<![CDATA[This study investigates the influence of Risk Profile, Good Corporate Governance, Earnings, and Capital (RGEC) indicators on financial distress of Bank Permata during 2014–2026 using a multi-model approach comparing Altman Z″-Score and Bank Z-Score. Quarterly data from 50 observations were analyzed through multiple linear regression, mean-difference tests, and Bankometer S-Score zone classification. Results demonstrate that the influence of RGEC on financial distress is model-dependent. In the Altman Z″-Score model, GCG and capital (CA) significantly influence financial distress, while in the Bank Z-Score model, credit risk (NPL), liquidity risk (LDR), and capital (CA) emerge as key determinants. Capital consistently appears as the most dominant variable across both models. Significant improvement in financial condition is observed after the acquisition by Bangkok Bank, reflected in higher scores of both prediction models. The Bank Z-Score exhibits substantially better explanatory power compared to the Altman Z″-Score. Furthermore, zone classification reveals that the Altman Z″-Score tends to overestimate bankruptcy potential, whereas the Bankometer S-Score consistently classifies the bank as highly sound throughout. Findings confirm that banking-specific models are more appropriate for financial distress assessment, providing empirical evidence that model selection significantly influences risk interpretation and strategic decision-making in the Indonesian banking sector.]]>
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