Published 02-11-2023
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
The insurance business is widely dependent on risk management. The impact of artificial intelligence within insurance will, in some aspects, reshape insurance models. AI itself, however, is a risk due to the 'black box' characteristic and the challenge of regulation and policies. The present paper specifically focuses on how risk management within the insurance sector, in adopting AI technologies, is implemented. The paper concludes that an academic and public-private approach to set out an AI risks framework is a way forward.
Insurance is a business mechanism designed to spread, mitigate, and shift diverse classes of risks. Insurance is a risk business, built around the successful assessment and management of clients' risks, but predominantly, a business constructed for risk management purposes. The rapid development of big data and artificial intelligence technologies has brought various changes. The insurance industry is playing a fundamental role in pulling our economy and social welfare model through hardship. It does so by performing the basic economic function of managing risk. The essence of the insurance transformation is from traditional insurance that is reactive, transactional, and customer-focused, to preventative, partnership-based, and embracing digital ecosystems. The recent digital innovation of the insurance sector is known as InsurTech. The insurance sector is rich in AI use cases. This paper, however, focuses on the risk factor managing the adaptation of AI within the insurance industry only.
Downloads
References
- S. Kumari, “Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures ”, J. Computational Intel. & Robotics, vol. 2, no. 2, pp. 9–27, Aug. 2022
- Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
- Machireddy, Jeshwanth Reddy. "Data-Driven Insights: Analyzing the Effects of Underutilized HRAs and HSAs on Healthcare Spending and Insurance Efficiency." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 450-470.
- Singh, Jaswinder. "Social Data Engineering: Leveraging User-Generated Content for Advanced Decision-Making and Predictive Analytics in Business and Public Policy." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 392-418.
- Tamanampudi, Venkata Mohit. "AI and DevOps: Enhancing Pipeline Automation with Deep Learning Models for Predictive Resource Scaling and Fault Tolerance." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 38-77.
- J. Singh, “Combining Machine Learning and RAG Models for Enhanced Data Retrieval: Applications in Search Engines, Enterprise Data Systems, and Recommendations ”, J. Computational Intel. & Robotics, vol. 3, no. 1, pp. 163–204, Mar. 2023.
- Tamanampudi, Venkata Mohit. "AI Agents in DevOps: Implementing Autonomous Agents for Self-Healing Systems and Automated Deployment in Cloud Environments." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 507-556.