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.

翻译：本文提出了一种新颖的强化学习（RL）框架，用于解决信用授信中不可泛化的情境挑战。我们将强化学习原理应用于信用评分，引入了动作空间更新与多选择动作机制。研究表明，传统授信方法等价于强化学习中的贪心策略。我们提出了两种基于强化学习的信用授信新算法，以实现更明智的决策。仿真实验表明，在数据符合模型假设的场景下，这些新方法优于传统方法。然而，复杂情境凸显了模型的局限性，强调了采用强大机器学习模型以实现最优性能的重要性。未来的研究方向包括探索更复杂的模型以及高效的探索机制。