Optimizing debt collection actions using markov decision process - Abad, Kimberly C..
ABSTRACT

In debt collection, several actions are undertaken by creditors and collection agencies in order to attempt to make contact with the debtor. These series of collection actions may not necessarily results in an immediate response from the debtor, but they can affect and improve the result of succeeding actions. Strategic optimization of these collection activities allows the creditor and the collection agency to profile their customers or debtors and perform targeted campaigns that will elicit high response from the debtor, as well as improve on debt resolution. This paper aims to tackle the multi-step optimization problem in debt collection through reinforcement learning (RL) as presented in a Markov Decision Process (MDP) framework. The MDP is composed of the state space as defined by feature variables on the debt and the debtor, the action space that include main collection actions and the reward signal being the contribution margin. The action-value function is estimated using a fitted Q iteration algorithm where supervised learning models are used including an ensemble regression tree, classical linear and logistic regression. The random forest has provided the highest level of accuracy in estimating the action-value function of the MDP model. Furthermore, the recommendation actions from the MDP model have resulted in an improvement in the current contribution margin of the collection agency.
ay 2023.
hesis (M.S. Statistics)--University of the Philippines, Diliman.
Keywords: Debt collection; Markov decision process; Reinforcement learning