A Conceptual Framework for Financial Distress Prediction Models and their Role in Analysing Company Financial Health

Abstract: Financial distress prediction models have become indispensable tools for stakeholders seeking to assess the financial health of companies and anticipate potential difficulties. This paper outlines the conceptual framework underpinning these models, detailing the key components and interrelationships involved. It explores the theoretical foundations, data types, methodologies, and evaluation metrics used in these models. Furthermore, it analyzes how these models contribute to understanding a company's financial health, enabling proactive decision-making by investors, creditors, management, and regulators. By providing early warnings of financial distress, these models facilitate timely intervention and mitigate potential losses. Keywords: Financial Distress, Prediction Model, Financial Health, Bankruptcy, Altman Z-Score, Logistic Regression, Support Vector Machines, Machine Learning, Financial Ratios, Credit Risk