Empirical Study on Regulatory Sandbox Application Based on Simulation and Reinforcement Learning
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Yuxuan Yang
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. This paper addresses the challenge of risk pricing in commercial banks amid increasing systemic risks influenced by global economic fluctuations, policy adjustments, and major global events. We introduce a novel framework combining digital twin technology and deep reinforcement learning to aid in more effective interest rate pricing decisions. By constructing a digital twin environment that simulates the operational conditions of commercial banks under various scenarios, and employing deep reinforcement learning models, the framework aims to devise optimal interest rate strategies that align with the banks' objectives. Our empirical analyses demonstrate the superiority of this AI-driven approach over traditional expert pricing methods, offering a robust decision support system for managing risk pricing in commercial banks.
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Yuxuan Yang
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References:
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