A Predictive Mechanism Using MLT To Detect Bankruptcy

This Research focuses on improving the accuracy and efficiency of bankruptcy prediction models so that it would serve as a valuable tool in assessing organizational fiscal health. These improvements may revolutionize the practice of risk assessment so that lending decisions may be informed, regulatory oversight improved, and investor protection enhanced. An optimized model may also support thee early warning system that will provide for timely intervention to perhaps reduce the devastating impacts of corporate bankruptcies on the economy at large. It discusses the delicate trade-off between the complexity of the deployed models as well as their interpretability, emphasizing the importance of transparency in financial decision-making. It aims to develop an interpretative model that achieves high performance while maintaining comprehensibility by eliminating irrelevant features. Given the rise of the need for explainable AI in the financial sector, where explainability is vital for building trust in AI systems and aligning with regulations, this proposed approach is well-aligned with current requirement.