Related papers: Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

URL: http://arxiv.org/abs/2203.03003v1
Date: Sun, 6 Mar 2022 16:32:53 GMT
Title: Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit
Authors: Raad Khraishi and Ramin Okhrati
Abstract summary: 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.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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