MACHINE LEARNING FOR INDIAN CORPORATE DEFAULT PREDICTION

Abstract

The study titled " Machine Learning for Indian Corporate Default Prediction " aims to explore and evaluate the effectiveness of various widely adopted techniques for predicting corporate defaults in the Indian context. Among the models examined are the traditional Altman Z-Score and its variant tailored for emerging markets. In addition, the study advocates for the use of ML classification algorithms—specifically Random Forests, logistic regression, K-Nearest Neighbors (KNN)—to forecast default events. By analyzing a huge range of variables, including financial ratios, the study's core objective is to evaluate the probability of corporate defaults. The findings suggest that machine learning approaches offer notable advantages over conventional models. In particular, the logistic regression model demonstrates superior predictive precision compared to traditional methods. In addition, the study finds that incorporating both financial ratios and market indicators greatly improves the ability to predict logistic regression. Contributing to the increasing pool of information in corporate finance, study provides fresh look into the application of ML techniques for predicting default in the Indian corporate landscape. Its conclusions hold practical relevance for policymakers, investors and financial institutions, while also opening new avenues for research and offering a more adaptive framework for forecasting corporate default events.