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.
@article{arxiv.2203.03003,
title = {Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit},
author = {Raad Khraishi and Ramin Okhrati},
journal= {arXiv preprint arXiv:2203.03003},
year = {2022}
}