Early Warning System for Islamic Banks: a Panel Logit Approach to Financial Distress in Indonesia - Muhammad Zaki As Shafi MT (Journal Of Economic Cluster, 2025)
This study uses a quantitative approach with a causal-comparative design. The population is all Islamic Banks (BUS) in Indonesia, with a sample of 10 BUS selected through purposive sampling during the 2019–2024 period, resulting in 60 panel data observation units. The analysis technique used is Panel Data Logistic Regression to estimate the probability of Financial Distress. Model validation is carried out through a classification matrix and Area Under the Curve (AUC). The results of the study indicate that the model has very strong discriminatory power with an overall prediction accuracy reaching 100% (Nagelkerke R Square = 1.000). The regression coefficients indicate that Financing Risk (NPF) and Operational Inefficiency (BOPO) have a significant positive effect on the probability of Financial Distress. Meanwhile, Bank Size (SIZE) also shows a positive effect that rejects the 'Too Big to Fail' hypothesis, and Liquidity (FDR) shows a negative effect, functioning as a buffer for profitability. This study concludes that NPF and BOPO are the most critical early warning indicators, and the Logit model built can be a valid and specific Early Warning System for BUS regulators and management.