EXPLORING THE ROLE OF SYNTHETIC DATA GENERATION IN TRAINING ROBUST INSURANCE MODELS AND MITIGATING DATA PRIVACY CONCERNS

Abstract

The insurance industry's growing dependence on data-driven models highlights the need  for practical ways to resolve data privacy issues and improve model resilience. With an  emphasis on techniques like Generative Adversarial Networks (GANs) and Variational  Autoencoders (VAEs), this study explores the use of synthetic data creation in training  insurance models. Synthetic data delivers equivalent efficacy while resolving privacy  concerns, according to the research, which compares the performance of models trained  with synthetic data to those taught with actual data. Synthetic data reduces the risks  involved with managing sensitive information by using strategies including  anonymization, de-identification, and differential privacy. The findings imply that  synthetic data might be used as a useful instrument to improve model accuracy and data  privacy in the insurance industry. The results demonstrate how synthetic data may be  used to strike a compromise between privacy and data value, encouraging safer and more  effective data management techniques.