scholarly journals A Comparative Analysis on the Relative Success of Mixed-Models for Financial Statement Fraud Risk Estimation

2015 ◽  
Vol 14 (24224) ◽  
pp. 65-88
Author(s):  
Mustafa UĞURLU ◽  
Şerafettin SEVİM
Keyword(s):  
Comparative Analysis ◽  
Mixed Models ◽  
Risk Estimation ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Fraud Risk ◽  
Relative Success

Scoring to estimate the financial statement fraud risk during the audit

2021 ◽  
Vol 22 (11) ◽  
pp. 1262-1275
Author(s):  
Sergei V. ARZHENOVSKII ◽  
Tat'yana G. SINYAVSKAYA ◽  
Andrei V. BAKHTEEV
Keyword(s):  
Discriminant Analysis ◽  
Statistical Method ◽  
Empirical Data ◽  
Financial Statements ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Two Stage ◽  
Preparation Methods ◽  
Behavioral Traits ◽  
Fraud Risk

Subject. This article assesses the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation, based on their behavioral traits. Objectives. The article aims to develop a scoring type methodology for identifying the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation. Methods. For the study, we used a multidimensional statistical method of discriminant analysis based on empirical data from an author-conducted survey of 515 employees charged with the financial statements preparation in companies. Results. The article presents a two-stage methodology that helps estimate whether a person has traits associated with a hyperpropensity for financial statements fraud risk. Conclusions and Relevance. The developed methodology for detecting the fraud risk is easy to use. It gives the result in binary form and does not violate the principles of audit ethics. The estimated material misstatement risk due to unfair actions makes it possible to justify the need for appropriate audit procedures when developing a strategy and audit plan.

Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms

2011 ◽  
Vol 30 (2) ◽  
pp. 19-50 ◽  
Author(s):  
Johan Perols
Keyword(s):  
Machine Learning ◽  
Analyst Forecasts ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Financial Statement ◽  
Support Vector ◽  
Classification Algorithms ◽  
Employee Productivity ◽  
Financial Statement Fraud ◽  
Fraud Risk

SUMMARY This study compares the performance of six popular statistical and machine learning models in detecting financial statement fraud under different assumptions of misclassification costs and ratios of fraud firms to nonfraud firms. The results show, somewhat surprisingly, that logistic regression and support vector machines perform well relative to an artificial neural network, bagging, C4.5, and stacking. The results also reveal some diversity in predictors used across the classification algorithms. Out of 42 predictors examined, only six are consistently selected and used by different classification algorithms: auditor turnover, total discretionary accruals, Big 4 auditor, accounts receivable, meeting or beating analyst forecasts, and unexpected employee productivity. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models. Data Availability: A list of fraud companies used in this study is available from the author upon request. All other data sources are described in the text.

scholarly journals Fraud Risk Factors of Fraud Triangle and the Likelihood of Fraud Occurrence: Evidence from Malaysia

2014 ◽  
Vol 6 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Shabnam Fazli Aghghaleh ◽  
Zakiah Muhammaddun Mohamed .
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Logistic Model ◽  
Audit Committee ◽  
Financial Statement ◽  
Fraud Triangle ◽  
Financial Statement Fraud ◽  
Fraud Risk ◽  
Available Information ◽  
Binary Logistic Model

The current research studies the usefulness of Cressey’s fraud risk factor framework adopted from SAS No. 99 to prevent fraud from occurring. In accordance with Cressey’s theory, pressure, opportunity and rationalization are existing when fraud occurs. The study suggests variables as proxy measures for pressure and opportunity, and test these variables using publicly available information relating to a set of fraud firms and a sample of no-fraud firms. Two pressure proxies and two opportunity proxies are identified and suggested to be significantly related to financial statement fraud. We find that leverage and sale to account receivable are positively related to the likelihood of fraud. Audit committee size and board of directors’ size are also linked to decrease the level of financial statement fraud. A binary logistic model based on examples of fraud risk factors of fraud triangle model measures the likelihood of financial statement fraud and can assist experts.

scholarly journals Financial Statement Fraud Risk Factors of Fraud Triangle: Evidence From Indonesia

2020 ◽  
Vol 11 (4) ◽  
pp. 36
Author(s):  
Hasni Yusrianti ◽  
Imam Ghozali ◽  
Etna Yuyetta ◽  
Aryanto Aryanto ◽  
Eka Meirawati
Keyword(s):  
Risk Factors ◽  
Financial Stability ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statement ◽  
Company Size ◽  
Fraud Triangle ◽  
Financial Statement Fraud ◽  
Fraud Risk ◽  
Industry Risk

The purpose of this study is to examine the risk factors that influencing financial statement fraud. Especially, it examines the influence of rationalization, pressure, and opportunity on the fraudulent financial statements and also examines the interaction effect of industry risk and company size on the relationship between rationalization, pressure, and opportunity on financial statement fraud. Secondary data were collected from Bloemberg Data Base, IDX and OJK RI. The population in this study is companies listed on the Indonesia Stock Exchange in the moving year from 2011 to 2017 and the sample was selected by companies that indicated financial statement fraud and those that did not indicate financial statement fraud. The company indicated by Fraud was collected from Bapepam and OJK RI. Data were tested using logistic regression analysis and different T-tests of 28 committed fraud companies and 28 companies that did not commit fraud. The results showed that only some variables had a significant effect on financial statement fraud, namely financial stability (ACHANGE), Financial Target (ROA), and the Nature of Industry (ARCHANGE). The results also show that company size and industry risk do not moderate the fraud factors on financial statement fraud. These results support the fraud triangle theory in explaining the phenomena of financial statement fraud.

Predicting Vietnam Listed Firms’ Financial Statement Fraud Risk

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Thuy Nguyen Thi Hong
Keyword(s):  
Financial Reporting ◽  
Stock Exchange ◽  
Financial Statements ◽  
Listed Companies ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Financial Leverage ◽  
Research Results ◽  
Listed Firms ◽  
Fraud Risk

Different from previous studies in Vietnam, this paper focuses on fraud risk, identifying the factors that affect the risk of financial reporting fraud of listed companies in Vietnam. Moreover, the research aims to forecast the possibility of fraudulent financial statements of listed companies in Vietnam. Based on M-score models (Beneish, 1999) and F-score model (Dechow et al., 2011), this research develops experimental results based on a sample of 307 companies with 3684 financial statements observations from 2007 to 2018. Research results show that the higher financial leverage in firms’ financial statements, the higher risk of financial statements, and the higher fraud’s tendency. Moreover, the findings also show that perennial firms, bigger firms, and listed firms, they likely to have a higher tendency of financial statement fraud. Research results show that the higher the financial leverage ratio, the more errors in reporting, the higher the tendency for fraud. At the same time, the older a business, the larger its scale and listed on the stock exchange, the more likely it is that the financial statements are fraudulent.

scholarly journals Pengaruh Fraud Risk Factor Menurut Fraud Pentagon terhadap Kecurangan Laporan Keuangan

2020 ◽  
Vol 2 (2) ◽  
pp. 127
Author(s):  
Maria Sofa ◽  
Mu'minatus Sholichah
Keyword(s):  
Real Estate ◽  
Financial Stability ◽  
Stock Exchange ◽  
Sample Selection ◽  
External Pressure ◽  
Financial Statement ◽  
Political Connection ◽  
Financial Statement Fraud ◽  
Fraud Risk ◽  
Study Population

This study aims to test the effect of pentagon fraud on financial statement fraud on property and real estate companies. This study selected property and real estate sub-sector companies listed on the Indonesia Stock Exchange in 2016-2018 as the study population. The sample selection method is done by using a purposive sampling method and obtains 34 companies as research samples with 102 observations. Testing in this study uses logistic regression that was tested with the help of SPSS 15. The results obtained from this study explain that financial targets, auditor quality, and the frequent number of CEO picture's effect on financial statement fraud, while financial stability, external pressure, managerial ownership, nature of the industry, ineffective monitoring, auditor change, auditor opinion, change in director, and political connection of CEO do not affect financial statement fraud.

Fraud Risk Brainstorming at Tesla Motors

2017 ◽  
Vol 33 (2) ◽  
pp. 19-34 ◽  
Author(s):  
Megan F. Hess ◽  
Lindsay M. Andiola
Keyword(s):  
Financial Statements ◽  
Learning Objectives ◽  
Assessment Process ◽  
Financial Statement ◽  
Learning Assessment ◽  
Financial Statement Fraud ◽  
Risk Assessment Process ◽  
Fraud Risk ◽  
Available Information ◽  
Group Component

ABSTRACT This instructional case offers students the opportunity to explore the fraud risk assessment process and participate in a simulated fraud brainstorming session as required by AS 2401 (formerly SAS 99) for financial statement audits. Drawing on publicly available information about Tesla, Inc. (formerly Tesla Motors), the revolutionary company behind the popular Model S all-electric vehicle, the case materials guide students through multiple learning objectives. These objectives include learning how to: (1) recognize the factors that contribute to financial statement fraud risk; (2) identify and evaluate the likelihood and severity of fraud risks; (3) analyze the ways that fraud risks can lead to material misstatements in the financial statements; (4) understand the purpose of and how to conduct a fraud brainstorming session; and (5) develop audit procedures that respond to assessed fraud risks. In a post-case learning assessment, students reported significant improvement in their knowledge, comprehension, and application of these learning objectives. Students also indicated that they enjoyed learning about these concepts in the context of this popular company. This case has both an individual and a group component, and it is designed for use in an auditing or forensic accounting course at either the undergraduate or the graduate level.

scholarly journals Detecting Financial Statements Fraud: the Evidence from Russia

2017 ◽  
Vol 11 (2) ◽  
pp. 32-45
Author(s):  
Наталья Валерьевна Ферулева ◽  
Мария Александровна Штефан
Keyword(s):  
Stock Exchange ◽  
Limited Liability ◽  
Probit Model ◽  
Financial Statements ◽  
General Purpose ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Fraud Risk ◽  
Net Assets ◽  
Accounting Policy

Russian stakeholders of joint stock companies, which shares are not traded on a stock exchange, and limited liability companies need the effective instruments which enable them to detect the facts of financial statement fraud quickly because the financial statement remains the main source of information about the companies’ performance for them.  Although Institute of Auditors is one of the most reliable tools which identify financial statement manipulations, the costs, connected with audit, are too high and, and as a result, stakeholders have to look for other instruments to distinguish fraudsters, which make an attempt to overestimate or underestimate net assets and financial results, from non-fraudsters. Mathematical model of the American researcher Messod Beneish can be considered as an example of such tools. The general purpose of this paper is to identify whether it is possible, basing on the Beneish model, to create a new one, which enables to distinguish fraudulent from non-fraudulent financial statements reporting in Russia, and determine the accuracy level of fraud status forecasts made by using this model.  In our research we are going to concentrate on identification of companies, which overestimate net assets and financial results. Tо obtain the information on the financial ratios included in the model we use financial reports of Russian both non-traded joint stock companies and limited liability firms. The conclusion can also be drawn that it is possible to develop the fraud detection probit model and linear model (integrated M-score index), which enabled stakeholders to identify fraud status correctly in 83% and 60 % respectively. Developing the model we include extra parameters, connected with growth rate of other income to sales ratio and an accounting policy of the company. It was found that fraud risk increases if the company chooses accounting policy according to which administrative costs are charged to core product expenses.

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