Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

Raad Khraishi and Ramin Okhrati

Papers from arXiv.org

Abstract: We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and requires no assumptions on the functional form of demand. Using both real and synthetic data on consumer credit applications, we demonstrate that our approach using the conservative Q-Learning algorithm is capable of learning an effective personalized pricing policy without any online interaction or price experimentation.

Date: 2022-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2203.03003

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