Comparative Analysis of the Altman Z-Score and Beneish M-Score Methods in Detecting Financial Statement Fraud: A Case Study on Mining Sector Companies Listed on the IDX for the 2018-2024 Period

Abstract

Financial statement fraud remains a significant threat to capital market integrity, with mining companies being particularly vulnerable due to commodity price volatility and complex accounting practices. This study compared the effectiveness of the Altman Z-Score and Beneish M-Score methods in detecting financial statement fraud (FSF) among mining companies listed on the Indonesia Stock Exchange (IDX) during the 2018–2024 period. Using an associative quantitative approach with 168 panel data observations, the results showed that the Altman Z-Score had a negative and significant effect on FSF, whereas the Beneish M-Score did not have a significant effect. The superior performance of the Z-Score was confirmed through evaluation metrics, including accuracy of 77.98%, precision of 94.34%, specificity of 96.43%, and an AUC value of 0.9025, compared with the Beneish M-Score, which achieved only 47.02% accuracy and an AUC value of 0.4157, indicating performance below random classification. These findings indicate that the Altman Z-Score, through its financial distress dimension, is more relevant and reliable as an early detection instrument for financial statement fraud in the Indonesian mining sector.