AI-Enhanced Predictive Analytics for Insurance Claim Frequency

Authors

  • Dr. Heejin Choi Professor of Computer Science, Gwangju Institute of Science and Technology (GIST) Author

Keywords:

Predictive Analytics, Insurance Claim

Abstract

This paper is concerned with AI-enhanced predictive analytics, which has been penetrating diverse areas of life, firms' activities, and public and private organizations for the last two decades. In the insurance business, AI technologies have been getting ever deeper integration with the peculiar characteristics of the various sectors of the industry, with the aim of rewriting the traditional paradigms of operations and decision-making. Understanding the grounding principles and the issues of predictive analytics, possibly enhanced by AI technologies, is of paramount importance not only for researchers but also primarily for practitioners. In this paper, we focus specifically on one dimension of AI-PA, that is, AI-PA for insurance claim frequency.

Downloads

Download data is not yet available.

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.

Downloads

Published

15-11-2023

How to Cite

[1]
D. H. Choi, “AI-Enhanced Predictive Analytics for Insurance Claim Frequency”, J. of Artificial Int. Research and App., vol. 3, no. 2, pp. 696–708, Nov. 2023, Accessed: Nov. 14, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/277

Similar Articles

1-10 of 65

You may also start an advanced similarity search for this article.