The modified ESG-KMV model for default prediction

This study enhances credit risk assessment by incorporating environmental, social, and governance (ESG) factors into the KMV model. Using matched data from 110 Taiwanese companies, half of which experienced financial crises between 2015 and 2023, we propose adjusting the KMV model’s default threshold on the basis of ESG ratings. This approach calculates ESG-adjusted default distance and expected default frequency (EDF), providing a more comprehensive evaluation of corporate credit risk beyond traditional financial indicators. The empirical findings show the following: First, incorporating ESG factors significantly widened the default probability gap between crisis and noncrisis companies. Second, ESG inclusion enhances the logit model’s explanatory power—each 1% increase in EDF with considering ESG ( ) increases the probability of financial distress by 2.210 times. Third, ESG factors improve model prediction accuracy, particularly for early warning. Finally, robustness tests confirm model stability.