DETECTION OF FINANCIAL STATEMENT FRAUD IN THE CONSTRUCTION SECTOR WITH THE BENEISH M-SCORE

This research investigates the occurrence of financial statement fraud by utilizing the Beneish M-Score model, focusing on construction companies which listed on the Indonesia Stock Exchange (IDX) over the period 2019 to 2023. Financial statement fraud, characterized by the intentional distortion of accounting data to mislead stakeholders, has emerged as a growing issue in the global financial environment, including within the Indonesian context. Introduced by Messod Beneish in 1999, the Beneish M-Score model employs a set of financial ratios to identify the likelihood of earnings manipulation within a company’s financial reporting. In this research, the model is applied to evaluate the financial statements of PT Waskita Karya, a state-owned company operating in Indonesia’s construction industry during the selected timeframe. The research identifies key financial ratios that indicate potential fraud, focusing on the 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 Index (SGAI), Leverage Index (LVGI), and Total Accrual to Total Assets Index (TATA). The results indicate that the Beneish M-Score model serves as an effective tool for detecting early indications of irregularities in financial reporting, while also providing useful insights for auditors, investors, and regulators in supporting transparency and accountability across infrastructure companies. This study adds to the expanding literature on fraud detection in financial reporting, especially within emerging markets, and emphasizes the need for strong financial control systems to reduce the risk of earnings manipulation.

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Publication Details

Journal
The International Conference on Sustainable Economics Management and Accounting Proceeding
Published
2025-08-13
DOI
https://doi.org/10.32424/icsema.1.1.454
Primary Topic
Imbalanced Data Classification Techniques
Type
article
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article

DETECTION OF FINANCIAL STATEMENT FRAUD IN THE CONSTRUCTION SECTOR WITH THE BENEISH M-SCORE

Ali Mugayat, Adillah Safitri, Dyah Anissa Yuniar, Ernanto
The International Conference on Sustainable Economics Management and Accounting Proceeding
Imbalanced Data Classification Techniques
article

DETECTION OF FINANCIAL STATEMENT FRAUD IN THE CONSTRUCTION SECTOR WITH THE BENEISH M-SCORE

Ali Mugayat, Adillah Safitri, Dyah Anissa Yuniar, Ernanto
article en

Abstract

This research investigates the occurrence of financial statement fraud by utilizing the Beneish M-Score model, focusing on construction companies which listed on the Indonesia Stock Exchange (IDX) over the period 2019 to 2023. Financial statement fraud, characterized by the intentional distortion of accounting data to mislead stakeholders, has emerged as a growing issue in the global financial environment, including within the Indonesian context. Introduced by Messod Beneish in 1999, the Beneish M-Score model employs a set of financial ratios to identify the likelihood of earnings manipulation within a company’s financial reporting. In this research, the model is applied to evaluate the financial statements of PT Waskita Karya, a state-owned company operating in Indonesia’s construction industry during the selected timeframe. The research identifies key financial ratios that indicate potential fraud, focusing on the 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 Index (SGAI), Leverage Index (LVGI), and Total Accrual to Total Assets Index (TATA). The results indicate that the Beneish M-Score model serves as an effective tool for detecting early indications of irregularities in financial reporting, while also providing useful insights for auditors, investors, and regulators in supporting transparency and accountability across infrastructure companies. This study adds to the expanding literature on fraud detection in financial reporting, especially within emerging markets, and emphasizes the need for strong financial control systems to reduce the risk of earnings manipulation.

The International Conference on Sustainable Economics Management and Accounting ProceedingVol. 1
Universitas Muhammadiyah Cirebon (ID)
Openalex Percentile: Top 88%
Imbalanced Data Classification Techniques
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DETECTION OF FINANCIAL STATEMENT FRAUD IN THE CONSTRUCTION SECTOR WITH THE BENEISH M-SCORE — Ali Mugayat, Adillah Safitri, et al. · The International Conference on Sustainable Economics Management and Accounting Proceeding (2025) | TGRS Research Map | TGRS