Citation:

Devidas Kanchetti, 2022. "Navigating Regulatory Challenges in Data-Driven Insurance: Strategies for Compliance and Innovation", ESP Journal of Engineering & Technology Advancements 2(3): 85-101.

Abstract:

The advancement of technology and the increase in the data, which increases at a geometric rate, have influenced the insurance industry, leading to a concept known as Data Driven insurance, which comes with numerous benefits to the insurers, including the development of new insurance products tailored for individual customers, risk assessment and management, and customer experience enhancement. Nevertheless, these improvements bring about enormous issues of the law. Some of the emerging concerns that insurers are faced with include Data privacy/ethics and regulations on new technologies, including Artificial Intelligence (AI) and Machine Learning (ML). The purpose of this paper is to discuss the topic of data-driven insurance combined with the regulator's guidelines and the consideration of strategies that insurers can take to be in line with the regulatory requirements while promoting innovation. Where necessary, a literature review is conducted to determine the current state of regulation, general data protection laws and ethics. This paper utilizes case study approaches as well as regulatory policy analysis to investigate how insurers can conform to rules such as the General Data Protection Regulation and the California Consumer Privacy Act. The paper also assesses the consequence of using the AI-driven solution with the recommendation of a clear algorithm, ethical use of the work done by the AI system, and data control. The discussion is about the prospects and challenges for the insurers, and the final point consists of the guidelines and tips for compliance and innovation. The main issue for insurers looking to embrace big data analytics while also dealing with the tight regulation they face. Based on the analysis, this paper contends that effective engagement with the regulators, implementing ethical AI practices and mitigating data usage opaque approaches are crucial to the thorny task of dealing with the peculiarity of the regulations.

References:

[1] Voigt, P., & Von demBussche, A. (2017). The EU General Data Protection Regulation (GDPR): A Practical Guide. Springer International Publishing.

[2] Khatri, V., & Brown, C. V. (2010). Designing Data Governance. Communications of the ACM, 53(1), 148-152.

[3] Weber, K., Otto, B., &Österle, H. (2009). One Size Does Not Fit All—A Contingency Approach to Data Governance. Journal of Data and Information Quality, 1(1), 1-27.

[4] Zarsky, T. Z. (2016). Incompatible: The GDPR in the Age of Big Data. Seton Hall Law Review, 47, 995.

[5] Coglianese, Cary, and David Lehr. "Regulating by robot: Administrative decision making in the machine-learning era." Geo. LJ 105 (2016): 1147.

[6] Swedloff, R. (2020). The new regulatory imperative for insurance. BCL Rev., 61, 2031.

[7] Henckaerts, R., Antonio, K., Clijsters, M., & Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018(8), 681-705.

[8] Doyle, E., McGovern, D., & McCarthy, S. (2014). Compliance–innovation: integrating quality and compliance knowledge and practice. Total Quality Management & Business Excellence, 25(9-10), 1156-1170.

[9] Korea Development Institute. (2021). Case Studies on the Regulatory Challenges Raised by Innovation and the Regulatory Responses. OECD Publishing.

[10] Kabanov, I. (2016, December). Effective frameworks for delivering compliance with personal data privacy regulatory requirements. In 2016 14th Annual Conference on Privacy, Security and Trust (PST) (pp. 551-554). IEEE.

[11] Pnevmatikakis, A., Kanavos, S., Matikas, G., Kostopoulou, K., Cesario, A., & Kyriazakos, S. (2021). Risk assessment for personalized health insurance based on real-world data. Risks, 9(3), 46.

[12] Thapa, C., & Camtepe, S. (2021). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in biology and medicine, 129, 104130.

[13] Kar, A. K., & Navin, L. (2021). Diffusion of blockchain in insurance industry: An analysis through the review of academic and trade literature. Telematics and Informatics, 58, 101532.

[14] Haddad, M. I., Williams, I. A., Hammoud, M. S., & Dwyer, R. J. (2020). Strategies for implementing innovation in small and medium-sized enterprises. World journal of entrepreneurship, management and sustainable development, 16(1), 12-29.

[15] Mishchenko, S., Naumenkova, S., Mishchenko, V., & Dorofeiev, D. (2021). Innovation risk management in financial institutions. Investment Management and Financial Innovations, 18(1), 191-203.

[16] Pugnetti, C., & Seitz, M. (2021). Data-driven services in insurance: Potential evolution and impact in the Swiss market. Journal of Risk and Financial Management, 14(5), 227.

[17] Kempeneer, S. (2021). A big data state of mind: Epistemological challenges to accountability and transparency in data-driven regulation. Government Information Quarterly, 38(3), 101578.

[18] Śmietanka, M., Koshiyama, A., & Treleaven, P. (2021). Algorithms in future insurance markets. International Journal of Data Science and Big Data Analytics, 1(1), 1-19.

[19] Hanafy, M., & Ming, R. (2021). Machine learning approaches for auto insurance big data. Risks, 9(2), 42.

[20] Ho, C. W., Ali, J., & Caals, K. (2020). Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance. Bulletin of the World Health Organization, 98(4), 263.

[21] Devidas Kanchetti, 2021. "Climate Change and Insurance: Using Predictive Analytics to Navigate Emerging Risks", ESP Journal of Engineering & Technology Advancements 1(1): 184-194.

[22] Devidas Kanchetti, 2021. "The Ethics of Data Science in Insurance: Balancing Innovation with Privacy and Fairness", ESP Journal of Engineering and Technology Advancements 2(1): 86-99.

Keywords:

Data-Driven Insurance, Regulatory Compliance, Artificial Intelligence, Data Privacy, GDPR, CCPA, Ethical AI, Data Governance.