Unveiling Bankruptcy Risk Through Advanced Data Analysis and Machine Learning Techniques

Bankruptcy is a legal proceeding initiated when an entity fails to repay their outstanding financial obligations to its creditors. Given the growing intricacy of the financial market, precise identification of the companies susceptible to the bankruptcy emerges as a pivotal concern for investors, creditors, and other regulatory authorities alike. This study endeavors to discern the likelihood of companies facing bankruptcy by analysis of diverse data metrics, encompassing ROA (Return on Asset), Gross Margin, Profit Rate, and other equally significant parameters. Several Machine Learning Algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machines, XGBoost, Gradient Boost Machines, and Naive Bayes, have been employed to meticulously scrutinize the data and identify intricate patterns indicative of potential bankruptcy. Notably, the comprehensive evaluation revealed that amidst all algorithms, Random Forest demonstrated unparalleled performance, achieving the highest accuracy score of 97.61%.