Benchmarking Financial Distress Prediction Models through Outcome Based Validation in Indian Companies - Mohd Jameel (Involvement International Journal of Business, 2026)
Although many studies have compared financial distress prediction models, most evaluate performance based on agreement among models rather than validation against actual financial outcomes. This limitation makes it difficult to identify which models provide the most reliable early-warning signals in practice. Addressing this gap, this study benchmarks five widely used financial distress prediction models namely the Altman Z-Score, Ohlson O-Score, Zmijewski, Springate, and Grover models through an outcome-based validation framework using real-world financial outcomes of selected Indian companies. Secondary financial data from six companies in the telecommunications, infrastructure, and automobile sectors covering the 2015–2025 period were analyzed. Model predictions were validated against independently verified evidence, including credit ratings, insolvency proceedings, financial restructuring events, and company-specific distress indicators. Predictive performance was evaluated using confusion matrix analysis and multiple classification metrics, including accuracy, sensitivity, specificity, precision, F1 score, and Type II error. The findings reveal substantial variation in model performance. The Springate model achieved perfect sensitivity and identified all distressed observations, whereas the Zmijewski model provided the most balanced overall performance with the highest F1 score. In contrast, the Altman, Ohlson, and Grover models showed higher overall accuracy but missed several actual distress cases. By introducing an outcome-based validation framework, this study advances the evaluation of financial distress prediction models beyond conventional model comparison and provides more reliable guidance for investors, lenders, regulators, and corporate managers in selecting effective early-warning tools.