Reinforcement Learning-Based Dynamic Pricing Strategy for Life Insurance in a Low-Interest Rate Environment

Zi-Jia Yi ()
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Zi-Jia Yi: Central University of Finance and Economics

A chapter in Proceedings of the 2026 3rd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2026), 2026, pp 524-534 from Springer

Abstract: Abstract Under the low-interest rate environment, the life insurance industry’s interest margin loss risk accumulates continuously, and traditional static and heuristic threshold pricing struggle to balance risk control, customer retention and enterprise benefits. Taking whole life insurance as the research object, this paper constructs an MDP-DQN dynamic pricing model by depicting the pricing sequential decision-making via Markov Decision Process (MDP) and solving the high-dimensional state problem with Deep Q-Network (DQN). Based on 2013–2025 10-year Treasury bond yields, a simulated policy pool is built for three pricing strategies comparison. The results show that compared with the traditional static pricing strategy, the model reduces interest margin loss by 64.3% and increases new business value by 29.6%; compared with the heuristic threshold pricing strategy, it cuts loss by 54.5% and boosts value by 65.3%, with customer churn rate stabilized within 5%, providing an effective technical solution for the industry to cope with low-interest rate risks.

Keywords: Low-Interest Rate Environment; Dynamic Life Insurance Pricing; MDP; DQN; Interest Margin Loss (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-672-2_50

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DOI: 10.2991/978-94-6239-672-2_50

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