BENEISH M-SCORE MODELS TO DETECT FINANCIAL FRAUD CASE

This study aims to determine companies classified as manipulators, nanomanipulators and gray companies Customer Goods companies listed on the Indonesia Stock Exchange. The population in this study were 20 Customer Goods companies listed on the Indonesia Stock Exchange for the period 2019 to 2023. The sampling technique used purposive sampling. The data collection method uses the documentation method. Data analysis using the Beneish M-Score Method which consists of 8 variables, namely Days Sales In Receivables Index (DSRI), Gross Margin Index (GMI), Asset Quality Index (AQI), Sales Growth Index (SGI), Depreciation Index (DEPI), Sales General and Administrative Expense (SGAI), Leverage Index (LVGI) and Total Accrual to Total Assets Index (TATA). Based on the results of the study, it shows that the DSRI variable is classified as a manipulator of 15%, gray company 20%, and non-manipulator 65%. GMI is classified as a 12% manipulator, 18% gray company, and 70% non-manipulator. AQI is classified as a manipulator 22%, gray company 28%, and non-manipulator 50%. SGI is classified as a 7% manipulator, 24% gray company and 69% non-manipulator. SGAI is classified as a 32% manipulator, 0% gray company, and 68% non-manipulator. LVGI is classified as a 30% manipulator, 15% gray company, and 55% non-manipulator. TATA is classified as an 11% manipulator, 2% gray company, and 87% non-manipulator.

Authors

Publication Details

Journal
Proceedings of Economics Business Innovation & Creativity
Published
2025-06-30
DOI
https://doi.org/10.32493/ebic.v2i1.51455
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BENEISH M-SCORE MODELS TO DETECT FINANCIAL FRAUD CASE

Defel Septian, Khairunnisa Khairunnisa, Vina Febrianti
1 links
Proceedings of Economics Business Innovation & Creativity
Imbalanced Data Classification Techniques
article

BENEISH M-SCORE MODELS TO DETECT FINANCIAL FRAUD CASE

Defel Septian, Khairunnisa Khairunnisa, Vina Febrianti
article en

Abstract

This study aims to determine companies classified as manipulators, nanomanipulators and gray companies Customer Goods companies listed on the Indonesia Stock Exchange. The population in this study were 20 Customer Goods companies listed on the Indonesia Stock Exchange for the period 2019 to 2023. The sampling technique used purposive sampling. The data collection method uses the documentation method. Data analysis using the Beneish M-Score Method which consists of 8 variables, namely Days Sales In Receivables Index (DSRI), Gross Margin Index (GMI), Asset Quality Index (AQI), Sales Growth Index (SGI), Depreciation Index (DEPI), Sales General and Administrative Expense (SGAI), Leverage Index (LVGI) and Total Accrual to Total Assets Index (TATA). Based on the results of the study, it shows that the DSRI variable is classified as a manipulator of 15%, gray company 20%, and non-manipulator 65%. GMI is classified as a 12% manipulator, 18% gray company, and 70% non-manipulator. AQI is classified as a manipulator 22%, gray company 28%, and non-manipulator 50%. SGI is classified as a 7% manipulator, 24% gray company and 69% non-manipulator. SGAI is classified as a 32% manipulator, 0% gray company, and 68% non-manipulator. LVGI is classified as a 30% manipulator, 15% gray company, and 55% non-manipulator. TATA is classified as an 11% manipulator, 2% gray company, and 87% non-manipulator.

Proceedings of Economics Business Innovation & CreativityVol. 2(1)
Openalex Percentile: Top 89%
Imbalanced Data Classification Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.