  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.
