Ensemble Machine Learning Approach to Bankruptcy Prediction

Abstract Bankruptcy is among the biggest threats every company has in terms of its reputation. It can be explained as a situation in which a company is unable to pay back the outstanding debts to banks, lenders, and/or suppliers. An accurate and timely prediction could help the companies avoid and prevent this situation by incorporating remedial actions beforehand. In this regard, current research aims to investigate a combination of techniques to accurately predict bankruptcy. This is achieved by investigating different ensemble techniques to combine the best methods for accuracy enhancement. Comparative analysis has been carried out on the proposed ensemble models using the original but imbalanced dataset as well as the balanced dataset using the oversampling technique. The proposed method of using the balanced dataset on the proposed ensemble models outperformed the models using the original dataset in terms of accuracy and other performance metrics. As an outcome, Balance Bagging exhibits the highest accuracy at 98.77% followed by Random Forest at 98.68% and AdaBoost at 96.9%, respectively. This is considerable in contrast to the state-of-the-art techniques.