The Impact of Machine Learning on the Future of Insurance Industry
DOI:
https://doi.org/10.18034/ajtp.v7i3.537Keywords:
Machine learning, insurance industry, data, assurance value chainAbstract
Recently data remains the central and the core concentration in the insurance industry. The outburst in data generation thus created the need for technologies that will be used to process or manage big data in the industry. Thus, the conditions at hand are sighted by a plodding but firm change, which is compelled by an atmosphere shown by enhanced rivalry, fraud activities, flexible market places, high prospects from clients, and stringent guidelines. The introduction of machine learning in solving industry tasks in the assurance value chain such as underwriting and forfeiture avoidance, entitlements management, fraud uncovering, product evaluating, transactions, and client capability will put the industry in the damp light in the future due to high increase of big data. This paper has examines some cases and bring out the vital role of machine learning in handling client data and resolving issues of entitlements. Hence, machine learning holds a brighter future for insurance organizations if implemented well.
Downloads
References
Anyoha, R. (2017), The History of Artificial Intelligence, Harvard Science in the News, http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ (accessed on 23/04/2021).
BaFin (2018), Big Data meets artificial intelligence– Challenges and implications for the supervision and regulation of financial services, https://www.bafin.de/SharedDocs/Downloads/EN/dl_bdai_studie_en.html (accessed on 26/04/2021).
Belhadji, E., G. Dionne, and Tarkhani, F. 2012. A Model for the Detection of Insurance Fraud, Geneva Papers on Risk and Insurance Theory‖, 25: 517-538, may 2012. DOI: https://doi.org/10.1111/1468-0440.00080
Burri, R.D., Burri, R., Bojja, R.R. and Buruga, S.R. 2019. Insurance Claim Analysis Using Machine Learning Algorithms. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6&4): 577-582. DOI: https://doi.org/10.35940/ijitee.F1118.0486S419
Chen, Chiang and Storey 2012. “Business Intelligence and Analytics: From Big Data to Big Impact”, MIS Quarterly, 36(4): 1165, http://dx.doi.org/10.2307/41703503. DOI: https://doi.org/10.2307/41703503
Crocker, K. J., and Tennyson, S. 2010. Insurance Fraud and Optimal Claims Settlement Strategies: An Empirical Investigation of Liability Insurance Settlements‖. The Journal of Law and Economics, 45(2), April 2010. DOI: https://doi.org/10.1086/340394
FCA. 2016, “FS16/5: Call for Inputs on Big Data in retail general insurance | FCA”, No. FS16/5, Accessed on 25/04/2021 https://www.fca.org.uk/publications/feedback-statements/fs16-5-call-inputs-big-data-retailgeneral-insurance
Gandomi, A. and Haider, M. 2015. “Beyond the hype: Big data concepts, methods, and analytics”, International Journal of Information Management, 35(2): 137-144, http://dx.doi.org/10.1016/J.IJINFOMGT.2014.10.007. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Geron, A. 2019. Hands-on machine learning with Scikit-Learn, Keras, and Tensor Flow: concepts, tools, and techniques to build intelligent systems. Accessed on 25/04/2020.
Jones, S., Humphreys, B, and Woolnough, M. 2019. Considering the Impact of AI in Insurance. IBM Power Systems, 1-25.
Kajiamuller, 2013. The Identification of Insurance Fraud – an Empirical Analysis Working papers on Risk Management and Insurance‖ no: 137, June 2013.
Kotsiantis, S.B. 2011. Supervised Machine Learning: A Review of Classification Techniques, Informaticavol., 31, 249-268.
Mishr K. 2016. Fundamentals of life insurance theories and applications. In: 2nd ed, Delhi: PHI Learning Pvt Ltd
OECD (2020), The Impact of Big Data and Artificial Intelligence (AI) in the Insurance Sector, www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.htm.
SAS. 2020. Machine learning, what it is and why it matters. Accessed on 20/04/2021 from https://www.sas.com/en_gb/insights/analytics/machine-learning.html
Sivarajah U, Kamal M, Irani Z and Weerakkody V. 2017. Critical analysis of big data challenges and analytical methods. J Bus Res 70:263–286 DOI: https://doi.org/10.1016/j.jbusres.2016.08.001
The Accenture website https://www.accenture.com. Accessed on 24/04/2021
Vadlamudi, S. (2015). Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion. Engineering International, 3(2), 105-114. https://doi.org/10.18034/ei.v3i2.519
Vadlamudi, S. (2016). What Impact does Internet of Things have on Project Management in Project based Firms?. Asian Business Review, 6(3), 179-186. https://doi.org/10.18034/abr.v6i3.520
Vadlamudi, S. (2018). Agri-Food System and Artificial Intelligence: Reconsidering Imperishability. Asian Journal of Applied Science and Engineering, 7(1), 33-42. Retrieved from https://journals.abc.us.org/index.php/ajase/article/view/1192
Vadlamudi, S. (2019). How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis. Asia Pacific Journal of Energy and Environment, 6(2), 91-100. https://doi.org/10.18034/apjee.v6i2.542 DOI: https://doi.org/10.18034/apjee.v6i2.542
--0--
Downloads
Published
Issue
Section
License
American Journal of Trade and Policy is an Open Access journal. Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a CC BY-NC 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of their work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. We require authors to inform us of any instances of re-publication.
How to Cite
