Implementation of the Z-Score Test to Predict Financial Distress as an Early Warning System Effort in Banking Companies Listed on the Indonesia Stock Exchange for the Period 2020-2024

Abstract

Financial distress in banking institutions remains a critical issue, yet previous studies predominantly focus on manufacturing or non-financial firms, leaving limited empirical evidence on whether the Altman Z-Score is still reliable for banking companies in emerging markets. This research fills that gap by examining the usefulness of the Z-Score as an early warning system and identifying the financial ratios that significantly influence financial distress among banks listed on the Indonesia Stock Exchange during 2020–2024.Using a descriptive-quantitative design and secondary data from 42 banking companies, the study integrates the Z-Score model with multinomial logistic regression to statistically validate distress determinants. The results show that 13 banks experienced financial distress in 2020 and 8 banks in 2024, indicating gradual improvement in financial stability. Multinomial logistic regression confirms that working capital to total assets, retained earnings to total assets, and earnings before interest and taxes significantly affect financial distress status (p < 0.05). The Z-Score model is empirically validated as a viable early-warning tool for Indonesian banks with strong classification performance. The study contributes by providing updated empirical evidence on the applicability of the Z-Score in the banking sector—an area with limited prior exploration—and by identifying key predictors of distress during the post-pandemic period. Limitations include the exclusion of macroeconomic variables and the reliance on secondary financial reports. Future research may incorporate macro-financial indicators or machine-learning-based validation to enhance predictive accuracy.