Reinforcement Learning Approaches for Pricing Condo Insurance Policies
DOI:
https://doi.org/10.5281/zenodo.16410428Keywords:
Reinforcement Learning In Finance, Sequential Decision Problems, Dynamic Pricing Challenges, Combinatorial Optimization With RL, Condo Insurance Pricing Models, Dyna And Optimal-Dyna Methods, Multi-Agent Reinforcement Learning, Hierarchical RL Techniques, Pricing Policy Modeling Constraints, Dynamic System Behavior In Insurance, Agent-Influenced Dynamic Models, System-Wide Movement Balancing, Financial Applications Of RL, Novel RL Paradigms In Pricing, Insurance Policy Optimization, RL Versus Traditional Optimization, Modeling Gaps In Pricing Policies, Simulation-Based Results Comparison, Future Research In Dynamic Pricing, RL For Complex Financial Systems.Abstract
Reinforcement Learning has arisen as a popular novel approach to solving sequential problems where multiple technical challenges converge, especially in the Finance industry. Dynamic pricing, as a consequence of the combination of several logistic and dynamically driven real-world events, is a major issue in Finance and has gained notoriety in the past decades. Reinforcement Learning Paradigms aim to solve intricate and complex combinatorial optimization Problems without the burden of proximate optimization heuristics. These inherently Related Problems make RL useful for tasks that include dynamic pricing, among others. The purpose of this chapter is to explore the use of Reinforcement Learning in the Problem of Pricing Condo Insurance Policies, which present additional modeling constraints that are unique. It concludes with a simulated comparison of results, as well as avenues for future work, such as applying hierarchies and multi-agent influenced techniques that can leverage the underlying dynamics of the system.
We propose three main questions: Are there RL paradigms that yield better solutions than prior works? And, can we utilize gaps in the modeling of pricing policies that help reinforcement learning paradigms? We explore the cases of Dyna and Optimal-Dyna models. However, it is still questionable if RL approaches can keep up with recent techniques. Finally, we propose some dynamic models based on agent influenced approaches that open a research avenue if support the balancing effort of system-wide collisions and movements.
