scholarly journals The Effect of Corporate Financial Pressure on Financial Statement Fraud during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Wahyu Manuhara Putra
Keyword(s):  
Financial Statement ◽  
Financial Statement Fraud ◽  
Financial Pressure

scholarly journals The Effect of Hexagon Fraud on the Potential Fraud Financial Statements with the Audit Committee as a Moderating Variable

2021 ◽  
Vol 04 (10) ◽  
Author(s):  
Agustina Riyanti ◽  
Keyword(s):  
Audit Committee ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Financial Statements ◽  
Financial Statement ◽  
Political Connection ◽  
Financial Statement Fraud ◽  
Financial Pressure ◽  
Negative Effect ◽  
Ceo Narcissism

This study aims to obtain empirical evidence of the influence of the six elements of fraud hexagon theory on potential financial statement fraud by involving the audit committee which acts as a moderating variable in the financial statements of state- owned companies listed on the Indonesia Stock Exchange in 2016-2020. Potential financial statement fraud is measured using the f-score model. The research sample was obtained by the purposive sampling technique. Data analysis methods and techniques include descriptive statistics, evaluation of the SEM-PLS model, and hypothesis testing. The results show that financial pressure has a positive effect on potential financial statement fraud, effective monitoring has a negative effect on potential financial statement fraud, while related party transactions, CEO education, CEO narcissism, and political connection do not affect potential financial statement fraud. The role of the audit committee as a moderating variable is only able to weaken the relationship between financial pressure and potential financial statement fraud.

scholarly journals The Effect Of Financial Health Levels To The Indication Of Financial Statement Fraud In Transportation Sector

Riset ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 449-465
Author(s):  
Sharon Callista Hanjaya ◽  
Sylvia Fettry
Keyword(s):  
Public Transportation ◽  
Financial Statement ◽  
Measurement Tool ◽  
Fraud Triangle ◽  
Financial Health ◽  
Transportation Industry ◽  
Financial Statement Fraud ◽  
Financial Pressure ◽  
Triangle Theory ◽  
A Company

Because of app-based transportation, traditional transportation companies are being abandoned because in terms of practicality, they are less accessible to various groups of people. Therefore, traditional transportation companies compete difficultly and have financial pressure. One example of financial pressure is poor financial health. Based on the Fraud Triangle theory, it is said that if a company is under pressure, the possibility of committing fraud will increase. This study is conducted to find empirical evidence whether the level of financial health could affect the possibility of financial statement fraud. This study is expected to provide knowledge to investors and potential investors how to use the Altman's Z-Score analysis method as a measuring tool for financial health level and the Beneish M-Score as a financial statement fraud red flag measurement tool, in order to avoid fraud in the future. The research method used is the method of causality. The data collected is secondary quantitative data in the form of financial statements of companies in the land public transportation industry for the period of year 2015 until 2019. Descriptive and verificative analysis will be done on these data. After conducting the analysis, it can be concluded that the level of financial health has a significant effect on financial statement fraud indications. But there are many other factors that can explain changes in the likelihood of a company committing financial statement fraud such as opportunity, rationalization and various other types of pressure.

Pengaruh Faktor Demografi Dan Iklim Organisasi Terhadap Niat Whistleblowing Internal

2017 ◽  
Vol 4 (1) ◽  
pp. 12-22
Author(s):  
Priyastiwi Priyastiwi
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Organizational Climate ◽  
Demographic Factors ◽  
Financial Statement ◽  
Research Use ◽  
Analysis Method ◽  
Financial Statement Fraud ◽  
Internal Whistleblowing ◽  
Collection Methods

This study aimed to examine the effect of demographic factors and organizational climate on the intention of internal whistleblowing. The sample was an accountant who had worked as an auditor. Data collection methods using questionnaires with financial statement fraud case scenarios. This research use ANOVA data analysis method to examine demographic factors include age, gender, and experience, as well as organizational climate on the intention of internal whistleblowing. The results showed only the experience of demographic factors that influence internal whistleblowing. Besides internal whistlelowing also influenced by organizational climate in the company.Keyword: Demographics, Organizational Climate, Whistleblowing

Fraud of Financial Statements at Listed Enterprises on Ho Chi Minh City Securities Department

2018 ◽  
Vol 34 (4) ◽  
Author(s):  
Nguyen Tien Hung ◽  
Huynh Van Sau
Keyword(s):  
New York ◽  
Financial Reporting ◽  
Financial Statements ◽  
Financial Statement ◽  
Viet Nam ◽  
Fraud Triangle ◽  
Fraudulent Financial Reporting ◽  
Financial Statement Fraud ◽  
Corporate Fraud ◽  
Two Factors

The study was conducted to identify fraudulent financial statements at listed companies (DNNY) on the Ho Chi Minh City Stock Exchange (HOSE) through the Triangular Fraud Platform This is a test of VSA 240. At the same time, the conformity assessment of this model in the Vietnamese market. The results show that the model is based on two factors: the ratio of sales to total assets and return on assets; an Opportunity Factor (Education Level); and two factors Attitude (change of independent auditors and opinion of independent auditors). This model is capable of accurately forecasting more than 78% of surveyed sample businesses and nearly 72% forecasts for non-research firms.  Keywords Triangle fraud, financial fraud report, VSA 240 References Nguyễn Tiến Hùng & Võ Hồng Đức (2017), “Nhận diện gian lận báo cáo tài chính: Bằng chứng thực nghiệm tại các doanh nghiệp niêm yết ở Việt Nam”, Tạp chí Công Nghệ Ngân Hàng, số 132 (5), tr. 58-72.[2]. Hà Thị Thúy Vân (2016), “Thủ thuật gian lận trong lập báo cáo tài chính của các công ty niêm yết”, Tạp chí tài chính, kỳ 1, tháng 4/2016 (630). [3]. Cressey, D. R. (1953). Other people's money; a study of the social psychology of embezzlement. New York, NY, US: Free Press.[4]. Bộ Tài Chính Việt Nam, (2012). Chuẩn mực kiểm toán Việt Nam số 240 – Trách nhiệm của kiểm toán viên đối với gian lận trong kiểm toán báo cáo tài chính. [5]. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of financial economics, 3(4), 305-360.[6]. Võ Hồng Đức & Phan Bùi Gia Thủy (2014), Quản trị công ty: Lý thuyết và cơ chế kiểm soát, Ấn bản lần 1, Tp.HCM, Nxb Thanh Niên.[7]. Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston: Pitman independence on corporate fraud. Managerial Finance 26 (11): 55-67.[9]. Skousen, C. J., Smith, K. R., & Wright, C. J. (2009). Detecting and predicting financial statement fraud: The effectiveness of the fraud triangle and SAS No. 99. Available at SSRN 1295494.[10]. Lou, Y. I., & Wang, M. L. (2011). Fraud risk factor of the fraud triangle assessing the likelihood of fraudulent financial reporting. Journal of Business and Economics Research (JBER), 7(2).[11]. Perols, J. L., & Lougee, B. A. (2011). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39-53.[12]. Trần Thị Giang Tân, Nguyễn Trí Tri, Đinh Ngọc Tú, Hoàng Trọng Hiệp và Nguyễn Đinh Hoàng Uyên (2014), “Đánh giá rủi ro gian lận báo cáo tài chính của các công ty niêm yết tại Việt Nam”, Tạp chí Phát triển kinh tế, số 26 (1) tr.74-94.[13]. Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.[14]. Amara, I., Amar, A. B., & Jarboui, A. (2013). Detection of Fraud in Financial Statements: French Companies as a Case Study. International Journal of Academic Research in Accounting, Finance and Management Sciences, 3(3), 40-51.[15]. Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. Accounting Review, 443-465.[16]. Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36.[17]. Persons, O. S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research (JABR), 11(3), 38-46.[18]. Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 131-146.[19]. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary accounting research, 13(1), 1-36.[20]. Loebbecke, J. K., Eining, M. M., & Willingham, J. J. (1989). Auditors experience with material irregularities – Frequency, nature, and detectability. Auditing – A journal of practice and Theory, 9(1), 1-28. [21]. Abbott, L. J., Park, Y., & Parker, S. (2000). The effects of audit committee activity and independence on corporate fraud. Managerial Finance, 26(11), 55-68.[22]. Farber, D. B. (2005). Restoring trust after fraud: Does corporate governance matter?. The Accounting Review, 80(2), 539-561.[23]. Stice, J. D. (1991). Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. Accounting Review, 516-533.[24]. Beasley, M. S., Carcello, J. V., & Hermanson, D. R. (1999). COSO's new fraud study: What it means for CPAs. Journal of Accountancy, 187(5), 12.[25]. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied statistical models.Richard D. Irwin, Inc., Burr Ridge, IL.[26]. Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.[27]. McFadden, D. (1974). Conditional Logit Analysis of Qualita-tive Choice Behavior," in Frontiers in Econometrics, P. Zarenm-bka, ed. New York: Academic Press, 105-42.(1989). A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Econometrica, 54(3), 1027-1058.[28]. DA Cohen, ADey, TZ Lys. (2008), “Accrual-Based Earnings Management in the Pre-and Post-Sarbanes-Oxley Periods”. The accounting review.

FRAUD TRIANGLE ANALYSES IN DETECTING FRAUDULENT FINANCIAL STATEMENT USING FRAUD SCORE MODEL

2020 ◽  
Vol 25 (1) ◽  
pp. 29-44
Author(s):  
Mariati ◽  
Emmy Indrayani
Keyword(s):  
Financial Stability ◽  
Linear Regression Analysis ◽  
Financial Statements ◽  
Multiple Linear Regression Analysis ◽  
External Pressure ◽  
Financial Statement ◽  
Fraud Triangle ◽  
Financial Statement Fraud ◽  
Financial Condition ◽  
Score Model

Company’s financial condition reflected in the financial statements. However, there are many loopholes in the financial statements which can become a chance for the management and certain parties to commit fraud on the financial statements. This study aims to detect financial statement fraud as measured using fraud score model that occurred in issuers entered into the LQ-45 index in 2014-2016 with the use of six independent variables are financial stability, external pressure, financial target, nature of industry, ineffective monitoring and rationalization. This study using 27 emiten of LQ-45 index during 2014-2016. However, there are some data outlier that shall be removed, thus sample results obtained 66 data from 25 companies. Multiple linear regression analysis were used in this study. The results showed that the financial stability variables (SATA), nature of industry (RECEIVBLE), ineffective monitoring (IND) and rationalization (ITRENDLB) proved to be influential or have the capability to detect financial statement fraud. While the external pressure variables (DER) and financial target (ROA) are not able to detect the existence of financial statement fraud. Simultaneously all variables in this study were able to detect significantly financial statement fraud.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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