PDx: Adaptive Credit Risk Forecasting with MLOps

This paper addresses the critical issue of model degradation in credit risk forecasting within digital lending. It highlights the limitations of static models and proposes PDx, a dynamic MLOps-driven system that incorporates continuous monitoring, retraining, and validation. The focus on adaptability to changing borrower behavior and the champion-challenger framework are key contributions. The empirical analysis provides valuable insights into the performance of different model types and the importance of frequent updates, particularly for decision tree-based models. The validation across various loan types demonstrates the system's scalability and adaptability.