| Course | ACC 693 Investigating with Computers |
|---|---|
| Module | Module 1 |
| Paper type | graduate discussion post on how computers changed fraud investigation |
| Length | About 390 words, 3 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MS Accounting |
| Updated | October 2026 |
Free sample paper for ACC 693 Module 1
Module One Discussion
Seventy-Four Invoices in Fifty-One Thousand
The case I will use this term involves a commercial roofing contractor in Colorado with about $85 million of annual revenue. From 2023 to 2025, a senior project manager billed about $1.1 million through a sheet-metal flashing subcontractor that existed only on paper, registered in the name of a relative. The scheme ran through seventy-four invoices.
Before computers, the external auditors' approach would have been a sample. The company's payables held about 51,400 invoices from 2,380 vendors over three years. A sample of sixty invoices, typical for that population, would have had only a small chance of including even one of the seventy-four, and an invoice from a flashing subcontractor on a roofing job would have looked normal if it had been chosen. Testing the whole population changes the odds completely. When the company's internal auditor ran every vendor against the employee file, the subcontractor's phone number matched the emergency contact listed on the project manager's personnel record. That single match took minutes.
The second change is that schemes now leave traces. The vendor master change log showed who created the subcontractor and when. The invoice files, though they looked like scans, carried metadata showing they had been made from a template on the project manager's company laptop. Emails approving change orders, phone records and bank data filled in the rest. In the survey by Bierstaker et al. (2006), practitioners judged software-driven analysis among the stronger detection methods while reporting that their own organizations seldom used it, a gap I suspect still holds for contractors this size.
The difficulty is volume. Garfinkel (2010) warned that the growth of storage would outpace investigators' ability to process it, and Quick and Choo (2014) describe backlogs and the risk of missing evidence among terabytes of irrelevant data. The phone-number test also produced eleven other matches, all innocent: employees who had referred relatives as vendors with approval. Someone had to look at each, document why it was innocent and move on, and that review took longer than finding the scheme. Computers made finding the scheme easy; they did not make deciding what it meant automatic.
For classmates: if you had the vendor file, the employee file and three years of payments, which single test would you run first in your own case, and what innocent explanations would you expect it to turn up?
References
Bierstaker, J. L., Brody, R. G., & Pacini, C. (2006). Accountants' perceptions regarding fraud detection and prevention methods. Managerial Auditing Journal, 21(5), 520-535. https://doi.org/10.1108/02686900610667283
Garfinkel, S. L. (2010). Digital forensics research: The next 10 years. Digital Investigation, 7, S64-S73. https://doi.org/10.1016/j.diin.2010.05.009
Quick, D., & Choo, K.-K. R. (2014). Impacts of increasing volume of digital forensic data: A survey and future research challenges. Digital Investigation, 11(4), 273-294. https://doi.org/10.1016/j.diin.2014.09.002
What the ACC 693 Module 1 instructions ask for
The first ACC 693 discussion usually asks how computers and data have changed fraud examinations: what kinds of evidence now exist, how analytics change detection, what new risks and difficulties arise and what skills examiners need. A strong post avoids a general list of technologies and instead shows, with one case, how a computer-based approach found or proved something a manual approach would not. It should also recognize limits, such as data volume, privacy and the risk of trusting tool output without understanding it. Research on digital forensics and on accountants' use of technology gives the post substance. Replies work best when they ask what evidence a classmate's example would leave behind.
How this ACC 693 Module 1 discussion example is built
The post follows the roofing contractor's payables, about 51,400 invoices from 2,380 vendors over three years. A traditional audit sample of sixty invoices had a small chance of including any of the sham subcontractor's seventy-four. Testing every payment against the employee file, by contrast, matched the subcontractor's phone number to the emergency contact on the project manager's personnel record. The post then lists the digital traces that turned that lead into proof: the vendor master change log, invoice file metadata, emails and phone records. It cites research on growing data volume, notes that the examiner still had to decide which hits mattered, and asks classmates what they would test first.
Where the ACC 693 Module 1 rubric puts the points
Grading for the opening discussion typically considers an accurate account of how technology has changed investigations, a concrete example, recognition of limits and risks, use of research and replies. Strong posts explain the difference between sampling and testing a whole population, identify specific digital evidence sources and acknowledge that tools produce leads rather than conclusions. They also mention practical concerns such as data volume, preservation and privacy. Posts that treat software as a substitute for judgment, or that list technologies without connecting them to a case, earn less. Instructors also look for a sense of proportion: data analytics is powerful, but it is one step in an investigation that still ends with documents, interviews and testimony. Replies gain credit when they add an evidence source or a limit the classmate missed.
ACC 693 Module 1 help: the mistakes that cost points
Posts on this prompt often read like technology catalogs. Pick one scheme and follow it from detection to proof, naming the systems and files involved. Explain why the computer mattered at each step, whether it was testing every transaction, recovering a deleted file or showing who changed a record. Another common gap is ignoring the downside: more data means more noise and more time spent ruling out innocent hits, and digital evidence must be preserved properly to be usable. Close by making classmates pick between options, such as which single data test they would run first in their own case, because a forced choice draws better replies than an open invitation.
Get ACC 693 Module 1 written to your instructions
Send the ACC 693 Module 1 prompt. Expect a post that walks one concrete scheme from first lead to proof, showing where the computer changed the outcome, cite research on digital evidence and finish with a question for classmates to argue. Two days is typical, and the first is free. 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 693 Module 1 questions, answered
Where can I find a free ACC 693 Module 1 Discussion sample?
This page includes the full ACC 693 Module 1 post on how computers changed fraud investigation, using a roofing contractor's sham subcontractor.
How have computers changed fraud investigations?
Investigators can test entire populations of transactions instead of samples, and schemes leave digital traces such as logs, metadata and messages that can prove who did what and when.
What is whole-population testing?
Applying analytical tests to every transaction in a data set, such as all vendor payments, rather than selecting a sample, which makes rare fraudulent items far more likely to be found.
What digital evidence is common in fraud cases?
Accounting system records and change logs, email, documents and their metadata, phone and messaging records, bank data and files stored in cloud services.
Does data analytics replace the fraud examiner's judgment?
No; analytics produce leads and exceptions, and the examiner must decide which ones matter, gather confirming evidence and interpret it.