The paper addresses methodological aspects of bankruptcy risk assessment. The study’s primary goal was to develop an effective bankruptcy prediction model specific to the chemical industry, independent of political and sanction influences. Correspondingly, classical models were tested on chemical industry data, revealing in particular that the Zaitseva model (1998) is less accurate than the Altman and Lis models. The hypothesis posited that a model based on current, industry-specific data would outperform general models. The newly developed model outperformed established models in predictive power, validating the hypothesis. By analyzing a dataset divided into narrow and broad samples, the study introduces new coefficients, including the IFR (Index of Financial Risk), which enhances model accuracy. This study classifies companies as bankrupt if declared by a court or multiple bankruptcy petitions are filed. The proposed model can be employed to assess bankruptcy probabilities in the chemical industry moving forward.