Improving Bankruptcy Prediction Using Machine Learning

Abstract

It is very crucial to predict bankruptcy for the decision-making process of creditors, investors, businesses, and policymakers. While it will help businesses and financial institutions in making sound judgments by accurately projecting bankruptcy, it helps to reduce the adverse impacts also for the economy and society. The methodologies like random forest regression and SMOTE along with other algorithms were cross validated to enhance accuracy. These prediction models can be developed further by including both financial and non-financial factors like market conditions and management quality. Moreover, dramatic efficiency improvements can be achieved through advanced technologies like deep learning. Technological advancement and data accessibility will increase these tactics such that banks and businesses can discover bankruptcy risk, make the right decisions, and save losses.