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

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  • Zi-Jia Yi

    (Central University of Finance and Economics)

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.

Suggested Citation

  • Zi-Jia Yi, 2026. "Reinforcement Learning-Based Dynamic Pricing Strategy for Life Insurance in a Low-Interest Rate Environment," Advances in Economics, Business and Management Research,, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-672-2_50
    DOI: 10.2991/978-94-6239-672-2_50
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