Predictive Analytics for Assessing Corporate Financial Distress, Liquidity Risk, and Long Term Business Sustainability Performance

Abstract

Corporate financial distress remains a persistent challenge for investors, lenders, regulators, and managers because deteriorating financial conditions can disrupt operations, weaken creditworthiness, destroy enterprise value, and threaten long-term economic sustainability. Traditional assessment approaches based primarily on historical accounting ratios may provide limited warning when liquidity pressures, leverage changes, profitability deterioration, cash-flow instability, and market conditions interact dynamically. This study develops an integrated predictive analytics framework for assessing corporate financial distress, liquidity risk, and long-term business sustainability performance. The framework combines financial statements, cash-flow indicators, working-capital measures, solvency ratios, profitability trends, market variables, and firm-specific characteristics to construct multidimensional risk features. Statistical and machine-learning models estimate distress probability, identify emerging liquidity constraints, and classify firms according to sustainability risk. Model development incorporates feature engineering, controlled data splitting, cross-validation, hyperparameter optimization, independent testing, and explainability analysis. Performance is evaluated using discrimination, calibration, prediction-error, and robustness measures. The proposed framework connects short-term liquidity deterioration with broader financial resilience, enabling earlier risk identification, transparent corporate monitoring, improved lending decisions, and evidence-based strategies for strengthening sustainable business performance.