RATIONALISATION RED FLAGS AND LIKELIHOOD OF FRAUD DETECTION IN NIGERIA

The broad objective of this study is to ascertain the impact of rationalisation red flags as prescribed by SAS.99 in relation to the fraud triangle on the likelihood of fraud detection in Nigeria. The specific objectives of this study are to determine the effects of rationalisation red flags proxies: quality of earnings; and effective cash tax rate on the likelihood of fraud detection in Nigeria. This study used secondary data sourced from audited annual reports of quoted companies in the Nigeria Stock Exchange and a sample size of sixty-five (65) companies were used for a six-year period of 2009-2014. The variables were derived by making necessary computations using information reflected on the face of financial statements to derive our figures not explicitly stated on the face of the financial statements. The probit regression estimation analyses on the pooled data shows that Rationalization red flags such as quality of earnings and effective cash tax rate on the average cannot aid the likelihood of fraud detection in Nigeria. It is however recommended that forensic accountants should as a matter of necessity pay close attention to our findings in this study and make use of SAS.99 qualitative and quantitative proxies red flags when carrying fraud examination.

The Effectiveness Of The Auditing Standards To Detect Fraudulent Financial Reporting Activities In Financial Statement Audits In Malaysia

In identifying relevant red flags to be used to detect possible fraud in financial statements, this study adopts the International Auditing Standard AI240 and adapts the US-based Statement of Auditing Standard No 99 (SAS 99). Both SAS 99 and AI240 classify the red flags into three categories: Opportunity, Pressure, and Rationalization. Opportunity Red Flags are found in situations that are ideal for people to commit fraud more easily due to ineffective internal controls, inadequate supervision or managers overriding internal controls. Pressure Red Flags are circumstances in which people have a financial incentive to commit fraud such as falsely overstating sales or profits to receive their bonuses or exerting pressure on managers to reduce actual expenses to be under budgeted costs. Rationalization Red Flags are situations where people have certain traits and abilities to commit fraud and justify it with false reasons which they believe are true. A Red Flag Questionnaire which contains 15 demographic multiple choice questions, followed by a five-point Likert scale with questions for 14 Opportunity Red Flags, 15 Pressure Red Flags and 11 Rationalization Red Flags was developed and distributed to three groups of auditors: External, internal and governmental. The study indicates the direct or inverse relationships between each demographic factor and each red flag. These relationships were identified by using multiple regression models. Three types of relationships are possible: direct, inverse and no relationship. These three types of relationships are as follows: (1) the relationship between the level of fraud-detecting effectiveness of each Opportunity Red Flag and each demographic factor, (2) the relationship between the level of fraud-detecting effectiveness of each Pressure Red Flag and each demographic factor, and (3) the relationship between the level of fraud-detecting effectiveness of each Rationalization Red Flag and each demographic factor. These relationships indicate which specific professional demographic factors are more likely associated with more effective fraud-detecting red flags. In contrast, other relationships also indicate which specific demographic factors are more likely associated with less effective fraud-detecting red flags. In conclusion, this research project should be conducted in other countries, so the result from one country can be compared to the results from other countries. Some results may vary between developed countries and developing countries. The learning curve or the period of time necessary for auditors to learn how to use red flags and then interpret the findings may also explain differences in the results between countries. This study may enhance the auditors understanding of the different levels of fraud-detecting effectiveness of red flags as well as when the auditors may benefit from using them in financial statement audits.

Auditor Perceptions Of SAS 99: Do Two Expectation Gaps Still Exist?

<p class="MsoBlockText" style="margin: 0in 0.5in 0pt;"><span style="font-style: normal; font-size: 10pt; mso-bidi-font-style: italic;"><span style="font-family: Times New Roman;">In 1988, the Auditing Standards Board released nine standards intended to narrow the previously identified &ldquo;expectation gap&rdquo;&mdash;a gap between the expectations of financial statement users and certified public accountants (&ldquo;Expectation Gap I&rdquo;).<span style="mso-spacerun: yes;">&nbsp; </span>A 1992 study of auditor perceptions of two of those standards dealing with errors, irregularities, and illegal acts of clients, revealed that there existed a second expectation gap, one between the standard setters and practicing CPAs (&ldquo;Expectation Gap II&rdquo;). Since then, the ASB has released two successor statements intended to address the issue of auditors&rsquo; responsibility to consider fraud in a financial statement audit.<span style="mso-spacerun: yes;">&nbsp; </span>This study examines auditor perceptions of the more recent pronouncement and reveals general skepticism among respondents regarding its effectiveness in promoting Congressional and public confidence in the auditing profession; i.e., little confidence that it will serve to reduce Expectation Gap I.<span style="mso-spacerun: yes;">&nbsp; </span>It also reveals the continued existence of Expectation Gap II.<span style="mso-spacerun: yes;">&nbsp;&nbsp; </span></span></span></p>