PREDICTIVE ANALYTICS FOR BANKRUPTCY RISK: A STUDY OF INDIAN BANKING COMPANIES

Abstract

This research paper examines how predictive analytics can help assess the risk of bankruptcy in the banking institutions of India. The study is using predictors of financial distress through statistical and machine learning algorithm, involving macroeconomic variables, financial ratios, and market-based predictors to determine the predictors. The article compares traditional Altman Z -score models with modern algorithms that include support vector machine and data envelopment analysis, and shows that the use of market data supplements predictive power of the models by increasing their overall explanatory power. The results show that Indian banking businesses are very susceptible because of their strong interdependence with the overall economy, and therefore, the necessity of early warning systems in the banking industry is inevitable. The findings substantiate the assumption that corporate governance indicators and financial ratios enhance the predictive value of bankruptcy forecasts, and the model of accounting- and market-based variables is more effective than the conventional ways, especially when it comes to stress testing and the anticipation of a crisis. In general, the paper identifies the usefulness of the sophisticated models to eliminate the risk of bankruptcy and enhance financial stability, and provides practical guidance to the regulators, bank managers, and financial institutions on the creation of an entire predictive framework that captures the unique nature of the Indian banking market.