Reinforcement Learning in Credit Scoring and Underwriting
This is an incremental improvement for credit scoring and underwriting, addressing domain-specific challenges with RL adaptations.
The paper tackles the problem of credit underwriting by proposing a reinforcement learning framework, showing that new algorithms outperform traditional methods in simulations when data aligns with the model, though limitations arise in complex scenarios.
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.