The Dual Quest for Interpretability and Performance in Credit Scoring via Spline-Rule Ensembles

Credit risk estimation is crucial for financial institutions to minimize defaults and maximize profitable opportunities. Traditional credit scoring models, such as Logistic Regression, offer high interpretability but may lack predictive performance, while complex models like Random Forest provide better accuracy but lack transparency. This paper introduces spline-rule ensembles as a novel approach in credit scoring, combining strengths of tree ensembles and linear models to obtain a high-performing, structurally interpretable model. Three variants using different tree generation methods are benchmarked against their conventional rule ensemble counterparts and other classifiers. Results indicate that spline-rule ensembles outperform traditional interpretable classifiers, and compete favorably with state-of-the-art models. Additionally, spline-rule ensembles with rules generated from Boosting generally perform better than those with rules from Random Forest and Bagging. A meta-learner identifies factors driving their superior performance, and a case study highlights their interpretability advantage over tree ensembles.