Paper ID: 2203.03003

Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

Raad Khraishi, Ramin Okhrati

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.

Submitted: Mar 6, 2022