Predicting Financial Distress Using the Grover, Springate, Taffler, Zmijewski, and Altman models: a Case Study of PT. Prima Alloy Steel Universal Tbk (PRAS) and PT. Sri Rejeki Isman Tbk (SRIL) - Rotinsulu, David Peter; Saerang, David Paul Elia (Abdurrauf Science and Society, 2025)
Every company aims to remain competitive in the business world and consistently generate profits. Nevertheless, some companies that have operated for years are eventually forced to cease production due to mounting debt, poor management planning, and unstable financial statements, leading to financial distress—as seen in the cases of PT. Prima Alloy Steel Universal Tbk (PRAS) and PT. Sri Rejeki Isman Tbk (SRIL). This research aims to determine which model among Grover (G-Score), Springate (S-Score), Taffler (T-Score), Zmijewski (X-Score), and Altman (Z-Score) is the most accurate in predicting financial distress. To evaluate the precision of each model, the study involves calculating the accuracy level of the prediction results. A comparative descriptive method with a quantitative approach is applied. The financial data analyzed in this study consists of the companies’ financial statements from the three years following their declaration of bankruptcy or financial distress by the Commercial Court. The results indicate that the Springate model has the highest level of accuracy, achieving a 100% accuracy rate and successfully predicting the bankruptcy of the companies studied. It hopes that the company must having competent leaders by seeking the risk of bankruptcy / financial distress to help decisions or policies making, in order to save and prevent from threatening bankruptcy in the future.