Evolutionary Rule Extraction from Corporate Default Prediction Models

2026-05-28

AI Summary

This study addresses the challenge of balancing predictive performance and interpretability in default prediction for small and medium-sized enterprises (SMEs), where complex models often fall short of regulatory and transparency requirements. The authors propose DEXiRE-EVO, a novel framework that uniquely integrates multi-objective evolutionary algorithms with Contextual Importance and Utility (CIU) analysis to simultaneously optimize prediction accuracy and generate highly interpretable, economically meaningful decision rules. Empirical evaluation on a dataset of 50,718 Italian SMEs demonstrates that the proposed approach significantly outperforms logistic regression in terms of both accuracy and PR-AUC while effectively uncovering key drivers of financial distress, thereby achieving a robust synthesis of high performance and high interpretability.

Abstract

Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerability to financial distress. Predicting SME default is therefore crucial for financial institutions, policymakers, and researchers. Recent advances in machine learning (ML) have improved predictive performance in credit risk modeling. Yet, the limited interpretability of complex models raises concerns regarding transparency and regulatory compliance. This study investigates SME's default predictors and applies explainable artificial intelligence (XAI) techniques to them. Using a panel of 50,718 Italian SME over the period 2015-2024, we compare traditional econometric approaches with several ML classifiers. The empirical results show that ML models significantly outperform the traditional logistic regression benchmark in terms of Balanced Accuracy and PR-AUC. To address the interpretability challenge, we introduce DEXiRE-EVO, a novel evolutionary rule extraction framework that combines multi-objective optimization with the Contextual Importance and Utility (CIU) explainability method. The extracted rules reveal economically meaningful patterns associated with SME financial distress, highlighting the roles of weak internal liquidity generation, internal capital erosion, high leverage, and operational inefficiency. Additionally, contextual macroeconomic conditions and the persistence of financial instability contribute to identifying high-risk firms. In general, the results show that combining ML with evolutionary rule extraction can improve both predictive performance and interpretability in credit risk modeling, thus supporting more transparent, data-driven decision-making in financial environments.

Problem

• SME default prediction
• interpretability
• explainable AI
• credit risk modeling
• machine learning

Innovation

• evolutionary rule extraction
• explainable AI (XAI)
• multi-objective optimization
• credit risk modeling
• CIU explainability