Digital Transformation: Enhancing Agent and Customer Experiences in P&C Explore the Role of Insurtech and Digital Tools in Streamlining Operations, Improving Agent Productivity, And Meeting Evolving Policyholder Expectations in a Post-Covid Landscape
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I3P112Keywords:
Digital Transformation, Property and Casualty (P&C) Insurance, InsurTech, Customer Experience, Agent Productivity, Policyholder Engagement, Automation in Insurance, Artificial Intelligence (AI), Machine Learning (ML), Digital Claims Management, Data AnalyticsAbstract
The COVID-19 pandemic served as a critical driver of the digital revolution in the Property and Casualty (P&C) insurance industry, forcing the insurers to update their business and customer relations strategies. Conventional ways of doing business that were frequently typified by archaic systems, manual functions, and unintegrated information reservoirs became ineffective in dealings with the increased demands of digitally savvy policyholders and intermediaries. The enhanced usage of insurtech solution has transformed the manner in which insurers underwrite risk, submit claim and interact with customers on various touchpoints. With the help of automation, artificial intelligence (AI) and analytics, P&C companies are advancing more and more intelligent workflows, predictive models and electronic onboarding systems to become more responsive, more efficient in their operations, and more precise with the decisions they are making in an intensely competitive market. In this study, a holistic digital transformation framework is proposed, which incorporates the cloud-native architecture, data standardization, and API-based ecosystems to ensure smooth cooperation among agents, clients, and the back-end infrastructure. Industry experience through implementations has shown significant reduction in claims turnaround time, quote-to-bind ratio and retention of customers. The paper has also demonstrated the strategic importance of advanced analytics, automation, and personalization in the development of customer-centric insurance ecosystems characterized by value creation. This paper earns a synthesis of current research and case studies to offer a conceptual framework and a practical guide that can be applied by the insurers to gain digital maturity, remain profitable, and resilient in the fast-changing post-COVID landscape of insurance
References
[1] Carletti, E., Claessens, S., Fatás, A., & Vives, X. (2020). Post-Covid-19 World. Centre for economic policy research.
[2] Sinha, D., Bagodi, V., & Dey, D. (2020). The supply chain disruption framework post COVID-19: A system dynamics model. Foreign trade review, 55(4), 511-534.
[3] Karri, N. (2021). Self-Driving Databases. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 74-83. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P10
[4] Lanfranchi, D., & Grassi, L. (2021). Translating technological innovation into efficiency: the case of US public P&C insurance companies. Eurasian Business Review, 11(4), 565-585.
[5] Amitrano, C. C., Russo Spena, T., & Bifulco, F. (2021). Digital engagement and customer experience. In Digital transformation in the cultural heritage sector: Challenges to marketing in the new digital era (pp. 119-136). Cham: Springer International Publishing.
[6] Karri, N., & Jangam, S. K. (2021). Security and Compliance Monitoring. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 73-82. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P109
[7] Demmers, J., Weltevreden, J. W., & van Dolen, W. M. (2020). Consumer engagement with brand posts on social media in consecutive stages of the customer journey. International Journal of Electronic Commerce, 24(1), 53-77.
[8] Jung, T., Polani, D., & Stone, P. (2011). Empowerment for continuous agent—environment systems. Adaptive Behavior, 19(1), 16-39.
[9] Karri, N. (2022). Leveraging Machine Learning to Predict Future Storage and Compute Needs Based on Usage Trends. International Journal of AI, BigData, Computational and Management Studies, 3(2), 89-98. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P109
[10] Östlund, U., Kidd, L., Wengström, Y., & Rowa-Dewar, N. (2011). Combining qualitative and quantitative research within mixed method research designs: a methodological review. International journal of nursing studies, 48(3), 369-383.
[11] Onwuegbuzie, A. J., & Leech, N. L. (2010). Generalization practices in qualitative research: A mixed methods case study. Quality & Quantity, 44(5), 881-892.
[12] Wieringa, R. (2014). Empirical research methods for technology validation: Scaling up to practice. Journal of systems and software, 95, 19-31.
[13] Karri, N. (2022). Predictive Maintenance for Database Systems. International Journal of Emerging Research in Engineering and Technology, 3(1), 105-115. https://doi.org/10.63282/3050-922X.IJERET-V3I1P111
[14] Van Veldhoven, Z., Alaswad, A., Barrett, S., Robinson, M. R., & Vanthienen, J. (2021). Digital Transformation in the Property and Casualty Insurance Industry. International Journal of Trade, Economics and Finance, 138-143.
[15] Adavelli, R. T. M. S. R. (2021). Digital Privacy in P&C Claims Processing: Balancing Innovation with Regulatory Requirements.
[16] Karri, N. (2023). ML Models That Learn Query Patterns and Suggest Execution Plans. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 133-141. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P115
[17] Catlin, T., Lorenz, J. T., Nandan, J., Sharma, S., & Waschto, A. (2018). Insurance beyond digital: The rise of ecosystems and platforms. McKinsey & Company, 10, 2018.
[18] Sklyar, A., Kowalkowski, C., Sörhammar, D., & Tronvoll, B. (2019). Resource integration through digitalisation: a service ecosystem perspective. Journal of Marketing Management, 35(11-12), 974-991.
[19] Karri, N., & Pedda Muntala, P. S. R. (2023). Query Optimization Using Machine Learning. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 109-117. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P112
[20] Rueckel, D., Muehlburger, M., & Koch, S. (2020). An updated framework of factors enabling digital transformation. Pacific Asia Journal of the Association for Information Systems, 12(4), 1.
[21] Greineder, M., Riasanow, T., Böhm, M., & Krcmar, H. (2020). The generic InsurTech ecosystem and its strategic implications for the digital transformation of the insurance industry. In 40 Years EMISA 2019 (pp. 119-132). Gesellschaft für Informatik eV.
[22] Karri, N., & Pedda Muntala, P. S. R. (2022). AI in Capacity Planning. International Journal of AI, BigData, Computational and Management Studies, 3(1), 99-108. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P111
[23] Radwan, S. M. (2019). The Impact of digital Technologies on Insurance Industry in light of digital transformation. Blom Egypt investments and Insurance Brokerage & Consultancy, 2, 1-87.
[24] Alkurd, R., Abualhaol, I., & Yanikomeroglu, H. (2020). Big-data-driven and AI-based framework to enable personalization in wireless networks. IEEE Communications Magazine, 58(3), 18-24.
[25] Kutnjak, A., Pihiri, I., & Furjan, M. T. (2019, May). Digital transformation case studies across industries–literature review. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1293-1298). IEEE.
[26] Karri, N., Pedda Muntala, P. S. R., & Jangam, S. K. (2025). Predictive Performance Tuning. International Journal of Emerging Research in Engineering and Technology, 2(1), 67-76. https://doi.org/10.63282/3050-922X.IJERET-V2I1P108
