Bankruptcy Prediction for US Businesses: Leveraging Machine Learning for Financial Stability

Abstract

The economic ramifications due to the bankruptcy of businesses in the USA are exponentially huge and multi-dimensional. Starting from small businesses to huge and large-scale businesses, all declare bankruptcy every year, leading to massive sacking, reduced consumer confidence, and consequently a trickled effect throughout other sectors of the economy. The prime objective of the present study was to devise and execute machine learning techniques to predict bankruptcy in US businesses effectively. This research project intends to develop an efficient understanding of the factors leading to business failures using algorithms that learn from data. For the present study focusing on bankruptcy prediction, we used several datasets to enhance the quality and reliability of forecasts. The major data sources were financial statements, which include balance sheets, income statements, and cash flow statements, providing quantitative measures that enable analysts to perceive the financial health of a firm through various ratios and indicators. Machine learning model selection for the prediction of bankruptcy is based on the evaluation of various algorithms: Logistic Regression, Random Forest, Gradient, and Boosting. The models were evaluated against a set of overall metrics: accuracy, precision, recall, F1-score, and ROC-AUC. Random Forest and XG-Boost resulted in marginally better scores across all metrics as compared to Logistic Regression. Predictive insights determined from bankruptcy risk models give rise to valuable interpretations for decision-makers. An organization in the USA can, from model prediction analysis, identify firms that show a high risk of going into bankruptcy and thus enable appropriate interventions in time. Machine learning-driven bankruptcy prediction undoubtedly assists in integrating better risk management policies and procedures in financial institutions. Similarly, by using complex algorithms for pattern identification in historical data, an institution will go deeper in identifying patterns constituting distress in companies