A Literature Review and Gap Analysis on Need for Early Corporate Distress Prediction

Abstract The paper covers literature review and gap analysis to focus the pressing need for early corporate distress prediction. It highlights the increasing complexity of global business situation and the severe economic and social impacts of delayed distress detection. This study views upon rise of prediction models, by using traditional financial ratio analysis to advanced machine learning approaches and identifying major limitations such as data quality, model transparency, and applicability across diverse sectors. Attention is given to challenges unique to emerging markets, including the integration of qualitative factors and sector-specific modeling needs. With critical analysis, the paper identifies persistent research gaps and traces for the development of holistic, adaptive, and interpretable early warning systems. The enhancements are pivotal for improving financial risk management and stakeholder protection, and overall economic stability. The study identifies specific research gaps notably in unified theoretical frameworks, sector- sensitive modelling, and adoption to indias dynamic regulatory landscape post IBC implementation.