BankRuptcyML: A Sustainable System for Company Bankruptcy Prediction and Detection using Machine Learning Techniques

This research study presents a comprehensive use of machine learning techniques used for bankruptcy prediction, which is crucial for financial institutions to assess creditworthiness. The study highlights methods such as logistic regression, decision trees, and ensemble learning (bagging, boosting), demonstrating the superiority of machine learning over classical statistics for smaller datasets. This study compares various strategies by considering financial statements, market trends, industry benchmarks, and macroeconomic indicators. Integrating diverse data sources helps identify risk factors and signs of financial distress, guiding proactive bankruptcy prevention measures. These include optimizing cash flow, enhancing efficiency, diversifying revenue, and implementing early warning systems. Predicting and preventing bankruptcies support financial stability, job protection, and economic growth. The proposed model employs financial ratios and relevant data to predict bankruptcy likelihood, which is evaluated using assessment and evaluation metrics. The AdaBoost model has a reported accuracy of 86.36% and an F1 score of 85.93%, which are higher than those of all the other cohorts included in the existing work.