TECHNOLOGICAL DISRUPTION IN P&C INSURANCE: THE IMPACT OF ADVANCED ANALYTICS ON RISK ASSESSMENT AND CUSTOMER ENGAGEMENT
Keywords:
P&C Insurance Analytics, InsurTech, Artificial Intelligence Underwriting, Big Data, Risk Assessment, IoT Insurance SolutionsAbstract
This article examines the transformative role of technological advancements in reshaping analytical practices within the property and casualty (P&C) insurance industry. By leveraging innovations such as big data analytics, artificial intelligence, machine learning, and the Internet of Things, insurers are revolutionizing their approach to risk assessment, underwriting, and claims management. The article explores how these technologies enable more accurate pricing models, personalized products, and enhanced fraud detection capabilities. Furthermore, it investigates the impact of advanced analytics on customer experience, discussing how data-driven insights foster improved engagement and loyalty. The article also addresses emerging trends, including telematics and blockchain technology, while considering the challenges insurers face in adopting these innovations, such as data privacy concerns and regulatory compliance. Through a comprehensive analysis of current practices and future possibilities, this article provides valuable insights into the ongoing digital transformation of the P&C insurance sector, highlighting the critical importance of embracing technological advancements to maintain competitiveness in an increasingly dynamic marketplace.
References
J. Charpentier, "Computational Actuarial Science with R," Chapman and Hall/CRC, 2014. https://www.routledge.com/Computational-Actuarial-Science-with-R/Charpentier/p/book/9781466592599
M. Eling and M. Lehmann, "The Impact of Digitalization on the Insurance Value Chain and the Insurability of Risks," The Geneva Papers on Risk and Insurance - Issues and Practice, vol. 43, no. 3, pp. 359-396, 2018. https://link.springer.com/article/10.1057/s41288-017-0073-0
K. Antonio and R. Plat, "Micro-level stochastic loss reserving for general insurance," Scandinavian Actuarial Journal, vol. 2014, no. 7, pp. 649-669, 2014. https://www.tandfonline.com/doi/full/10.1080/03461238.2012.755938
G. Pesantez-Narvaez, J. Guillen, and M. Alcañiz, "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, vol. 7, no. 2, p. 70, 2019. https://www.mdpi.com/2227-9091/7/2/70
S. Viaene, R. A. Derrig, B. Baesens, and G. Dedene, "A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection," Journal of Risk and Insurance, vol. 69, no. 3, pp. 373-421, 2002. https://onlinelibrary.wiley.com/doi/10.1111/1539-6975.00023
M. Riikkinen, H. Saarijärvi, P. Sarlin, and I. Lähteenmäki, "Using artificial intelligence to create value in insurance," International Journal of Bank Marketing, vol. 36, no. 6, pp. 1145-1168, 2018. https://www.emerald.com/insight/content/doi/10.1108/IJBM-01-2017-0015/full/html
F. Bohnert, A. Fritzsche, and S. Gregor, "Digital Agendas in the Insurance Industry: The Importance of Comprehensive Approaches," The Geneva Papers on Risk and Insurance - Issues and Practice, vol. 44, no. 1, pp. 1-19, 2019. https://link.springer.com/article/10.1057/s41288-018-0109-0
S. Viaene and B. Dedene, "Insurance Fraud: Issues and Challenges," The Geneva Papers on Risk and Insurance - Issues and Practice, vol. 29, no. 2, pp. 313-333, 2004. https://link.springer.com/article/10.1111/j.1468-0440.2004.00290.x
A. Cappiello, "Technology and the Insurance Industry: Re-configuring the Competitive Landscape," Springer, 2018. https://link.springer.com/book/10.1007/978-3-319-74712-5
C. Biener, M. Eling, and J. H. Wirfs, "Insurability of Cyber Risk: An Empirical Analysis," The Geneva Papers on Risk and Insurance - Issues and Practice, vol. 40, no. 1, pp. 131-158, 2015. https://link.springer.com/article/10.1057/gpp.2014.19