Reinforcement Learning in Credit Scoring and Underwriting

arXiv:2212.07632v2 Announce Type: replace-cross
Abstract: This paper proposes a novel reinforcement learning (RL) framework for credit underwriting that tackles ungeneralizable contextual challenges. We adapt RL principles for credit scoring, incorporating action space renewal and multi-choice actions. Our work demonstrates that the traditional underwriting approach aligns with the RL greedy strategy. We introduce two new RL-based credit underwriting algorithms to enable more informed decision-making. Simulations show these new approaches outperform the traditional method in scenarios where the data aligns with the …

abstract action adapt arxiv challenges credit cs.lg framework multi novel paper reinforcement reinforcement learning replace scoring space stat.ml strategy type underwriting work