Model Sensitivity in Early Warning Systems: Evidence from Conventional and Islamic Insurance in Indonesia

Luthfia Ayu Karina; Yogi Aris Vara Wisnu Wardhana; Gusti Nurul Kamaliah

doi:10.63922/ijebir.v5i04.3592

Luthfia Ayu Karina Accounting Study Program, Nahdlatul Ulama University of South Kalimantan, Indonesia
Yogi Aris Vara Wisnu Wardhana Accounting Study Program, Nahdlatul Ulama University of South Kalimantan, Indonesia
Gusti Nurul Kamaliah Accounting Study Program, Nahdlatul Ulama University of South Kalimantan, Indonesia

DOI: https://doi.org/10.63922/ijebir.v5i04.3592

Keywords: Early Warning System; financial fragility; Islamic insurance; risk management; model sensitivity.

Abstract

This study examines how Early Warning System (EWS) model design influences financial risk detection and decision-making relevance in conventional and Islamic insurance. It addresses whether a single EWS model can be applied reliably across structurally different insurance systems. A quantitative comparative-longitudinal design was applied over 2019–2024 using data from two Indonesia Stock Exchange-listed insurance companies. Seven NAIC-based financial ratios were normalized using z-scores and constructed into three model variants: simple averaging, NAIC threshold, and an Enhanced model incorporating liquidity and operational indicators. Hypotheses were tested using Shapiro–Wilk, independent t-tests, and Mann–Whitney U tests. The results show that simple averaging fails to differentiate financial fragility (p = 0.607), while the NAIC Threshold model produces a highly significant distinction (p < 0.001), strengthened by the Enhanced model (p = 0.0099; R² = 0.50). The Islamic insurer exhibits higher fragility during 2022–2024, driven by liquidity pressure and operational inefficiency. These findings challenge the assumption that generic EWS models perform equivalently across systems. Applying conventional EWS models without structural adaptation leads to biased risk interpretation in Islamic insurance and may delay early risk detection. The study provides implications for risk management, regulatory supervision, and investor-oriented early warning analysis.

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