Bankruptcy Prediction Using Machine Learning

Abstract

Bankruptcy prediction is a critical task for companies, lenders, investors, and regulators. Accurately forecasting the likelihood of a company going bankrupt can help stakeholders make informed decisions, prevent financial losses, and mitigate risks. With its ability to handle vast amounts, machine learning has become a useful technique for bankruptcy prediction, identifying non-linear relationships, and learn from past patterns. In this, we present an bankruptcy prediction model using machine learning. We also focused on the key features used in the model, such as financial ratios, industry-specific metrics, and macroeconomic indicators. We also compared the different machine learning algorithms with better results. Finally, we highlight the importance of model evaluation and interpretability in bankruptcy prediction, and present some of the common metrics used to evaluate the model's performance.