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Abstract
Generative AI has changed the insurance business by coming up with new ways to improve screening, speed up claims handling, and make risk assessment more accurate. Using advanced machine learning models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), generative AI lets insurers handle tasks that used to be done by hand, mimic real-life situations, and create fake data. This research investigates how generative AI is used in the insurance business and looks at its pros, cons, issues, and moral concerns. It is stressed how important it is to follow the rules, be fair, and be open about how these models are used. It is also underlined how these models may help solve long-lasting problems like lack of data, scam detection, and risk modelling. Case studies, market trends, and expert tips are used in this piece to give a thorough look at how the role of generative AI is changing in insurance.
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