Comparative Analysis of Zmijewski and Grover Methods in Predicting Financial Distress of Islamic Banks in Indonesia

Abstract

This study is motivated by Indonesia's economic instability, which has led to a rise in problematic financing within Islamic banking. Given the fluctuating nature of real-based financing, an early prediction model is needed to minimize the negative impact of financial distress. This research aims to analyze and compare the accuracy of the Zmijewski and Grover models in predicting financial distress among Islamic Commercial Banks registered with the Financial Services Authority (OJK) during the 2014-2023 period. A descriptive quantitative approach was employed using secondary data from annual financial reports of five purposively selected banks. Data analysis involved descriptive statistics, normality tests, Wilcoxon Signed Ranks test, accuracy rate, and type error measurement. The findings reveal a significant difference between the two models. The Zmijewski model achieved 76% accuracy with a 24% type error, while the Grover model reached perfect accuracy of 100% with no error. These results indicate that the Grover model provides a more accurate tool for predicting financial distress and offers practical implications for risk management in Islamic banking.