Abstract :

Generative Artificial Intelligence (GenAI) Is Rapidly Transforming The Insurance Industry By Enabling Intelligent Automation, Predictive Decision-making, And Customer-centric Service Innovation. This Study Explores The Role Of Generative AI In Enhancing Fraud Detection, Hyper-personalization, And Operational Intelligence Within Modern Insurance Ecosystems. The Primary Objective Of The Research Is To Analyze How GenAI-driven Frameworks Improve Insurance Efficiency, Customer Engagement, Claims Automation, And Predictive Risk Assessment. The Study Adopts A Qualitative And Exploratory Research Methodology Supported By Predictive Analytics And Synthetic Data Modelling Techniques. A Publicly Available Insurance Dataset Consisting Of 1,338 Records Was Utilized And Further Augmented Using Conditional Tabular Generative Adversarial Networks (CTGAN) To Generate Statistically Realistic Synthetic Data For Enhanced Model Training And Scalability. Multiple Machine Learning And Ensemble Learning Algorithms, Including Linear Regression, Decision Trees, Random Forest, Gradient Boosting, And XGBoost, Were Evaluated For Insurance Charge Prediction And Intelligent Risk Assessment. Among These Models, XGBoost Achieved The Highest Predictive Performance With An R² Score Of 0.8804, Demonstrating Superior Capability In Handling Complex Nonlinear Insurance Data. The Findings Reveal That Generative AI Significantly Improves Fraud Detection Accuracy, Operational Efficiency, And Personalized Customer Engagement Through Explainable AI-driven Recommendations, Intelligent Virtual Assistants, Automated Claims Processing, And Dynamic Underwriting Systems. The Integration Of Predictive Analytics And Hyper-personalization Enables Insurers To Deliver Customer-specific Policy Recommendations, Faster Claim Settlements, And Enhanced Service Responsiveness. Furthermore, The Study Highlights The Strategic Role Of Operational Intelligence In Streamlining Workflows, Reducing Administrative Burden, And Enabling Data-driven Insurance Decision-making.

Published:

09-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1016-1035

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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