Applying ML Models for the Analysis of Bankruptcy Prediction

Bankruptcy prediction helps to evaluate the financial condition of a company and it helps not only the policymakers but the investors and all concerned people so they can take all required steps to avoid or to reduce the after-effects of bankruptcy. Bankruptcy prediction will not only help in making the best decision but also provides insight to reduce losses. The major reasons for the business organization’s failure are due to economic conditions such as proper allocation of resources, Input to policymakers, appropriate steps for business managers, identification of sector-wide problems, too much debt, insufficient capital, signal to Investors, etc. These factors can lead to making business unsustainable. The failure rate of businesses has tended to fluctuate with the state of the economy. The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting, banking, and decision sciences over the past two decades. Many traditional approaches were suggested based on hypothesis testing and statistical analysis. Therefore, our focus and research are to come up with an approach that can estimate the probability of corporate bankruptcy and by evaluating its occurrence of failure using different machine learning models such as random forest, Random forest, XGboost, logistic method and choosing the one which gives highest accuracy. The dataset used was not well prepared and contained missing values, various data mining and data pre-processing techniques were utilized for data preparation. We use models such asRandom forest, Logistic method, random forest, XGBoost to predict corporate bankruptcy earlier to the occurrence. The accuracy results for accurate predictions of whether an organization will go bankrupt within the next 30, 90, or 180 days, using financial ratios as input features. The XGBoost-based model performs exceptionally well, with 98-99% accuracy.