Sarcouncil Journal of Economics and Business Management

Abstract: Corporate bankruptcy prediction is still a very important issue in the United States capital market because of its effects on investors, regulators, and even policymakers. Classical models of financial distress relying on financial ratios like Altman Z-score have presented a useful, but growingly inadequate, insight into the broad-based, multidimensional causes of financial distress in contemporary markets. The research objective is to improve the accuracy of the prediction of bankruptcy through combining financial and non-financial elements in hybrid and AI-oriented models. Among the objectives are the study of predictive model development, scrutiny of governance, Environmental, Social and Governance (ESG), and macroeconomic determinants, and research of best practices and failures in predicting bankruptcy. The case study of Ford Motor Company and Sears Holdings shows differing results: Ford was able to use governance and ESG-based strategies to remain afloat amidst times of crisis, but Sears failed to take advantage of and ignored non-financial indicators and used old financial models to demonstrate its downfall. The results indicate that hybrid models, which integrate quantitative ratios with qualitative and prospective indicators, have better predictive power, improve early warning systems, and mitigate systemic market risk. Finally, the study draws the conclusion that taking the step of combining financial, non-financial, and macroeconomic indicators by using AI-enhanced models is the future of bankruptcy prediction. These multidimensional strategies offer better, more transparent, policy-relevant tools to investors and regulators, to ensure financial stability and sustainable corporations within the United States capital market.