Abstract: Due to the limitations of traditional statistical models for credit risk assessment,machine learning techniques have significantly enhanced model accuracy and predictive capabilities.However,the complexity and opacity pose significant challenges in terms of interpretation.To address this issue,this paper introduces an interpretable machine learning model for credit risk assessment that integrates the attention mechanism with tree ensemble rule extraction approach.This model automatically identifies complex nonlinear relationships within the data,extracts a large number of interpretable rules from the trained tree ensemble model,encodes these rules into new feature variables,and inputs them into an attention neural network to obtain attention weights for each rule.Subsequently,based on the attention weights,objective function,and constraints,the model balances the predictive performance,stability,and interpretability of the rule subset.The optimal rule subset can be derived in O(n) time.Experimental results,based on three public datasets,demonstrate that the proposed approach not only maintains high predictive accuracy but also substantially enhances the model's interpretability.

Key words: Interpretable machine learning,Credit risk assessment,Attention mechanism,Rule generation algorithm,Tree ensemble models