A Comprehensive Survey of the Potential of Artificial Intelligence in the Insurance Industry

Main Article Content

Wachiranun Pum
Narongsak Sukma

Abstract

This article explores the transformative potential of artificial intelligence (AI), which is rapidly and profoundly impacting the insurance industry. This study provides an in-depth survey of the intriguing trends and potential applications of AI in the insurance industry. Through a historical analysis tracing back to the limited capabilities of early AI systems for insurance in the 1990s to the sophisticated and multifaceted deployments today. The study explores examples of AI applications across various insurance domains, from underwriting, claims processing, fraud detection, to customer service.


This study aims to determine AI's potential to revolutionize risk analysis, loss prediction, and personalized risk-based pricing, highlighting cutting-edge AI innovations from leading insurance companies. Importantly, the study addresses significant challenges such as privacy concerns, technical limitations, the necessity of establishing clear AI ethics policies, along with ongoing investments in staff training and impact assessments. The efficient sustainable, and socially responsible integration of AI into the insurance industry. In summary, this work envisions a transformed insurance landscape through widespread AI integration, enabling improved decision making, enhanced fraud detection, accurate pricing, and superior customer experiences. It emphasizes the cautious governance of these innovations to maintain a balance between efficiency and social accountability. However, carefully considering ethical implications and proactive steps are essential for harnessing AI's potential while mitigating risks.

Article Details

How to Cite
Pum, W., & Sukma, N. (2024). A Comprehensive Survey of the Potential of Artificial Intelligence in the Insurance Industry. Executive Journal, 44(1), 79–96. Retrieved from https://so01.tci-thaijo.org/index.php/executivejournal/article/view/271044
Section
Academic Articles

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