Fonseca,et al.  Co-authorship network analysis in health research: method and potential use , 2016, Health Research Policy and Systems.

[33]  Wray L. Buntine,et al.  Bibliographic analysis on research publications using authors, categorical labels and the citation network , 2016, Machine Learning.

[34]  Keke Gai,et al.  Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm , 2015, IEEE Transactions on Computers.

[35]  D. Segev,et al.  Big Data in Organ Transplantation: Registries and Administrative Claims , 2014, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.

[36]  Keke Gai,et al.  A Review of Leveraging Private Cloud Computing in Financial Service Institutions: Value Propositions and Current Performances , 2014 .

[37]  Frank A. Pasquale,et al.  [89WashLRev0001] The Scored Society: Due Process for Automated Predictions , 2014 .

[38]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[39]  Varun Chandola,et al.  Knowledge discovery from massive healthcare claims data , 2013, KDD.

[40]  Seref Sagiroglu,et al.  Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[41]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[42]  Wolfgang Glänzel,et al.  Domesticity and internationality in co-authorship, references and citations , 2005, Scientometrics.

[43]  Zinoviy Landsman,et al.  Risk measures and insurance premium principles , 2001 .

[44]  Henry G. Small,et al.  Co-citation in the scientific literature: A new measure of the relationship between two documents , 1973, J. Am. Soc. Inf. Sci..

[45]  J. Tukey.  Comparing individual means in the analysis of variance. , 1949, Biometrics.

[46]  Satish Muppidi,et al.  Co-occurrence analysis of scientific documents in citation networks , 2020, Int. J. Knowl. Based Intell. Eng. Syst..

[47]  Jaakko Peltonen,et al.  The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research , 2020 .

[48]  Jan W. Buzydlowski,et al.  Co-occurrence analysis as a framework for data mining , 2015 .

[49]  L. Breiman.  Random Forests , 2001, Machine Learning.

[50]  K. Boyack,et al.  Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? , 2010, J. Assoc. Inf. Sci. Technol..