Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning

arXiv:2306.15585v2 Announce Type: replace-cross
Abstract: Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. Because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining …

abstract adversarial arxiv banking credit cs.lg environments games management operations portfolio q-fin.gn reinforcement reinforcement learning stochastic study test type video video games