Effects Of A Decision Aid For The Assessment Of Fraudulent Financial Reporting: An Application Of SAS No. 82

The Statement on Auditing Standards (SAS) No.82, Consideration of Fraud in a Financial Statement Audit, requires the auditor to assess the risk of material misstatement due to a fraud and to consider the assessment in designing appropriate audit procedures to be performed. The SAS No. 82 has thus explicitly made the detection of material fraud the auditor’s responsibility. The purpose of the study is to use the risk factors identified in SAS No. 82 as the foundation to develop a decision aid to help auditors assess the likelihood of fraudulent financial reporting and to empirically test the effects of the decision aid on assessing the likelihood of fraudulent financial reporting. Using a sample of 45 fraud engagements and 206 nonfraud engagements, we developed and tested a logistic regression model that estimates the likelihood of fraudulent financial reporting. We found that the logistic model (proxied as a decision aid in the study) outperforms the practicing auditors in assessing risk for fraud and nonfraud cases.

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A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting

Auditing A Journal of Practice & Theory ◽

10.2308/aud.2000.19.1.169 ◽

2000 ◽

Vol 19 (1) ◽

pp. 169-184 ◽

Cited By ~ 207

Author(s):

Timothy B. Bell ◽

Joseph V. Carcello

Keyword(s):

Risk Factors ◽

Financial Reporting ◽

Internal Control ◽

Decision Aid ◽

Significant Risk ◽

Control Environment ◽

Fraudulent Financial Reporting ◽

Fraud Risk ◽

Financial Statement Audit ◽

Significant Difference

The auditor's responsibility for detecting fraudulent financial reporting is of continuing importance to both the profession and society. The Auditing Standards Board has recently issued SAS No. 82, Consideration of Fraud in a Financial Statement Audit, which makes the auditor's responsibility for the detection of material fraud more explicit without increasing the level of responsibility. Using a sample of 77 fraud engagements and 305 nonfraud engagements, we develop and test a logistic regression model that estimates the likelihood of fraudulent financial reporting for an audit client, conditioned on the presence or absence of several fraud-risk factors. The significant risk factors included in the final model are: weak internal control environment, rapid company growth, inadequate or inconsistent relative profitability, management places undue emphasis on meeting earnings projections, management lied to the auditors or was overly evasive, the ownership status (public vs. private) of the entity, and an interaction term between a weak control environment and an aggressive management attitude toward financial reporting. The logistic model was significantly more accurate than practicing auditors in assessing risk for the 77 fraud observations. There was not a significant difference between model assessments and those of practicing auditors for the sample of nonfraud cases. These findings suggest that a relatively simple decision aid performs quite well in differentiating between fraud and nonfraud observations. Practitioners might consider using this model, or one developed using a similar procedure, in fulfilling the SAS No. 82 requirement to “assess the risk of material misstatement of the financial statements due to fraud.”

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The Consideration Of Fraud In A Financial Statement Audit: Some Study Questions

Journal of Business Case Studies (JBCS) ◽

10.19030/jbcs.v3i1.4832 ◽

2007 ◽

Vol 3 (1) ◽

pp. 37-44

Author(s):

Eddie Metrejean ◽

Lou X. Orchard ◽

Dwight Sneathen Jr

Keyword(s):

Financial Reporting ◽

Major Part ◽

Task Force ◽

Financial Statement ◽

Accounting Students ◽

Fraudulent Financial Reporting ◽

Financial Statement Audit ◽

Auditing Standards

In October 2002, the Auditing Standards Board (ASB) issued Statement on Auditing Standards (SAS) No. 99, Consideration of Fraud in a Financial Statement Audit in response to recommendations from the Fraud Task Force. SAS No. 99 is intended to improve auditor performance during audits and to increase the likelihood that the auditors will detect fraudulent financial reporting if any is present. Since fraud awareness is such a major part of any audit, accounting students should be well versed on the content of SAS No. 99. However, not all accounting students read SASs in detail. Then how do accounting educators get this important content to these students?

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Fraud Diamond Model for Fraudulent Financial Statement Detection

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5838.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6865-6872

Keyword(s):

Logistic Regression ◽

Financial Reporting ◽

Stock Exchange ◽

Financial Statements ◽

Financial Statement ◽

Fraudulent Financial Reporting ◽

Return On Assets ◽

Board Of Director ◽

Fraudulent Financial Statements

Many cases of fraud that occur and are revealed. something happened in the realm of employee fraud and fraud management. One of the frauds that causes substantial losses is fraudulent financial reporting. Fraudulent financial statement becomes one of the fraud schemes that growth simultaneously within the current years. Many of this fraud scheme cause large sum amount of loss to investor, creditors and other financial statement user. The purpose of this research is to gain empirical evidence about financial statements fraud detection using fraud diamond elements. This research is conducted on listed banking companies in Indonesia Stock Exchange year 2014-2018. There is a total of 190 samples companies used in this research which further analyzed by using Logistic Regression Analysis. Statistical test is conducted to test the hypothesis. The test included: determination of coefficient, logistic regression and partial hypothesis testing. Fraudulent financial statement is proxies by Beneish M-score. The research concluded that Pressure proxies by Changes in Total Assets affects significantly to fraudulent financial statements detection. Meanwhile, Pressure proxies by Return on Assets, Opportunity proxies by Ratio of Independent Board of Commissioners, Rationalization proxies by Changes of External Auditor and Capability proxies by Changes in Board of Director do not affect significantly towards fraudulent financial statements detection. Determination of coefficient test result indicates that 20% of fraudulent financial reporting was able to be explained by pressure, opportunity, rationalization and capabilities.

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A Task-Centered, Multiple Method Approach To Teaching Fraud Risk Assessment

Journal of College Teaching & Learning (TLC) ◽

10.19030/tlc.v3i3.1736 ◽

2011 ◽

Vol 3 (3) ◽

Author(s):

Bonnie W. Morris ◽

Ann B. Pushkin ◽

William E. Spangler

Keyword(s):

Risk Assessment ◽

Financial Reporting ◽

Financial Statement ◽

Occurrence Rate ◽

Fraudulent Financial Reporting ◽

Fraud Risk ◽

Financial Statement Audit ◽

Multiple Method ◽

Approach To Teaching ◽

Research In Education

This manuscript provides an approach to teaching fraud risk assessment that is based on an analysis of the task and relevant research in education, cognitive psychology, and artificial intelligence. Fraud risk assessment (FRA) in financial reporting is an important and difficult task that must be performed in every financial statement audit. When auditors fail to detect fraudulent financial reporting (FFR), they are likely to become targets of shareholder and creditor litigation. Although FFR has a low occurrence rate considering the large number of financial statement audits conducted, it has a devastating impact on the investors, creditors and the profession.

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Fraud Risk Factor Of The Fraud Triangle Assessing The Likelihood Of Fraudulent Financial Reporting

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v7i2.2262 ◽

2011 ◽

Vol 7 (2) ◽

Cited By ~ 11

Author(s):

Yung-I Lou ◽

Ming-Long Wang

Keyword(s):

Risk Factors ◽

Financial Reporting ◽

Forecast Error ◽

Fraud Triangle ◽

Font Size ◽

Fraudulent Financial Reporting ◽

Fraud Risk ◽

Related Party Transaction ◽

Financial Pressure ◽

Simple Logistic

This research examines risk factors of the fraud triangle, core of all fraud auditing standards, for assessing likelihood of fraudulent financial reporting. Significant variables, including analyst’s forecast error, debt ratio, directors’ and supervisors’ stock pledged ratio, percentage of sales related party transaction, number of historical restatements, and number of auditor switch, belong to pressure/incentive, opportunity and attitude/rationalization. Results indicate fraudulent reporting positively correlated to one of the following conditions: more financial pressure of a firm or supervisor of a firm, higher percentage of complex transactions of a firm, more questionable integrity of a firm’s managers, or more deterioration in relation between a firm and its auditor. A simple logistic model based on examples of fraud risk factors of ISA 240 and SAS 99 gauges the likelihood of fraudulent financial reporting and can benefit practitioners.

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The Differences In Perceived Level Of Fraud-Detecting Effectiveness Of SAS No. 99 Red Flags Between External And Internal Auditors

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v5i6.2551 ◽

2011 ◽

Vol 5 (6) ◽

Author(s):

Glen D. Moyes

Keyword(s):

Financial Reporting ◽

Financial Statement ◽

Red Flags ◽

Perceived Effectiveness ◽

Font Size ◽

Fraudulent Financial Reporting ◽

Internal Auditors ◽

External Auditors ◽

Effectiveness Rating ◽

Red Flag

The purpose of this study is to examine the differences and the causes for the differences between external and internal auditors regarding the perceived levels of fraud detection of the 42 red flags found in Statement of Auditing Standard (SAS) No. 99.  SAS No. 99 requires the 42 red flags to be used in financial statement audits in order to detect fraudulent financial reporting activity.  No differences were found between external and internal auditors with respect to overall perceptions.  However, 17 of the 42 red flags had significant differences regarding the effectiveness of red flags in the detection of fraud.  For the external auditors, the extent of use and exposure to red flags were significant predictors regarding perceived effectiveness.  For internal auditors, perceived fraud-detecting effectiveness was a function of one’s internal and total audit experience.  Surprisingly, gender differences occurred with both external and internal auditors with females rating the red flag effectiveness consistently higher than male auditors.  With the exception of two red flags, external auditors displayed a higher degree of consensus regarding the effectiveness rating of each red flag than internal auditors.  When asked to identify the more effective red flags based on the SAS No. 99 categories, both groups of auditors perceived the attitude/rationalization red flag category as the most effective red flags.

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

VNU Journal of Science Economics and Business ◽

10.25073/2588-1108/vnueab.4129 ◽

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.

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A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.234 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

T Heseltine ◽

SW Murray ◽

RL Jones ◽

M Fisher ◽

B Ruzsics

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Coronary Artery ◽

Regression Model ◽

Coronary Artery Calcium ◽

Logistic Regression Model ◽

Prediction Models ◽

Multinomial Logistic Regression ◽

Cvd Risk ◽

Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score >400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score >400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

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Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

Scientific Reports ◽

10.1038/s41598-021-86853-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Anping Guo ◽

Jin Lu ◽

Haizhu Tan ◽

Zejian Kuang ◽

Ying Luo ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Liver Disease ◽

Length Of Stay ◽

Regression Model ◽

Logistic Regression Model ◽

Hospital Length ◽

Clinical Treatment ◽

Hospital Length Of Stay ◽

Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

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Pengaruh Kecurangan Laporan Keuangan terhadap Nilai Perusahaan: Ditinjau dari Perspektif Fraud Pentagon (Kasus di Indonesia)

Jurnal Ilmiah Universitas Batanghari Jambi ◽

10.33087/jiubj.v20i1.828 ◽

2020 ◽

Vol 20 (1) ◽

pp. 121

Author(s):

Desi Elviani ◽

Syahril Ali ◽

Rahmat Kurniawan

Keyword(s):

Financial Reporting ◽

Firm Value ◽

Stock Exchange ◽

Sample Selection ◽

Financial Statement ◽

Fraudulent Financial Reporting ◽

Financial Statement Fraud ◽

Company Value ◽

Negative Effect ◽

Five Elements

This study aims to examine how the influence of fraudulent financial reporting on firm value is viewed from the perspective of a pentagon fraud with a sample of 71 companies from the infrastructure, utilities and transportation sectors in the Indonesia Stock Exchange in 2014-2018. The sample selection used was purposive sampling method. Company value is measured by price book value, financial statement fraud is measured by fraud-score models. There are two variables that have a positive and significant influence, namely the opportunity and arrogance variables, the two variables present two of the five elements of pentagon fraud, where as the three variables, pressure, rasionalization, competence, do not affect the fraudulent financial reporting. The results of this study have proven that fraudulent financial reporting has a negative effect on firm value.

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