Dual‐Rule Logical Neural Network for Explainable Credit Scoring

ABSTRACT

Credit scoring requires modeling complex nonlinear relationships between borrower characteristics and default risk, yet highly expressive predictive models often suffer from limited transparency and weak generalization on small‐scale financial datasets. To address this challenge, this study proposes an interpretable Dual‐Rule Logical Neural Network (DLNet) for credit risk assessment. DLNet first converts continuous features into explicit binary threshold conditions, and then learns risk patterns through differentiable logical operators that explicitly construct conjunctive and disjunctive rule structures. To further improve rule compactness and readability, an explicit logic‐level sparse regularization term is imposed on the continuous logical weights during training, encouraging concise logical connections without penalizing the final prediction layer. In addition, a local rule backtracking extractor is developed to generate instance‐level explanations by tracing the top‐contributing activated logical nodes back to their corresponding threshold or categorical conditions. Experiments on four real‐world credit datasets show that DLNet achieves competitive predictive performance while providing both global rule patterns and local sample‐specific rationales. The learned rules further reveal heterogeneous logical structures across datasets, with disjunctive, conjunctive, and atomic patterns all appearing among high‐contribution rules. These results demonstrate that DLNet offers a practical framework for combining predictive effectiveness, structural sparsity, and decision transparency in credit scoring.