Artificial Intelligence Applicability in the Insurance Industry: A Scientometric and Content Analysis Approach

Abstract

Introduction To reduce costs, make efficient decisions, grow the market sustainably, and profit, private insurance companies must increase their computing power for big data analysis by using artificial intelligence (AI) algorithms. In this review, we build upon the existing literature on AI applications in insurance and provide a comprehensive review to identify obstacles to future research. Materials and Methods A search was conducted on the Web of Sciences (WOS) database until January 5 th , 2025. Using the terms AI and insurance, 6913 articles were extracted from the database search and they were reviewed by two experts based on the inclusion/exclusion criteria. In the end, 76 articles were included in the study and then scientometric and content analysis were carried out on them. Results Based on recent studies, the volume of scientific publications on AI applications in the insurance industry has grown significantly since 2022. China ( n = 34), the United States of America ( n = 14), Belgium ( n = 13), the United Kingdom ( n = 12), Spain ( n = 10), and Egypt ( n = 9) are the leading contributors to this research domain. The findings highlight that AI has been integrated into the insurance sector across seven major categories. However, critical research gaps remain, classified into three overarching stages: pre‐AI implementation, focusing on challenges related to data readiness, regulatory compliance, and organizational preparedness; AI application areas, addressing the scope, effectiveness, and ethical concerns of AI‐driven solutions; and post‐AI implementation, examining long‐term impacts, performance evaluations, and continuous improvements. To bridge these gaps, future research should explore these three stages in depth, ensuring a more comprehensive and sustainable integration of AI in the insurance industry. Conclusion In today’s competitive market, insurance managers should be aware of how AI can help organizations provide innovative services and achieve valuable results. Therefore, future research should leverage the gaps identified in this study to introduce new and innovative algorithms for insurance data analysis in the modern world, thereby increasing profitability and reducing costs for insurance companies.

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