| Course | ACC 640 Auditing |
|---|---|
| Module | Module 7 |
| Paper type | graduate discussion post on data analytics in auditing |
| Length | About 370 words, 3 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MS Accounting |
| Updated | October 2026 |
Free sample paper for ACC 640 Module 7
Module Seven Discussion
1.9 Million Orders and 2,300 Questions
In my course project, the audit team tested every online order for the year, 1.9 million of them, rather than a sample. The script matched each recorded sale to a payment authorization from the processor and a pickup scan from the carriers, testing both occurrence and cutoff. Before running it, the team reconciled the order file to the general ledger and obtained the processor and carrier files directly from those parties, because AS 1105 requires the auditor to evaluate the reliability of information used as evidence (Public Company Accounting Oversight Board, 2016).
The match produced 2,300 exceptions, about 0.12 percent of orders. With a sample, an exception rate is projected to the population; here there is nothing to project, but every exception still needs an explanation. Grouping them was the key step. About 2,050 were split shipments, where one order shipped in two boxes on different days and the first scan was matched; these were legitimate. About 190 were orders paid by gift card, which have no card authorization; we tested a sample of those to gift card redemption records. The remaining 60 were orders picked up on January 2 or 3 but recorded in December, a $41,000 cutoff error, below our posting threshold but a sign the system recognized revenue on label printing rather than pickup for some orders.
Appelbaum et al. (2017) argue that big data analytics shift the audit toward exception investigation and require new guidance on what the results mean as evidence. The PCAOB's recent amendments to AS 1105 and AS 2301 on technology-assisted analysis respond to that, emphasizing that auditors must investigate items identified by the analysis and consider whether they indicate misstatements or control deficiencies. Austin et al. (2021) found that managers, auditors and regulators are still working out expectations, and that analytics raised new questions about data access and reliability. My view is that the 60 cutoff orders show the value of analytics, since a sample would likely have missed them, but the 2,300 also show the work: someone had to understand every group.
For classmates: if your analysis produced several thousand exceptions, how would you decide which groups need individual follow-up and which can be cleared with a sample?
References
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1-27. https://doi.org/10.2308/ajpt-51684
Austin, A. A., Carpenter, T. D., Christ, M. H., & Nielson, C. S. (2021). The data analytics journey: Interactions among auditors, managers, regulation, and technology. Contemporary Accounting Research, 38(3), 1888-1924. https://doi.org/10.1111/1911-3846.12680
Public Company Accounting Oversight Board. (2016). Audit evidence (AS 1105). Author.
What the ACC 640 Module 7 instructions ask for
The Module Seven discussion in ACC 640 usually asks about data analytics and technology in auditing: how analytics differ from traditional sampling, what evidence they provide, what risks they bring and how standards address them. Plan on three or four paragraphs drawing on AS 1105, the textbook and research, then reply to classmates. Strong posts use a specific audit test as an example and address the practical problems analytics create, such as the reliability of client data and the volume of exceptions. Some prompts ask whether analytics will reduce the need for auditors, which invites a position supported by evidence. Avoid presenting analytics as a cure-all; graders reward a clear account of what the test could not show.
How this ACC 640 Module 7 discussion example is built
The post uses the online revenue test: all 1.9 million orders matched to payment authorizations and carrier pickup scans, covering both occurrence and cutoff. The match produced 2,300 exceptions. The post explains that these are not sample errors to project; each must be understood. Grouping showed 2,050 were split shipments, 190 were gift card orders with no card authorization and 60 were shipped after year end but recorded in December, a $41,000 cutoff error. It notes that the data had to be tested for completeness and accuracy before use, cites Appelbaum, Kogan and Vasarhelyi and Austin and colleagues, and asks classmates how they would triage the list when time is short and the exceptions run into the thousands.
Where the ACC 640 Module 7 rubric puts the points
Scoring for the analytics discussion typically weighs understanding of how analytics provide audit evidence, a concrete example, recognition of data reliability and exception handling issues, use of standards and research, and engagement with classmates. The best posts explain why full-population tests are not automatically sufficient, how exceptions are evaluated and how the auditor establishes that client data is reliable. Posts that describe analytics enthusiastically without these limits score lower. Replies that suggest a specific grouping or follow-up procedure for a classmate's exceptions earn more participation credit than agreement. Accurate use of terms such as population, exception and reliability matters, and so does citing the evidence standard by number.
ACC 640 Module 7 help: the mistakes that cost points
Students sometimes describe full-population testing as giving absolute assurance, when it tests only what the data contains and only the attributes the test checks. Others overlook that the data itself must be tested for completeness and accuracy before it can serve as evidence. If your prompt focuses on artificial intelligence or continuous auditing instead, the same issues of data reliability, exception handling and judgment apply. Give the exception count and how you would group it; that single example shows more understanding of analytics than a general description of their benefits. Then say which group you would clear first and why.
Get ACC 640 Module 7 written to your instructions
Send the ACC 640 Module 7 prompt. The post will explain how analytics fit the audit evidence standards, work through a concrete example including exceptions and connect it to research, with a question for replies. Plan on roughly two days; your first costs nothing. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.
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ACC 640 Module 7 questions, answered
Where can I find a free ACC 640 Module 7 Discussion sample?
This page includes the full ACC 640 Module 7 post on full-population analytics over online orders and handling exceptions.
Does testing 100 percent of a population give absolute assurance?
No. It tests only the attributes examined, using data that must itself be reliable, and it cannot detect misstatements the data does not reveal.
Can exceptions from a full-population test be projected?
There is nothing to project because the whole population was tested, but each exception or group of exceptions must be investigated to determine whether it is a misstatement.
How does an auditor establish that client data is reliable?
By testing the completeness and accuracy of the data, for example reconciling it to the general ledger and testing the controls or source records that produce it.
Will data analytics replace auditors?
Research suggests analytics change the auditor's work toward investigating exceptions and exercising judgment rather than replacing it.