INSURANCE OPPORTUNITIES AND CHALLENGES IN AN ARTIFICIAL INTELLIGENCE SOCIETY
DOI:
https://doi.org/10.47672/ejt.1180Keywords:
Insurance Opportunities, Challenges, Artificial Intelligence SocietyAbstract
Purpose: The study examined the insurance opportunities and challenges in an Artificial Intelligence society.
Methodology: This study relied on a critical review of previous academic studies on Artificial Intelligence and the insurance industry between 2017 and 2022. This method was chosen since it is more reliable, economical, and efficient in terms of time and resources. In addition, the researcher opted to use recent studies since they provide more updated information on the AI technologies that have significant impact on the insurance industry.
Findings: The results of the study revealed that the reviewed studies consisted of contextual and conceptual gaps. This is because some of the studies did not target insurance companies but other companies hence the findings may not be applicable to the insurance industry. In addition, most of these studies discussed the insurance opportunities and benefits at in depth, but failed to discuss on the challenges that insurance firms face in an artificial intelligence society.
Unique Contribution to Theory, Practice and Policy: The findings of this study will be relevant to the main players in the insurance industry. The management team of the insurance companies will have a better understanding of how the various AI technologies are adopted to the different insurance operations so as to improve business efficiency. The study has also been able to outline the main challenge that the insurance sector experience in the adoption of AI, this will encourage the management to consider investing in training programmes so as to improve the skills of their employees. On the other hand, the policymakers will also benefit from this study when developing better legal frameworks that promote the adoption of AI. Further, the study will also be useful academically to researchers and scholars by expanding the body of information on insurance and artificial intelligence, which is still scarce. The reviewed studies' highlighted research gaps that can also inspire researchers and scholars to conduct additional research on artificial intelligence and insurance. In particular, the study's key research gap on the insurance challenge experienced in an artificial intelligence society.
Downloads
References
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.
Abrardi, L., Cambini, C., & Rondi, L. (2019). The economics of artificial intelligence: A survey. Robert Schuman Centre for Advanced Studies Research Paper No. RSCAS, 58.
Abrardi, L., Cambini, C., & Rondi, L. (2021). Artificial intelligence, firms and consumer behavior: A survey. Journal of Economic Surveys.
Allam, Z., Dey, G., & Jones, D. S. (2020). Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future Urban health policy internationally. Ai, 1(2), 156-165.
Altenried, M. (2020). The platform as factory: Crowdwork and the hidden labour behind artificial intelligence. Capital & Class, 44(2), 145-158.
Baum, S. D. (2020). Medium-term artificial intelligence and society. Information, 11(6), 290.
Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems.
Boodhun, N., & Jayabalan, M. (2018). Risk prediction in life insurance industry using supervised learning algorithms. Complex & Intelligent Systems, 4(2), 145-154.
Butow, P., & Hoque, E. (2020). Using artificial intelligence to analyse and teach communication in healthcare. The Breast, 50, 49-55.
Dick, S. (2019). Artificial intelligence.
Eckert, C., & Osterrieder, K. (2020). How digitalization affects insurance companies: overview and use cases of digital technologies. Zeitschrift für die gesamte Versicherungswissenschaft, 109(5), 333-360.
Eling, M., & Kraft, M. (2020). The impact of telematics on the insurability of risks. The Journal of Risk Finance, 21(2), 77-109.
Eling, M., Nuessle, D., & Staubli, J. (2022). The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The Geneva Papers on Risk and Insurance-Issues and Practice, 47(2), 205-241.
Geetha, R., & Bhanu, S. R. D. (2018). Recruitment through artificial intelligence: a conceptual study. International Journal of Mechanical Engineering and Technology, 9(7), 63-70.
Hagger, M. S. (2019). The reasoned action approach and the theories of reasoned action and planned behavior.
Hagger, M. S., Polet, J., & Lintunen, T. (2018). The reasoned action approach applied to health behavior: Role of past behavior and tests of some key moderators using meta-analytic structural equation modeling. Social Science & Medicine, 213, 85-94.
Hentzen, J. K., Hoffmann, A., Dolan, R., & Pala, E. (2021). Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research. International Journal of Bank Marketing.
Johnson, M., Albizri, A., & Harfouche, A. (2021). Responsible artificial intelligence in healthcare: Predicting and preventing insurance claim denials for economic and social wellbeing. Information Systems Frontiers, 1-17.
Karnowski, V., Leonhard, L., & Kümpel, A. S. (2018). Why users share the news: A theory of reasoned action-based study on the antecedents of news-sharing behavior. Communication Research Reports, 35(2), 91-100.
Kietzmann, J., & Pitt, L. F. (2020). Artificial intelligence and machine learning: What managers need to know. Business Horizons, 63(2), 131-133.
LaCaille, L. (2020). Theory of reasoned action. Encyclopedia of behavioral medicine, 2231-2234.
Lamberton, C., Brigo, D., & Hoy, D. (2017). Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities. Journal of Financial Perspectives, 4(1).
Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1-29.
Malali, A. B., & Gopalakrishnan, S. (2020). Application of Artificial Intelligence and Its Powered Technologies in the Indian Banking and Financial Industry: An Overview. IOSR Journal Of Humanities And Social Science, 25(4), 55-60.
Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 376(2128), 20170357.
Naud, W., Bray, A., & Lee, C. (2022). Crowdsourcing Artificial Intelligence in Africa: Analysis of a Data Science Contest. Available at SSRN 4076351.
Ng, K. Y. N. (2020). The moderating role of trust and the theory of reasoned action. Journal of Knowledge Management, 24(6), 1221-1240.
Rawat, S., Rawat, A., Kumar, D., & Sabitha, A. S. (2021). Application of machine learning and data visualization techniques for decision support in the insurance sector. International Journal of Information Management Data Insights, 1(2), 100012.
Riikkinen, M., Saarijärvi, H., Sarlin, P., & Lähteenmäki, I. (2018). Using artificial intelligence to create value in insurance. International Journal of Bank Marketing.
Setiawan, R., Cavaliere, L. P. L., Koti, K., Ogunmola, G. A., Jalil, N. A., Chakravarthi, M. K., ... & Singh, S. (2021). The Artificial Intelligence and Inventory Effect on Banking Industrial Performance (Doctoral dissertation, Petra Christian University).
Sidaoui, K., Jaakkola, M., & Burton, J. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal of Service Management.
Toniolo, K., Masiero, E., Massaro, M., & Bagnoli, C. (2020). Sustainable business models and artificial intelligence: Opportunities and challenges. Knowledge, People, and Digital Transformation, 103-117.
Tuck, M., & Riley, D. (2017). The theory of reasoned action: A decision theory of crime. In The reasoning criminal (pp. 156-169). Routledge.
Turkina, E. (2018). The importance of networking to entrepreneurship: Montreal's artificial intelligence cluster and its born-global firm element AI. Journal of Small Business & Entrepreneurship, 30(1), 1-8.
Yzer, M. (2017). Theory of reasoned action and theory of planned behavior. The international encyclopedia of media effects, 1-7.
Downloads
Published
How to Cite
Issue
Section
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
