Determinants of Financial Distress Based on the RGEC Framework: A Logistic Regression Analysis of Commercial Banks Listed on the Indonesia Stock Exchange (2014-2024) - Anisa Aura Rahma; Sartika Wulandari (Golden Ratio of Finance Management, 2026)
The resilience of the banking sector has faced unprecedented challenges over the last decade, particularly because of the disruption caused by the COVID-19 pandemic and the subsequent economic volatility. This study aims to reconstruct an early warning model for financial distress in the Indonesian banking sector by evaluating the efficacy of the Risk Profile, Good Corporate Governance (GCG), Earnings, and Capital (RGEC) framework. Unlike previous studies that relied on the Altman Z-score, this study defines financial distress using a profitability-based approach (negative ROA), which is more aligned with the operational realities of financial institutions. Using secondary data collected from audited annual reports published on the Indonesia Stock Exchange, a sample of 220 firm-year observations was analyzed via logistic regression. The findings reveal that credit risk (NPL) is the sole, dominant predictor of financial distress. Conversely, the GCG scores and Capital Adequacy Ratios (CAR) failed to demonstrate statistical significance, meaning these variables do not reliably predict the likelihood of a bank falling into financial distress. This indicates a clear disconnect between administrative compliance and actual financial resilience. These results challenge the efficacy of the "self-assessment" governance model and imply that high capital buffers alone are insufficient to prevent distress without rigorous asset-quality management. This study contributes to the literature by providing a specific early warning model for emerging markets and offers policy recommendations for the Indonesian Financial Services Authority regarding the supervision of asset quality.