Leveraging AI for Automated Insurance Policy Pricing
Abstract
Global economic, social, political, and environmental changes are transforming the operating frameworks of different sectors, including the financial services industry, among them the insurance sector. Traditional business practices are increasingly pressed by a high-tech environment. The insurance industry is being directly revolutionized from multiple angles, such as digital distribution, product innovation, underwriting, and so on. A trend might involve automating processes that have traditionally been performed manually, with relevant implications in the pricing process. It is in this scenario that the theme of this essay is set, which addresses the automated pricing of insurance policies by leveraging artificial intelligence.
In general, insurance pricing refers to the activity of defining the annual premium that the client should pay. In the highly competitive insurance market, this process must also guarantee that the price of the coupled policy is accurate. The application of AI brings, on the one hand, the possibility of improving the accuracy of this process, reducing the possibilities of incorrect policies and reducing complaints. On the other hand, the possibility of competing in other segments, given the increasing level of software and electronic components used in cars, yachts, and industrial machines, becomes an opportunity and not only a simple necessity. In order to achieve these goals, in general, we answer the following questions: (1) How to be profitable in a highly complex and diversified environment? (2) Are there hidden parameters that have not been considered or quantified enough? Management in the insurance sector seems to be closely linked to an 'interpretation' approach while instead leveraging artificial intelligence might provide a complementary approach. Finally, research can contribute to achieving the managerial objectives of increased customer satisfaction and switching from selling insurance policies to offering protection that encompasses the dynamics of digitalization.
References
S. Kumari, “AI-Driven Cloud Transformation for Product Management: Optimizing Resource Allocation, Cost Management, and Market Adaptation in Digital Products ”, IoT and Edge Comp. J, vol. 2, no. 1, pp. 29–54, Jun. 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.
J. Singh, “The Future of Autonomous Driving: Vision-Based Systems vs. LiDAR and the Benefits of Combining Both for Fully Autonomous Vehicles ”, J. of Artificial Int. Research and App., vol. 1, no. 2, pp. 333–376, Jul. 2021
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.
Ahmed Qureshi, Hamza, et al. “The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis.” International Journal for Multidisciplinary Research, vol. 6, no. 4, 14 Aug. 2024, pp. 1–21.
Singh, Jaswinder. "Deepfakes: The Threat to Data Authenticity and Public Trust in the Age of AI-Driven Manipulation of Visual and Audio Content." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 428-467.
Tamanampudi, Venkata Mohit. "Autonomous AI Agents for Continuous Deployment Pipelines: Using Machine Learning for Automated Code Testing and Release Management in DevOps." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 557-600.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
