Predictive Analytics for Corporate Bankruptcy: A Machine Learning Framework Using Financial Ratios

Abstract

This research develops a comprehensive machine learning framework
for predicting corporate bankruptcy using }nancial ratios and accounting metrics. We analyze a dataset of 1,500 publicly traded companies
over a 10-year period, employing multiple classi}cation algorithms including logistic regression, random forests, and support vector machines. Our
methodology incorporates feature selection techniques to identify the most
predictive }nancial indicators and addresses class imbalance through synthetic data generation. The results demonstrate that ensemble methods
achieve 94.2% accuracy in predicting bankruptcy events 12 months prior
to occurrence, signi}cantly outperforming traditional statistical models.
The study contributes to the accounting literature by providing a robust
predictive tool for financial distress assessment and offers practical implications for auditors, investors, and regulatory bodies in early risk detection
and mitigation strategies.