Enhancing credit risk assessments of SMEs with non-financial information

We investigate non-financial variables for predicting bankruptcy in small and medium-sized enterprises (SMEs). The variables encompass management, board and ownership structures and are sourced from universally accessible information, rendering them available to all stakeholders and allowing for the analysis of all SMEs within a market. Using a large and recent sample of SMEs, we empirically examine the variables that predict bankruptcy over time horizons of one, two and three years. Our analysis incorporates state-of-the-art discrete hazard models, the least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bagging and random forest. We also test robustness using balanced datasets generated using the synthetic minority oversampling technique (SMOTE). We find that including non-financial variables enhances bankruptcy predictions compared to using financial variables alone. Moreover, our results show that among our variables, the most significant non-financial predictors of bankruptcy are the age of chief executive officers (CEOs), chairpersons and board members, as well as ownership share and place of the board members’ residences. This research highlights the critical role of integrating non-financial information with traditional financial variables to enhance the prediction of SME bankruptcy. While financial variables remain the most significant predictors, the inclusion of nonfinancial factors significantly improves the accuracy of the assessment of SMEs’ financial health, benefiting investors, policymakers, and financial institutions by enabling better risk management and more effective support schemes. The findings underscore the importance of diverse board composition and local engagement in reducing bankruptcy risk, offering valuable insights for improving SME governance and stability.