Application of F-Score in Predicting Fraud, Errors: Experimental Research in Vietnam

Hung, Dang Ngoc; Ha, Hoang Thi; Binh, Dang Thai

doi:10.5296/ijafr.v7i2.12174

Cited by 16 publications

(15 citation statements)

References 13 publications

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“…Several previous studies on the F-Score model have shown consistent results, which are able to detect fraudulent financial reporting effectively, even better than the Beneish M-Score (Aghghaleh 2016;Hugo, 2019;Hung et al, 2017). In a previous study, Aghghaleh et al (2016) researched companies in Malaysia, Hung et al (2017) researched companies in Vietnam, and Hugo (2019) researched companies in the United States. Previously, Skousen & Twedt (2009) conducted research based on the initial model F-Score in 2007 on emerging market countries and compared to companies in the United States.…”

Section: Introductionmentioning

confidence: 86%

The Effect of Fraud Pentagon and F-Score Model in Detecting Fraudulent Financial Reporting in Indonesia

Putra¹,

Dinarjito²

2021

Jurnal Ilmiah Akuntansi dan Bisnis

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This study analyzes the effect of fraud pentagon and the efficacy of the F-score model in detecting fraudulent financial reporting in Indonesia. The sample used is the company’s annual report subject to Financial Service Authority (OJK) sanctions and a twofold comparative annual study from companies not subject to sanctions in the same reporting year and industry type. The sample was selected using a purposive sampling method. The data was analyzed using the logistic regression analysis. The test results prove that pressure, opportunity, and rationalization in the fraud pentagon can be used to detect bogus financial reporting. The results reveal that the F-score model is unsuitable for hypothesis testing; consequently, some variables should be eliminated. Therefore, the F-score model has limited usefulness in detecting counterfeit financial reporting in Indonesia. Keywords: F-Score, accounting fraud, fraud detection, fraud pentagon, fraudulent financial reporting

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Section: Introductionmentioning

confidence: 86%

The Effect of Fraud Pentagon and F-Score Model in Detecting Fraudulent Financial Reporting in Indonesia

Putra¹,

Dinarjito²

2021

Jurnal Ilmiah Akuntansi dan Bisnis

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“…The likelihood of fraud is calculated according to the F-score model, which the fraud risk exists when the F-score value is more than 1, and the risk is considered high and very high when the F-score value is more than 1.85 and 2.45 respectively. The model utilized by Hung et al (2017) was also shown to have a fraud predictive capacity of approximately 78%. Aghghaleh et al (2016) empirically investigated the fraud detection and predictive capabilities of the Beneish M-score and the Dechow F-core based on the financial ratios of listed companies in Malaysia from 2001 to 2014.…”

Section: Altman Z-scorementioning

confidence: 99%

“…Conversely, an F-score of less than 1 indicated that there is relatively low risk of financial misstatements. Hung et al (2017) used the F-score model by Dechow et al (2011), to investigate the probability of fraudulent financial reporting in the financial statements of firms listed on the Ho Chi Minh Stock Exchange in Vietnam. They also re-examined the relationship between all the F-score components (RSST accrual (Rsstacc), change in receivables (Chrec), change in inventory (Chinv), soft assets (Softassets), change in cash sales (Chcs), change in ROA (Chroa), actual issuance of stock (Issue), return on assets (ROA), the size of enterprises by revenue (Size) and financial leverage (LV)) and the likelihood of fraud.…”

Section: Altman Z-scorementioning

confidence: 99%

“…In addition, prior studies documented that financial statement fraud detection models such as Beneish M score, Dechow F score and Altman Z-score are highly accurate and important measurement in fraud detection (Omar et al 2014;Hung et al 2017;MacCarthy 2017). Are the auditors aware and do they apply these fraud detection models to assess the fraud risk when they do the planning and performing part of the audit work?…”

Section: Introductionmentioning

confidence: 99%

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Fraud Detection Gap between Auditor and Fraud Detection Models: Evidence from Gulf Cooperation Council

Hakami

Rahmat

Yaacob

et al. 2020

AJAG

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This study investigates an auditor's fraud detection gap (FDG) in Gulf Cooperation Council (GCC) companies by comparing the result of the fraud detection models (namely the Beneish M-score, Dechow F-score, and Altman Z-score) with an actual of audit opinion given by the auditors. Prior scholars documented that financial models are accurate and important measurements in fraud detection. However, the majority of fraud cases in the region are revealed accidentally which indicates the unclear role of the internal and external auditor. The data consists of 365 companies operated in the GCC for the period from 2015 to 2017 with a total of 1,095 observations. The study found that the success rate of detecting financial statement frauds for Dechow F model is much higher than Beneish M or Altman Z models. The result also indicated that the highest FDG-score results were obtained by the Dechow F model. However, the Beneish M model can detect financial statements' fraud better for companies associated to local audit firms as compared to international audit firms. Additionally, Big 4 audit firms are associated with a lower FDG in Beneish M model but increase FDG in Altman Z model. Hence, the study supported the inclusion of statistical models, to a certain extent, as an alternative or supplementary method that assisted in making better decision-making for companies within the Gulf States. The regulators, policy maker, and practitioners, mainly the audit firms must concern that the ability to detect financial statement's fraud can be enhanced by utilizing the appropriate fraud detection model.

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“…/ International Journal of Research in Business andSocial Science, Vol 8 No 4, 2019 ISSN: 2147-4486 Peer-reviewed Academic Journal published by SSBFNET with respect to copyright holders. The F-Score reading result is (Hung, et al (2017)): F-score> 2.45 Very high risk, F-score> 1.85 high risk, F-score> = 1 risk above normal level, and F-score < 1 risk is normal or below.…”

Section: Financial Statement Fraud Riskmentioning

confidence: 99%

Relationship between Financial Ratio and Financial Statement Fraud Risk Moderated by Auditor Quality

Izzalqurny

Subroto²,

Ghofar³

2019

IJRBS

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This study was aimed to prove the research hypothesis that there are effects of financial ratios, which consist of profitability, leverage, and liquidity on the financial statements fraud risk, and the quality of auditors are able to moderate the relationship between financial ratios to financial statements fraud. This study uses a population of manufacturing companies that publish their financial statements on the Indonesian Stock Exchange in 2016-2017 will also be summarized and inferred. This study uses purposive sampling so that the study sample amounted to 275 firm years. The dependent variable uses the financial statements fraud risk with the proxy Dechow F-score. The independent variable in this study consisted of profitability with ROA ratio, leverage using the calculation of the ratio of total liabilities to total assets, and liquidity using the calculation of the ratio of total current assets to current liabilities. The moderating variable in this study is auditor quality as a moderating variable with a dummy variable. The Hypothesis test conducted is using moderated regression analysis (MRA). The results of this study indicate that the financial statements fraud risk is influenced by financial liquidity ratios, while financial ratios of profitability and leverage have not been proven to affect financial report fraud. This study provides a contribution by providing evidence that the quality of auditors can suppress fraudulent actions on financial statements with low profitability. This research provides information to regulators to pay more attention to companies that experience liquidity problems, and become input for regulators to make rules that improve the quality of auditors.

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Application of F-Score in Predicting Fraud, Errors: Experimental Research in Vietnam

Cited by 16 publications

References 13 publications

The Effect of Fraud Pentagon and F-Score Model in Detecting Fraudulent Financial Reporting in Indonesia

The Effect of Fraud Pentagon and F-Score Model in Detecting Fraudulent Financial Reporting in Indonesia

Fraud Detection Gap between Auditor and Fraud Detection Models: Evidence from Gulf Cooperation Council

Relationship between Financial Ratio and Financial Statement Fraud Risk Moderated by Auditor Quality

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