AI-Powered Predictive Health Risk Management in Insurance Plans: A Multi-Agent System Approach for Personalized Policy Customization

Abstract

AI-powered predictive health risk management is transforming insurance by enabling personalized policy customization through 
advanced analytics and automation. This study explores the integration of a Multi-Agent System (MAS) approach, where AI-driven 
agents analyze vast health datasets, assess individual risk factors, and tailor insurance plans based on predictive modeling. Machine 
learning algorithms enhance risk stratification, enabling insurers to offer dynamic pricing, preventive healthcare incentives, and real
time policy adjustments. The MAS framework facilitates efficient data exchange, ensuring transparency, compliance, and ethical AI 
governance in insurance decision-making. Cloud computing and secure data pipelines further enhance scalability and privacy while 
optimizing underwriting processes. This research highlights the benefits, challenges, and future prospects of AI-driven predictive 
analytics in insurance, emphasizing its potential to improve risk assessment accuracy, enhance customer experience, and promote 
proactive healthcare management in a rapidly evolving insurance landscape.