The Comparison of the Effectiveness of Bankruptcy Prediction Methods: A Literature Review on Altman, Grover, Springate, and Zmijewski Models

Abstract

This study aims to analyze and compare the effectiveness of four bankruptcy prediction models Altman Z-Score, Grover, Springate, and Zmijewski in detecting financial distress within companies for the 2021–2025 period. Employing a scoping review method, this research examines relevant scientific articles published during this timeframe to map which model maintains the most consistent accuracy amidst post-pandemic economic dynamics. The literature analysis indicates that the Altman Z-Score model tends to exhibit the highest precision due to its comprehensive variables, which encompass aspects ranging from liquidity to accumulated profitability. Meanwhile, the Springate and Grover models demonstrate robust performance in assessing operational efficiency, although the Grover model frequently yields more optimistic results. On the other hand, the Zmijewski model was found to have a lower accuracy rate due to its narrow focus on leverage ratios. This study concludes that selecting the appropriate prediction model is crucial for investors and management in implementing early financial risk mitigation. This research recommends the integration of these models tailored to specific industry characteristics to achieve more accurate and credible detection results.