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Abstract
The increasing reliance on data-driven models in the insurance industry underscores the need for effective solutions to address data privacy concerns and enhance model robustness. This research investigates the role of synthetic data generation in training insurance models, focusing on methods such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The study evaluates the performance of models trained with synthetic data compared to those trained with real data, finding that synthetic data offers comparable effectiveness while addressing privacy issues. By employing techniques such as anonymization, de-identification, and differential privacy, synthetic data helps mitigate risks associated with handling sensitive information. The results suggest that synthetic data can serve as a practical tool for enhancing data privacy and improving model accuracy in the insurance sector. The findings highlight the potential of synthetic data to balance data utility with privacy, promoting more secure and efficient data management practices.
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