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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67914
Volume:
10 |
10 |
Oct 2026
Pages:
1-14
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Bachelor of Business Administration
KL University
Bachelor of Business Administration
KL University
Bachelor of Business Administration
KL University
Artificial Intelligence has become a buzzword across industries, and insurance is no exception, with applications in customer service, claims processing, underwriting and fraud detection. However, many existing studies focus on individual applications. This study examines the relationship between AI adoption and perceived operational efficiency in the insurance industry, considering AI chat bots, claims automation, AI underwriting, fraud detection and AI-based customer service. Operational efficiency is measured through processing speed, accuracy and response time, among other dimensions. The research design was quantitative and cross-sectional, and data were gathered with a structured questionnaire on a five-point Likert scale. Convenience sampling was used to reach college students and insurance policyholders; 400 questionnaires were distributed and 78 completed responses were returned. Microsoft Excel was used for descriptive statistics, Cronbach’s alpha, Pearson correlation, chi-square tests, one-way ANOVA and linear regression. The findings show a strong positive relationship between AI adoption and operational efficiency, with AI adoption accounting for 64.6% of the variance in operational efficiency; the null hypothesis was therefore rejected. Among the individual applications, fraud detection was the strongest predictor once it and AI-based customer service were added to the model, which increased explanatory power. Familiarity with AI and age were associated with perceptions of operational efficiency, whereas gender and prior insurance use were not. The main barriers to adoption were data privacy and security, lack of AI skills, and cost. Overall, the study provides empirical evidence from Indian respondents on the relationship between AI adoption and perceived operational efficiency. The results should be interpreted with caution given the small, non-probability sample, the predominance of young undergraduate respondents, and the use of perception-based measures from a single questionnaire.
KEYWORDS
This study analyzed the relationship between AI adoption and operational efficiency in the insurance sector using survey data from 78 respondents. Perceived operational efficiency was strongly and significantly related to AI adoption (R² = .646, β = .804). AI chatbots, claims automation and AI underwriting were each significant predictors of perceived efficiency, and fraud detection was also a strong predictor when added to the model. Respondents with greater knowledge of AI, especially of AI in insurance, perceived greater efficiency. Data privacy and security, lack of skills and cost were the main perceived barriers. The null hypothesis of no impact was rejected. Because the results rest on a small convenience sample and perceptual measures, they should be regarded as preliminary empirical evidence of a relationship between AI adoption and more efficient insurance operations, to be verified with larger samples and objective performance measures.
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Publication Date
10/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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