AI Governance in Regulated Cloud-Native Insurance Platforms
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P111Keywords:
AI Governance, Cloud-Native Insurance, Regulatory Compliance, Explainable AI, Zero Trust Architecture, Data Governance, Claims Automation, Federated AIAbstract
Artificial Intelligence (AI) and cloud-native architectures are becoming increasingly critical to transforming the insurance industry, allowing insurers to update their legacy systems, process claims faster, and become more compliant with regulatory requirements. This paper discusses the importance of AI governance in regulated cloud-native insurance platforms and how governance frameworks can guarantee fairness, transparency, accountability, and privacy of AI-based decision-making. Kubernetes, service mesh frameworks, and event-driven architecture are examples of cloud-native technologies that can facilitate systems to scale services, incorporate AI modules, and ensure high availability, while also integrating governance into their design. Examining 2023 case studies in this study reveals that AI governance across insurers results in a 65% reduction in the time taken to settle claims, a 40% decrease in operational expenditure, and a more effective method of fraud identification, with strict compliance with GDPR and other requirements. The study also notes emerging frameworks, including Zero Trust Architectures, explainable AI, and automated regulatory reporting, which make governance a force, rather than a compliance burden, resulting in increased trust and innovation. Moreover, moral and social issues, such as algorithmic bias, fairness, and accountability, are also discussed in the context of the insurance business. Finally, future trends and developments that are of interest regarding governance over digital insurance ecosystems, including federated AI, regulatory sandbox, and adaptive policy, are touched on
References
1. Lior, A. (2021). Insuring AI: The role of insurance in artificial intelligence regulation. Harv. JL & Tech., 35, 467.
2. Zeier Röschmann, A., Erny, M., & Wagner, J. (2022). On the (future) role of on-demand insurance: Market landscape, business model and customer perception. The Geneva Papers on Risk and Insurance-Issues and Practice, 47(3), 603-642.
3. Lechterman, T. M. (2022). The concept of accountability in AI ethics and governance. The Oxford Handbook of AI Governance, 164-182.
4. Gatzert, N., & Maegebier, A. (2015). Critical illness insurance: challenges and opportunities for insurers. Risk Management and Insurance Review, 18(2), 255-272.
5. De Almeida, P. G. R., Dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505-525.
6. Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd.
7. Haider, A. (2014, August). Asset lifecycle data governance framework. In Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012) (pp. 287-296). Cham: Springer International Publishing.
8. Lemieux, V. (Ed.). (2012). Financial analysis and risk management: Data governance, analytics and life cycle management. Springer Science & Business Media.
9. Ren, Z., Shi, J., & Imran, M. (2022). Data evolution governance for ontology-based digital twin product lifecycle management. IEEE Transactions on Industrial Informatics, 19(2), 1791-1802.
10. Higgins, S. (2012). The lifecycle of data management. Managing research data, 57-61.
11. Plate, H., Basile, C., & Paraboschi, S. (2013). Policy-driven system management. In Computer and Information Security Handbook (pp. 427-460). Morgan Kaufmann.
12. Zhang, C. A., Cho, S., & Vasarhelyi, M. (2022). Explainable artificial intelligence (XAI) in auditing. International Journal of Accounting Information Systems, 46, 100572.
13. Cihon, P., Schuett, J., & Baum, S. D. (2021). Corporate governance of artificial intelligence in the public interest. Information, 12(7), 275.
14. García-Sánchez, I. M., Rodríguez-Ariza, L., & Frías-Aceituno, J. V. (2013). The cultural system and integrated reporting. International business review, 22(5), 828-838.
15. Gatzert, N., Reichel, P., & Zitzmann, A. (2020). Sustainability Risks and Opportunities in the Insurance Industry. Zeitschrift für die gesamte Versicherungswissenschaft, 109(5), 311-331.
16. Himick, M., & Bouriaux, S. (Eds.). (1998). Securitized insurance risk: strategic opportunities for insurers and investors. Global Professional Publishing.
17. Krouse, J. H. (2015). Balancing evidence, innovation, and regulation. Otolaryngology--Head and Neck Surgery, 152(4), 579-580.
18. Sadgrove, K. (2016). The complete guide to business risk management. Routledge.
19. Leroi, E., Bonnard, C., Fell, R., & McInnes, R. (2005). Risk assessment and management. In Landslide risk management (pp. 169-208). CRC Press.
20. Zetzsche, D. A., Buckley, R. P., Barberis, J. N., & Arner, D. W. (2017). Regulating a revolution: from regulatory sandboxes to smart regulation. Fordham J. Corp. & Fin. L., 23, 31.
21. Pappula, K. K., & Anasuri, S. (2020). A Domain-Specific Language for Automating Feature-Based Part Creation in Parametric CAD. International Journal of Emerging Research in Engineering and Technology, 1(3), 35-44. https://doi.org/10.63282/3050-922X.IJERET-V1I3P105
22. Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
23. Pappula, K. K., Anasuri, S., & Rusum, G. P. (2021). Building Observability into Full-Stack Systems: Metrics That Matter. International Journal of Emerging Research in Engineering and Technology, 2(4), 48-58. https://doi.org/10.63282/3050-922X.IJERET-V2I4P106
24. Pedda Muntala, P. S. R., & Karri, N. (2021). Leveraging Oracle Fusion ERP’s Embedded AI for Predictive Financial Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(3), 74-82. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I3P108
25. Rahul, N. (2021). Strengthening Fraud Prevention with AI in P&C Insurance: Enhancing Cyber Resilience. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 43-53. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P106
26. Rusum, G. P. (2022). WebAssembly across Platforms: Running Native Apps in the Browser, Cloud, and Edge. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 107-115. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P112
27. Pappula, K. K. (2022). Architectural Evolution: Transitioning from Monoliths to Service-Oriented Systems. International Journal of Emerging Research in Engineering and Technology, 3(4), 53-62. https://doi.org/10.63282/3050-922X.IJERET-V3I4P107
28. Jangam, S. K. (2022). Self-Healing Autonomous Software Code Development. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 42-52. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P105
29. Anasuri, S. (2022). Zero-Trust Architectures for Multi-Cloud Environments. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 64-76. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P107
30. Pedda Muntala, P. S. R. (2022). Anomaly Detection in Expense Management using Oracle AI Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 87-94. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P109
31. Rahul, N. (2022). Automating Claims, Policy, and Billing with AI in Guidewire: Streamlining Insurance Operations. International Journal of Emerging Research in Engineering and Technology, 3(4), 75-83. https://doi.org/10.63282/3050-922X.IJERET-V3I4P109
