Predicting Corporate Bankruptcy: A Comparative Analysis of Machine Learning Models

Corporate bankruptcy prediction is a critical task for investors, creditors, and policymakers alike, as it enables them to anticipate and mitigate financial risks.In this study, we conduct a comparative analysis of machine learning models for predicting corporate bankruptcy.We utilize a diverse set of features including financial ratios, market indicators, and macroeconomic variables to train and evaluate several popular machine learning algorithms.Our comparative analysis includes logistic regression, decision trees, random forests, support vector machines, and artificial neural networks.We evaluate the performance of these models using metrics such as accuracy, precision, recall, and F1-score, and conduct a thorough comparison to identify the most effective approach for corporate bankruptcy prediction.Additionally, we examine the interpretability of the models to understand the factors driving their predictions.Our findings provide valuable insights into the application of machine learning in corporate bankruptcy prediction and offer guidance for stakeholders in making informed financial decisions.