Assessing the Cost of Monotonicity in Credit Risk Modeling with Gradient Boosting

This research paper explores the performance implications of incorporating monotonicity constraints in gradient boosting models, specifically for credit risk probability of default (PD) estimation. The study provides valuable insights into the trade-offs between model accuracy and constraint satisfaction, a key consideration for regulatory compliance in finance.

• •The research investigates the impact of monotonicity constraints on the performance of gradient boosting models.
• •It likely uses a multi-dataset benchmark for robust evaluation in the context of credit PD.
• •Findings are relevant to understanding model accuracy and compliance trade-offs in financial applications.