Prediction of Bankruptcy Using Machine Learning Models

Effectively predicting bankruptcy is a pivotal aspect of robust financial risk management, holding profound implications for businesses, investors, and the broader economic landscape. Bankruptcy, often entailing insolvency and financial distress, can lead to substantial losses for creditors and investors, disrupt supply chains, and trigger cascading effects across the economy. This multifaceted challenge lacks a universal solution, but various commonly considered factors, including financial ratios, management changes, cash flow, and asset assessments, contribute to bankruptcy prediction. Integrating machine-learning algorithms enhances prediction accuracy significantly. This study proposes the implementation of diverse machine-learning classifiers—random forest classifier, K-nearest neighbor, support vector classifier, logistic regression, and decision tree classifier. Trained on a dataset featuring financial statements from both bankrupt and non-bankrupt companies, our findings indicate that the random forest classifier stands out as the most precise algorithm for bankruptcy prediction. The novelty of this work lies in its comprehensive exploration of multiple machine-learning classifiers for bankruptcy prediction, providing insights into their comparative effectiveness and offering a valuable contribution to the state of the art in financial risk management.