Applying Reinforcement Learning to Optimize Healthcare Insurance Premium Pricing

Abstract

In recent years, reinforcement learning (RL) has garnered increasing attention for its applications in various domains, including finance, robotics, and healthcare. One critical area in healthcare where RL has shown potential is the optimization of insurance premium pricing. Traditional pricing models in healthcare insurance often rely on a combination of actuarial calculations and statistical methods. However, these methods can be limited by a lack of dynamic adaptability to the constantly changing nature of healthcare risks. This article explores the potential for using RL to optimize healthcare insurance premium pricing, offering a detailed review of existing models, the challenges in the domain, and the fundamentals of reinforcement learning. Additionally, it provides insights into how RL can be integrated into pricing strategies, discusses various methodologies, and highlights the potential improvements in accuracy, efficiency, and adaptability.