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1. #Disadvantages of using excel for data analysis software#
2. #Disadvantages of using excel for data analysis professional#

In conjunction with these changes, the IIA released Practice Guides for Internal Auditing and Fraud, Fraud Prevention and Detection in an Automated World (GTAG 13) and Data Analysis Technologies (GTAG 16).Īccording to GTAG 13: “Data analysis technology enables auditors and other fraud examiners to analyze transactional data to obtain insights into the operating effectiveness of internal controls and to identify indicators of fraud risk or actual fraudulent activities.”įurthermore, ISACA (formerly known as the Information Systems Audit and Control Association) - a 95,000-member trade association for IT auditors - recently published an informative white paper entitled “Data Analytics - a Practical Approach.” In it, the authors state that “few would argue that an enterprise’s data are among its most valuable assets.

#Disadvantages of using excel for data analysis professional#

This is partly due to the fact that the professional standards relating to the auditor’s responsibility for detecting and preventing fraud changed in 2009 when the Institute of Internal Auditors (IIA) updated the International Professional Practices Framework (IPPF).Īuditors must now consider fraud risks and red flags as part of planning audits. Important: Anti-fraud professionals predict that the trend toward adoption of these powerful tools will gradually gain momentum and eventually become a key part of auditors’ standard toolkit of techniques, tests and methodologies. Following is a chart showing the responses:

#Disadvantages of using excel for data analysis software#

The software most widely used for detecting and investigating fraud includes ACL, IDEA, Excel, Access and ActiveData (an Excel add in). Only 16 percent of those who use data analysis software indicated that they do have a defined strategy for using it to perform audits. The largest response came from those who said they do not use data analysis technology (approximately 30 percent) to detect or investigate fraud.Īnother 23 percent of auditors said they determine on a case-by-case basis when to employ data analysis but do not appear to have a defined strategy.

Yet, according to recent indicators, large segments of the anti-fraud community have yet to incorporate these automated tools into their fraud audit and detection practices.Ī recent survey by the Web portal,, illustrates this point: AuditNet asked internal auditors whether their organizations owned data analysis software and had a defined strategy for using it for all audits (including fraud audits). Data analytics - the practice of assessing bodies of business data to identify potential indicators of fraud - is slowly, but surely, becoming a mainstay of the fraud fighter’s professional repertoire.īooks, articles and white papers have been published about the virtues of using computers to manipulate data to ramp up the task of locating smoking guns in fraud cases.