Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

We introduce a method for pricing consumer credit using recent advances in offline deep reinforcement learning. This approach relies on a static dataset and as opposed to commonly used pricing approaches it 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. In particular, using historical data on online auto loan applications we estimate an increase in expected profit of 21% with a less than 15% average change in prices relative to the original pricing policy.