Reinforcement Learning for Household Finance : Designing Policy via Responsiveness

In a Q-learning model-free setting, the mortgage servicer can learn the default incentive of the borrower from soft information and responsiveness during communication; and in turn undertake appropriate actions to maximize her reward. This reward maximization incentivizes the effort of the servicer and the loan outcome for the borrower via this optimal policy is not worse off. By implementing the optimal policy, the servicer can preempt borrower's adversarial behavior, thereby increasing borrower cooperation. Our approach differs from the conventional approach which depends on the qualitative legal and industry expert judgement. We also provide evidence that past experiences are not the dominant deciding factors for the optimal action of the servicer in a high learning environment