Machine Learning Models Comparison for Bankruptcy Predication for Indian Companies A study based on India’s Insolvency and Bankruptcy Code (IBC ‘2016)

Abstract

It is essential to recognize that dynamics of bankruptcy events vary across regions and legal frameworks. In this context, the paper aims to fill the critical gap in literature by presenting an analysis of machine learning (ML) models for early detection of bankruptcy probability among Indian companies operating under the Insolvency and Bankruptcy Code (IBC) of 2016. This study distinguishes itself by leveraging an extensive dataset covering the period from FY 2016 to FY 2022, encompassing 65,583 entries for 7,008 unique corporations, including 257 bankrupt entities. This paper employs various predictive variables, including traditional financial ratios, Altman Z-scores, and comprehensive financial statement data, employing a scenario-based approach over a one-year forecasting horizon. The findings support the notion that ML models, particularly XGBoost, outperform traditional logistic regression models and Altman Z-scores in accurately predicting bankruptcy among Indian corporates. These findings align with the trend in the literature favoring ML models for enhanced predictive power, offering valuable insights for financial institutions and policymakers in India’s corporate landscape.