PERBANDINGAN METODE ALTMAN Z-SCORE, BENEISH M-SCORE-DATA MINING DAN SPRINGATE DALAM MENDETEKSI FRAUDULENT FINANCIAL REPORTING (Studi Empiris Perusahaan Manufaktur Tahun 2014-2018)

Abstract

Yudi Partama Putra. This study is based on the number of fraud cases that occur both domestically and abroad. Financial reporting fraud that occurs within the company is a major concern throughout the world. This study aims to investigate whether there are significant differences between the Altman Z-score, the beneish m-score - data mining, and springate methods in detecting fraudulent financial reporting. This is quantitative research, and the data used are secondary data. The population of this study is Manufacturing companies listed on the Indonesia Stock Exchange in 2014-2018. Sample is selected by using purposive sampling method so that there are 26 companies to be the samples. The analysis technique of this study is Regression Analysis on Partial Least Square using SmartPLS. The results of this study indicate that altman z-score and springate have a positive and significant effect on fraudulent financial reporting, while the beneish m-score-data mining has no effect on the fraudulent financial reporting. However, of the three methods, the Altman Z-score method is more influential in detecting the fraudulent financial reporting than the Springate method.
 Keywords: Altman Z-Score, Beneish M-Score - Data Mining, Springate, Fraudulent Financial Reporting