Reinforcement Learning in Credit Scoring and Underwriting

• URL: http://arxiv.org/abs/2212.07632v2
• Date: Thu, 27 Jun 2024 02:32:42 GMT
• Title: Reinforcement Learning in Credit Scoring and Underwriting
• Authors: Seksan Kiatsupaibul, Pakawan Chansiripas, Pojtanut Manopanjasiri, Kantapong Visantavarakul, Zheng Wen,
• Abstract summary: We adapt reinforcement learning principles for credit scoring, incorporating action space renewal and multi-choice actions. We introduce two new RL-based credit underwriting algorithms to enable more informed decision-making.
• Score: 7.356954349107956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 model. However, complex situations highlight model limitations, emphasizing the importance of powerful machine learning models for optimal performance. Future research directions include exploring more sophisticated models alongside efficient exploration mechanisms.

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