Al-Augmented Risk Prediction in Health Insurance

Abstract—Risk prediction is central to the underwriting and pricing processes for health insurers. While traditional actuarial models are built around statistical robustness, they struggle to model complex, non-linear associations between different risk factors. In this paper, we propose an audience awareness AI augmented framework for the risk prediction in health insurance using the Machine Learning algorithms for improving prediction accuracy. Based on structured health data, electronic health records, and socio-demographic variables, we compare several models: Random Forest, Gradient Boosting, Deep Neural Networks. Our findings indicate a marked enhancement in predictive performance over conventional logistic regression models and highlight the potential of Artificial Intelligence as a revolutionary asset in the health insurance sector.