Financial Distress Prediction for Indian Banks: A Path to Sustainable Financial Stability

The present study focuses on the development of a robust model to forecast financial distress in Indian public sector bank. 200 banks data was used from 2010 to 2024. The research made use of financial ratios and macroeconomic indicators sourced from the CMIE database, employing a multinomial logistic regression method to predict the likelihood of financial distress. Altman Z score is a very well-known model for the manufacturing companies to predict the financial distress. However, in this study revised Altman Z-Score is used for evaluating the financial health of banks and identifying potential distress. This revised Altman Z-Score is meant for nonmanufacturing companies. We conduct a variance inflation factor (VIF) analysis to ensure the model's reliability, and the results indicate that there is no significant multicollinearity among the predictors. Key elements such as GDP growth, retained earnings, net working capital, inflation, return on assets, and bank size are analyzed using $\mathbf{R}$ programming, emphasizing their crucial impact on financial stability. The findings provide a thorough method for assessing financial distress, which has significant implications for policymakers, banks, and regulatory agencies.