Evaluating the Impact of Generative Models in Personalized Insurance Product Design

Authors

Adarsh Naidu

Abstract

This paper investigates the application of Generative Adversarial Networks (GANs) in developing personalized insurance products. We propose a novel framework that leverages GANs to generate synthetic customer profiles and simulate customized insurance offerings that optimize both customer satisfaction and insurer profitability. The framework was tested on a dataset of 500 insurance customers with diverse demographic and behavioural characteristics. Our results demonstrate a 24% increase in predicted customer satisfaction and a 17% improvement in projected policy retention rates compared to traditional product development approaches [5]. Additionally, the model projects an 11% increase in profitability through more precise risk assessment and product matching [6]. These findings suggest that generative modeling techniques can significantly enhance the insurance industry's ability to develop targeted products that better serve individual customer needs while maintaining sound business practices. We further validate our approach through extensive sensitivity analysis, demonstrating the model's robustness across various customer segments and market conditions. The implications extend beyond immediate business metrics to broader industry transformation potential, suggesting a paradigm shift in how insurance products can be conceptualized and delivered [2][3].

Keywords

Evaluating the Impact of Generative Models in Personalized Insurance Product Design. Adarsh Naidu. 2021. IJIRCT, Volume 7, Issue 5. Pages 1-14. https://www.ijirct.org/viewPaper.php?paperId=2503055