Developing Student Abilities to Recognize Risk Factors: A Series of Scenarios

2002 ◽  
Vol 17 (1) ◽  
pp. 57-67
Author(s):  
Carolyn A. Strand ◽  
Sandra T. Welch ◽  
Sarah A. Holmes ◽  
Steven L. Judd
Keyword(s):  
Risk Factors ◽  
Financial Statement ◽  
Red Flags ◽  
Professional Skill ◽  
Fraud Risk ◽  
Financial Statement Audit ◽  
Auditing Standards

Misappropriation of assets is an expensive and growing problem. However, detecting this type of fraud is very difficult. Green and Calderon (1996) claim that externally observable risk factors can help signal the likelihood of fraud. Awareness and timely recognition of these “red flags” might improve an individual's ability to assess the potential vulnerability of an organization to fraud. Contained herein is a case consisting of five scenarios that deal with the risk factors identified in Statement on Auditing Standards (SAS) No. 82, Consideration of Fraud in a Financial Statement Audit (AICPA 1997). Throughout the case, you will be confronted with a number of clues that may suggest employee wrongdoing. This case is designed to help you develop your knowledge and professional skill regarding the recognition of fraud risk factors. Although textbooks, and other sources, frequently list various risk factors, these same clues may not be as obvious to you when they actually occur in an organization.

Fraud Detection and Investigation: Microcomputer Consulting Services

1999 ◽  
Vol 14 (1) ◽  
pp. 99-115 ◽  
Author(s):  
Bonita K. Peterson ◽  
Thomas H. Gibson
Keyword(s):  
Fraud Detection ◽  
Internal Controls ◽  
Financial Statement ◽  
Sufficient Evidence ◽  
Red Flags ◽  
Fine Line ◽  
Consulting Services ◽  
Financial Statement Audit ◽  
Audit Team ◽  
University Setting

This nonfictional case of inventory fraud in a university setting exposes students to fraud detection and investigation. These skills are becoming increasingly important for auditors, as evidenced by the alarming rate of fraud. The accounting profession has acknowledged the seriousness of this issue with the issuance of SAS No. 82, Consideration of Fraud in a Financial Statement Audit, developed in part to improve detection of frauds by auditors. The case raises many of the fraud-related issues faced by accountants: recognizing red flags indicative of fraud; the importance of a good system of internal controls; the profile of the typical fraud perpetrator; the fine line auditors walk when investigating a fraud; the need to develop an audit team with the appropriate level of expertise which may require members from a variety of disciplines (e.g., investigative, legal and forensic areas); and the difficulty of obtaining sufficient evidence to prosecute and convict perpetrators.

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.

Dickinson Technologies, Inc.: Assessing Control Environment and Fraud Risk

2003 ◽  
Vol 18 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Christopher P. Agoglia ◽  
Kevin F. Brown ◽  
Dennis M. Hanno
Keyword(s):  
Risk Assessment ◽  
Internal Control ◽  
Financial Statement ◽  
Risk Assessments ◽  
Background Information ◽  
Control Environment ◽  
Abstract Concepts ◽  
Fraud Risk ◽  
Financial Statement Audit ◽  
Fraud Risk Assessments

This instructional case provides you an opportunity to perform realistic audit tasks using evidence obtained from an actual company. Through the use of engaging materials, the case helps you to develop an understanding of the control environment concepts presented in SAS No. 78 (AICPA 1995), Consideration of Internal Control in a Financial Statement Audit, and fraud risk assessment presented in SAS No. 99 (AICPA 2002), Consideration of Fraud in a Financial Statement Audit. This case involves making a series of fraud risk assessments based on company background information and a detailed and realistic control environment questionnaire, which provide you a context that makes the often abstract concepts relating to control environment and fraud risk assessment more concrete.

Automated Clustering for Data Analytics

2019 ◽  
Vol 16 (2) ◽  
pp. 43-58 ◽  
Author(s):  
Paul E. Byrnes
Keyword(s):  
Data Analytics ◽  
Financial Statement ◽  
Decision Point ◽  
Technical Aspects ◽  
Fraud Risk ◽  
Use Of Data ◽  
Decision Points ◽  
Financial Statement Audit ◽  
Mining Methods ◽  
Construction Model

ABSTRACT Today, auditors must consider the risks of material misstatement due to fraud during the financial statement audit (Messier, Glover, and Prawitt 2016). Current audit guidance recommends the use of data mining methods such as clustering to improve the likelihood of discovering irregularities during fraud risk assessment (ASB 2012). Unfortunately, significant challenges exist relative to using clustering in practice, including data preprocessing, model construction, model selection, and outlier detection. The traditional auditor is not trained to effectively address these complexities. One solution entails automation of clustering, thus eliminating the difficult, manual decision points within the clustering process. This would allow practitioners to focus on problem investigation and resolution, rather than being burdened with the technical aspects of clustering. In this paper, automated clustering is explored. In the process, each manual decision point is addressed, and a suitable automated solution is developed. Upon conclusion, a clustering application is formulated and demonstrated.

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