Beneish M-Score Model Research Articles

Using the Beneish M-score model: Evidence from non-financial companies listed on the Warsaw Stock Exchange

The risk of distortion of financial statements has been growing. Following the 2008 crisis, recipients of financial information are increasingly focusing on the likelihood of financial statements being distorted through fraudulent presentation of financial information. Therefore, scientific research pays more attention to models capable of detecting financial statement manipulation.The paper aims to present the principles of functioning and the possibility of using the Beneish M-score model in Polish realities. It analyzes the history of more than 30 companies listed on the Warsaw Stock Exchange to select those whose history indicates that they can be classified as manipulators, and to select the same number of companies from the control group that are considered as non-manipulators.The research method involves the analysis of empirical data on companies listed on the Warsaw Stock Exchange. The analysis showed the 8-factor Beneish model identified manipulators with 100% accuracy and succeeded in identifying non-manipulators. The effectiveness of the 5-factor model was much lower. To serve the purpose of the study, the effectiveness of the Beneish model was tested on a small sample of Polish listed companies as an introduction to a planned larger scale research. The results obtained are consistent with the results of numerous studies by authors from various countries and confirm the effectiveness of the Beneish model in detecting financial statement manipulation. AcknowledgmentThe publication is sponsored by funds from the Cracow University of Economics for the maintenance and development of research potential.

Factors Impact Reducing Money Earning Manipulation withintheListed Malaysian Companies; theModerating Role ofAccounting Ethics

This study aims to investigate the impact of three dimensions suggested by Beneish M-Score model on reducing money earning manipulation within listed companies in Malaysia. Also, examining the moderation role of accounting ethics toward the improvement of these factors on reducing money earning manipulation. These three dimensions called asset quality management, accrual quality management, and leverage quality management. This study follows the cross-section method. The questionnaire instrument is used to collect the data from 370 employees in financial and insurance business of Malaysia. The empirical analysis of structural model of the study revealed that the direct relationships of financial management transactions dimensions are significantly positively related to reducing money earning manipulation. The path analysis confirmed moderating effect of accounting ethics has also been found positively significant positive on the relationship between financial management transactions and reducing money earning manipulation. The present study is first of its kind to explore the moderating effect of accounting ethics among financial and insurance business of Malaysia. It suggested that financial management transactions are an important concept for management of financial and insurance organizations, which can considerably influence working environment and reduce money earning manipulation.

Investigating Financial Statement Fraud in Ghana Using Beneish M-Score: A Case of Listed Companies on the Ghana Stock Exchange

This quantitative research was conducted to detect the possibility of earnings manipulation by listed companies on the Ghana Stock Exchange, determine the relationship between company size and earnings manipulation and find out the existence of a correlation between share price and earnings manipulation. Using 22 companies out of a total of 41 listed companies, financial data gathered from published financial statements on the companies’ websites, Ghana Stock Exchange website and Annual Report Ghana website were examined from 2011 to 2016. Applying Beneish M-score model for the period 2011-2016, it was found that 26.2% of the sample size on the average were involved in creative accounting. The study also found that 28.4% of the small companies on the average were involved in earnings manipulation during the period 2011-2016 as compared to 25.4% of the big companies. However, the Mann-Whitney U test conducted revealed that there is no statistically significant difference between the level of earnings manipulation and company size. Spearman’s correlation analysis was conducted, firstly, on the entire sample and separately on the small and big companies. The results of the analysis showed that earnings manipulation and share price, statistically, were not significantly correlated. The quantitative research provides an insight into the level of earnings management amongst listed companies in Ghana and the appropriateness of the M-score model in detecting earnings manipulation. The evidence of incidence of creative accounting amongst the sampled companies is an indication of the need for more stringent measures to curb such practice to ensure the stability of the Ghanaian stock market and protect investor interest.

Dampak dari auditor quality, financial stability, dan financial target terhadap fraudulent financial reporting

Purpose: This study aims to provide empirical evidence of the effect of auditor quality, financial stability, and financial targets on fraudulent financial reporting. Research methodology: This research was quantitative descriptive combining the Beneish M-Score and Springate's Model in predicting fraudulent financial reporting. The sample in this study was non-financial companies listed on the Indonesia Stock Exchange with an observation period of 2010-2018. The sampling technique was purposive sampling and 1120 observations were obtained. Data processing was done using SPSS version 22 with logistic regression. Results: The results of the study prove that the auditor quality variable has a probability of a negative effect on FFR where the companies audited by BIG4 are able to minimize fraudulent financial reporting. Financial targets have negative effect on FFR, which means the lower the financial target, the greater the probability of the company to do fraudulent financial reporting. While the financial stability variable is negatively expected influencing the fraudulent financial reporting variable. Limitations: This study only used a sample of non-financial companies listed on the Indonesia Stock Exchange in 2010-2018 and met the criteria. The dependent variable fraudulent financial reporting was measured by the Beneish M-Score model and the Springate’s Model. The independent variable was auditor quality, financial stability, and financial target. Contribution: This research is expected to be able to enrich the literature and references that can be used as a reference in other studies as well as in the company. The results of this study are expected to provide a deeper understanding of how to predict fraudulent financial reporting using the Beneish M-Score and Springate’s Model. Keywords: Financial reporting, Auditor quality, Financial stability, Financial target

The creative accounting in determining the bankruptcy of Business Corporation

If there is a period of unfavourable to critical economic development Business Corporation, there is no doubt about the significance of determining the actual factual moment of bankruptcy of the business corporation. In such a situation, it is crucial for further development approach of the statutory body of Business Corporation in terms of truly evaluation and timely search for a constructive solution. There are different methods of creative accounting to delaying the impending corporation bankruptcy. This paper describes the individual ways in which the creative accounting can favour the business corporation, e.g. increase the reported earnings or reduce the reported loss, manipulate with indicators used in financial analysis, conceal financial risk or strengthen the company´s access to finance. The reason for using these practices by the company management can be just a simple delay in solving the situation, trying to keep the company position as long as possible or the reasons are more complex and sophisticated. There are many model to predict the adjusting financial statements. We described and applied two of them, the CFEBT model and BENEISH M-score model.

Investigating Financial Statement Fraud in Ghana using Beneish M-Score: A Case of Listed Companies on the Ghana Stock Exchange (GSE)

This quantitative research was conducted to detect the possibility of earnings manipulation by listed companies on the Ghana Stock Exchange, determine which company size engages more in creative accounting and find out whether there is a correlation between share price and earnings manipulation. Using 22 companies out of a total of 41 listed companies, financial data gathered from published financial statements on the companies’ websites, Ghana Stock Exchange website and Annual Report Ghana website were examined from 2011 to 2016. Applying Beneish M-score model for the period 2011-2016, it was found that 26.2% of the sample size on the average were involved in creative accounting. The study also found that 28.4% of the small companies on the average were involved in earnings manipulation during the period 2011-2016 as compared to 25.4% of the big companies. However, the Mann-Whitney U test conducted revealed that there is no statistically significant difference between the level of earnings manipulation amongst small and big companies. Spearman’s correlation analysis was conducted, first on the entire sample and subsequently conducted separately on the small and big companies. The results of the analysis showed that earnings manipulation and share price, statistically, were not significantly related. The quantitative research provides an insight into the level of earnings management amongst public companies in Ghana and the appropriateness of the M-score model in detecting earnings manipulation. The evidence of incidence of creative accounting amongst the sampled companies is an indication of the need for more stringent measures to curb such practice to ensure the stability of the Ghanaian stock market and protect investor interest.

Evaluasi Keakuratan Model Beneish M-Score Sebagai Alat Deteksi Kecurangan Laporan Keuangan (Kasus Perusahaan Pada Otoritas Jasa Keuangan di Indonesia)

This research has been conducted aiming to see in more detail whether the fraud detection model that has been used so far, the Beneish M-Score, is capable of being one of the tools to see financial report fraud occurring in the business world. This is interesting to study considering that many companies in the Financial Services Authority (OJK) in Indonesia receive warnings and even fines for the delay in submitting financial reports to Capital Market Authority.To carry out the analysis process as in the objectives in the first paragraph, the research team took a sample of 23 companies on the Indonesia Stock Exchange, where the companies were in the list of OJK. The 23 companies that were sanctioned by the OJK compared to 23 not sanctioned companies. In sample of companies that were sanctioned by the OJK, the number of non-manipulator companies according to the Beneish M-Score calculation was 62% and for companies included in the manipulator classification only 38%. Whereas in the sample of companies not subject to sanctions from the OJK, the number of companies included in the non-manipulator category is actually smaller, 52%, calculated using M-Score. This is the main basis for further research.In this study, the analysis process is carried out by quantitative explanatory analysis using probit regression models (probit models), on financial statement data which are categorized into two, the financial ratio with original data from OJK (audited) and the financial ratio with data modification (advanced business analysis). The results show that Beneish M-Score Model could not be implemented effectively to detect the fraud in the companies under control by OJK because only 2 (two) variables influence the existence of fraudulent, are Asset Quality Index (AQI) and Total Accrual To Total Assets (TATA). Thus, it is appropriate and important for the Beneish M-Score modeling to be equipped with other models that are more able to explain.

Corporate Governance and Financial Statement Fraud among Listed Firms in Nigeria

The study looked into the association which exists amid financial statement fraud and governance among business organizations in Nigeria. A population of 122 non-financial companies registered on Nigeria stock exchange was limited to 20 firms employing the rule of thumb based on stratified and simple random technique for a period of 2012-2016. The method of data analysis is panel regression. The dependent variable, fraud in the financial statement was measured using the Beneish M-score model while the independent variable was measured using audit committee independence, board structure. Findings show that an insignificant association exist amid audit committee independence, the composition of the board and financial statement fraud. This research design suggests regarding the reduction of the occurrence of financial statement fraud, less emphasis should be placed on audit committee independence, board composition and independent non-executive directors’ effectiveness.

PENDETEKSIAN KECURANGAN LAPORAN KEUANGAN DENGAN BENEISH M-SCORE PADA PERUSAHAAN L-Q 45

This study aims to analyze the effect of fraud triangle that is proxied bypressure, opportunity, and rationalization to influence fraud that occurs in thecompany's financial statements. The research method was carried out by usingquantitative research with 40 samples taken in the LQ45 in the period 2015-2017. The type of data used is secondary data. Analysis of the data used is theBeneish Ratio Index, which is then included in a model, the Beneish M-ScoreModel using five calculated Days Days in Receivable Index (DSRI), Gross marginIndex (GMI), Asset Quality Index (AQI), Sales Growth Index (SGI),Depreciation Index (DEPI). The results of this study indicate that based on theBeneish M-Score, that ROA has no significant effect on FFR. AGROW has aneffect on FFR. LEV has no significant effect on FFR.

PENDETEKSIAN POTENSI KECURANGAN PELAPORAN KEUANGAN DENGAN BENEISH MODEL (STUDI PADA PERUSAHAAN BADAN USAHA MILIK NEGARA YANG TERDAFTAR DI BEI)

As the world is becoming more globalized than before, financial market participants especially in Indonesia face serious risk for dealing with fraudulent financial reporting. This study aims to detect potential fraud reporting by using Beneish M-Score Model. Sample in this studi is Indonesia State Owned Enterprises who listed in Indonesia Stock Exchange from 2016 to 2018. Our evidence conclude that Beneish Model supports effectively in analyzing characteristics of falsified financial statements.

PREDICTING FINANCIAL STATEMENTS CORPORATE FRAUD: BENEISH M-SCORE MODEL

This research aims to detect fraudulent financial statements the financial statements of XYZ, PT in the period 2010-2013. This research is a qualitative descriptive research. Data collection techniques used are observation and documentation. Data analysis techniques used are data reduction, data presentation and conclusion with Beniesh M-Score Model. The results showed that the financial statements in the period 2010-2013 as a whole that Beniesh M-Score XYZ, PT reached -3.94 in 2010, 0.70 in 2011, -3.32 in 2012 and -2.38 in 2013, the overall Beniesh M-Score reached above -2.22 except in 2011. Beniesh M-Score Model indicates that the management has performed the practice of manipulating numbers on the financial statements conducted continuously in the period of 2010, 2012 and 2013. The authors suggest the regulation (tax office) and public accounting firms, namely that the regulators do a review/inspection directly on the company's assets that has been recorded in the financial statements and the external auditor who has been appointed to conduct an examination of the financial statements to be more independent and do detection of financial statements using ratio analysis.Keywords: Detection, Fraudulent Financial Statements, Beneish M-Score Model, Practice of Manipulation Number

The Comparison of Two Data Mining Method to Detect Financial Fraud in Indonesia

Objective - This research is expected to improve the weaknesses in the research conducted by Tarjo and Herawati (2015). The objective of this study was to analyse two data mining methods in detecting financial fraud based on Beneish m-score model. Methodology/Technique - The research data were companies who committed fraud based Database Case Sanctions Issuers and Public Companies which was released by the Financial Services Authority in the period 2001-2014. For comparison, researchers also used data from companies that did not commit fraud. Companies were selected based on the same industry group of companies committing fraud for the purposes of classification. Findings - The results show that data mining methods can be used to detect financial fraud based on Beneish m-score model. However, there are differences in the classification. In the logit regression, the results are only limited to the accuracy of classification and weak. While the K-Nearest Neighbor model, in addition, it is capable of performing high classification accuracy. Novelty - The study indicates a better method for detecting financial fraud. Type of Paper Empirical Keywords: Detecting Financial Fraud, Beneish M-Score, Logit Regression, K-Nearest Neighbor. JEL Classification: C81, M41.

Detecting Financial Statement Frauds in Malaysia: Comparing the Abilities of Beneish and Dechow Models

Financial statement frauds ( FSF ) are becoming rampant phenomena in current economic and financial landscapes. One of the ways to curb FSF is to detect them early so that preventive measures can be applied. This study aims to empirically investigate the abilities of two financial-based models namely the Beneish’s M-score and Dechow’s F-score, to detect and predict FSF for Malaysian companies. In addition, this study compares the accuracy including the error rates between the two models. Financial data of Malaysian listed companies from 2001 to 2014 are used using a matched pair in this study. The findings reveal that both Beneish and Dechow models are effective in predicting both the fraudulent and non-fraudulent companies with average accuracy at 73.17% and 76.22%, respectively. The results also indicate that Dechow F-score model outperforms the Beneish M-score model in the sensitivity of predicting fraud cases with 73.17% compared to 69.51%. On the efficiency aspect, the Dechow F Score model is found to have lower type II error (26.83%) than Beneish M Score model (30.49%). This finding suggests that Dechow F Score model is a better model that can be used by the regulators to detect FSF among companies in Malaysia.

A complete literature review on financial fraud detection applying data mining techniques

Financial fraud is defined as unlawful or criminal duplicity attempted to result to organisational or personal gain. It is a big threat to the economics of a firm, corporate sector, government or ordinary customers in the form of credit card fraud, insurance fraud, and financial statement fraud. Several approaches exist in the literature of financial fraud detection. But, due to the inefficiency of those approaches, researchers leverage data mining techniques to detect financial fraud. This paper aims to build a systematic academic review of financial fraud detection approaches based on data mining techniques in the recent years. In the practice, different data mining techniques namely: K-nearest neighbour, decision tree, fuzzy logic, logistic model, Bayesian belief network, naïve Bayes, Beneish M-Score model, Benfords law, Altman Z-score have been applied to improve the accuracy of fraud detection. In this paper, existing financial fraud detection techniques are compared with the advantages and limitations of the techniques.

Detecting Financial Statement Fraud by Malaysian Public Listed Companies: The Reliability of the Beneish M-Score Model

ABSTRACT Various fraud prediction tools have been developed to detect financial statement fraud triggered by earnings manipulation. Among them is the Beneish M-Score model as a financial forensic tool to gauge potential earnings manipulation in firms’ financial statements. The model was found to be effective in detecting 76% of earnings manipulating firms subjected to accounting enforcement actions by the United States Securities and Exchange Commission (U.S. SEC). Furthermore, the earnings manipulation model was also successful in discovering 71% of the most prominent fraudulent financial reporting scandals in the United States prior to public announcement. The current study assesses the reliability of the Beneish M-Score model in detecting earnings manipulation and financial statement fraud committed by Malaysian public listed companies prior to public announcement. The research sample consists of 17 public listed companies of which their directors and top management have been charged and prosecuted by the Securities Commission Malaysia (SC) for committing fraudulent reporting and misstatement from 1996 until 2014. The results show that the Beneish M-score model is reliable in detecting earnings manipulation and financial statement fraud by 82% in 14 out of 17 listed companies charged for fraudulent financial reporting. The findings provide support for the application of the Beneish M-Score model by the management of the company to check for any irregularities in firms’ financial report so that adjustment can be made before submission to Bursa Malaysia to prevent from any potential backlash that could damage firm reputation. The model application is likewise beneficial to prospective and existing shareholders to assist their investment decision making in reducing risk of losses due to fraud. In addition, the findings suggest that the model can be applied by researchers, auditors, and enforcement agencies as an effective detection tool to signal potentially fraudulent reporting companies in Bursa Malaysia for further investigation and enforcement action. Keywords: Earnings manipulation; Beneish M-Score model; financial statement fraud ABSTRAK Di dalam mengesan penipuan penyata kewangan yang dicetuskan oleh manipulasi pendapatan, pelbagai model ramalan penipuan telah dibangunkan baru-baru ini. Antaranya ialah model Beneish M-Score yang telah digunakan sebagai alat forensik kewangan untuk mengesan potensi manipulasi pendapatan dalam penyata kewangan firma, yang dianggap sebagai menyalahi undang-undang dan peraturan perakaunan Amerika Syarikat. Model ini telah didapati berkesan dalam mengesan 76% daripada syarikat-syarikat yang telah memanipulasi pendapatan yang tertakluk kepada tindakan penguatkuasaan perakaunan oleh Suruhanjaya Sekuriti dan Bursa Amerika Syarikat (SEC). Tambahan pula, model pengesanan manipulasi pendapatan ini juga telah berjaya mengesan 71% daripada syarikat-syarikat yang terlibat di dalam skandal penipuan laporan kewangan yang paling menonjol di Amerika Syarikat sebelum pengumuman awam dilaksanakan. Kajian ini bertujuan untuk menilai keberkesanan model Beneish M-Score untuk mengesan manipulasi pendapatan dan penipuan penyata kewangan yang dilakukan oleh syarikat-syarikat tersenarai di Bursa Malaysia sebelum pengumuman awam dilakukan. Sampel kajian ini terdiri daripada 17 syarikat-syarikat tersenarai di Bursa Malaysia di mana pengarah dan pengurusan tertinggi berkaitan telah didakwa oleh Suruhanjaya Sekuriti Malaysia (SC) kerana melakukan penipuan laporan kewangan dan salah nyata dari tahun 1996 sehingga 2014. Keputusan kajian ini mendapati bahawa model Beneish M-score berkesan di dalam mengesan manipulasi pendapatan dan penipuan penyata kewangan pada kadar 82% bagi 14 daripada 17 syarikat-syarikat yang telah didakwa oleh Suruhanjaya Sekuriti Malaysia (SC) kerana melakukan penipuan laporan kewangan dan salah nyata. Penemuan kajian ini juga menyokong pengaplikasian model Beneish M-score oleh pihak pengurusan syarikat yang tersenarai di Bursa Malaysia untuk mengesan potensi penyelewengan di dalam laporan kewangan berkaitan supaya pelarasan boleh dilakukan sebelum dikemukakan kepada Bursa Malaysia, bagi mengelak daripada sebarang tindak balas yang berpotensi menggugat reputasi syarikat jika penipuan dikesan. Pengaplikasian model ini juga berfaedah dan boleh memberi manfaat kepada pemegang saham berpotensi dan sedia ada untuk membantu meraka bagi membuat keputusan pelaburan yang tepat bagi mengurangkan risiko kerugian disebabkan oleh penipuan penyata kewangan. Di samping itu, model Beneish-Mscore ini boleh dimanfaatkan oleh penyelidik, juruaudit dan agensi-agensi penguatkuasaan sebagai alat pengesanan penipuan penyata kewangan yang berkesan bagi memberi isyarat kepada mereka untuk melakukan siasatan selanjutnya sebelum melaksanakan tindakan penguatkuasaan. Kata kunci : Manipulasi pendapatan; model Beneish M-Score; penipuan penyata kewangan

Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud

This study aims to analyze the ability of m-score Beneish in detecting financial fraud. This research data refer to companies that commit fraud according to the fraud Database of Sanctions of Issuer Cases Public Companies that was released by the Financial Services Authority in the period of 2001-2014. The results showed that overall Beneish m-score model was capable to detect financial fraud. Gross margin index, depreciation index, index of sales and general administrative burden and total accruals were all significant in detecting financial fraud. Sales index, asset quality index, and leverage index was statistically not significant in detecting financial fraud.

Prediction of Business Failure and Fraudulent Financial Reporting: Evidence from Malaysia

Fraud has been of concern for many corporate managers and regulators since many businesses have been victim of business failures. Business failure corporations have reported and become an element of fraudulent financial reporting.Therefore, the first aim of this study is to examine whether a collective prediction tool can be used to predict business failure and fraudulent financial reporting.The second and more important objective of this study is to examine whether business failure companies are associated with fraudulent financial reporting. The collective prediction tool is based on ratio analysis, Beneish M-score model and Z-score model. Using publicly available information in the annual reports of 24 failed firms matched with 24 non-failed firms listed on Bursa Malaysia, relevant information are extracted and applied in the three models to assist in predicting business failure and detecting fraudulent financial reporting. A total of 10 ratios, cash conversion cycle, Beneish M-score model and Altman's Z-score model were identified for examination as potential predictors of business failure and fraudulent financial reporting. Based on the results, the model was accurate in classifying the total sample of approximately 96 per cent as predictors of business failure and approximately 83.3 per cent as predictors of fraudulent financial reporting as well as predicting the relationship between business failures and fraudulent financial reporting. Hence, it can be concluded that these model functions effectively and can be adopted by regulators, bankers, management, internal and external auditors and to the forensic accountants so that proper preventive or corrective action can be taken to mitigate fraud at its inception.

Disclosure of Corporate Governance Structure and the Likelihood of Fraudulent Financial Reporting

The purpose of this study is to examine the relationships between corporate governance structure and the likelihood of fraudulent financial reporting. Likelihood of fraudulent financial reporting is based on an integration of Beneish M-score model and Altman's Z-score model. These relationships are examined based on content analysis of annual reports of 227 Public Listed Companies in Malaysia for the year 2010-2011. Results of this study provide evidence that the effectiveness of corporate governance structure reduces the likelihood of fraudulent financial reporting. These results indicate that effective corporate governance structure is paramount in enhancing the credibility of financial reporting.