<p>The present study designs an early warning prediction model for bank distress and investigates the factors leading to bank distress in India. The study employs an empirical and descriptive research design. The study is based on the performance of 32 public and private sector banks over 7&#xa0;years (2017–2023). The logit regression model has been applied, using bank distress as the dependent variable and bank-specific CAMEL indicators and macroeconomic variables as predictors. The empirical results indicate that the macro–micro framework for predicting bank distress provides better results compared to the framework that only includes bank-level micro indicators. Bank’s size, return on equity, capital adequacy, debt-to-equity ratio, and liquidity ratio are significant predictors of stability. Larger banks and those with higher returns on net worth are less likely to face distress, and banks with a high debt-equity ratio, weak asset quality and liquidity ratio are vulnerable. Bank distress is more likely during economic expansions and periods of inflation. This study offers valuable insights into the literature on banking failure prediction models, utilizing both micro and macro indicators, particularly within the Indian context, following the demonetization in November 2016.</p>