| Course | ACC 427 Investigating with Computers |
|---|---|
| Module | Module 7 |
| Paper type | undergraduate investigation report built on data analytics and corroboration |
| Length | About 1,000 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | BS Accounting |
| Updated | October 2026 |
Free sample paper for ACC 427 Module 7
Report on Voided Cash Tickets at the Landfill Scale House
[Student Name]
Southern New Hampshire University
ACC 427: Investigating with Computers
Project Two
[Instructor Name]
[Date]
The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.
Report on Voided Cash Tickets at the Landfill Scale House
Summary
Between the start of the review period and last month, 1,148 cash tickets at the landfill scale house were voided under one operator's user ID after customers had paid, removing $212,400 in fees from the records while the cash drawer still balanced. Analysis of all scale data, camera footage, system login records and confirmations from customers supports the conclusion that most of these voids removed completed, paid transactions. The estimated loss is $183,100 to $212,400. The evidence establishes the use of the operator's credentials and the pattern of voids; it does not by itself establish who received the money, which is a question for the interview and for counsel. Recommended controls would prevent the same method from recurring.
Data and Validation
The analysis used 22 months of scale tickets, voids, user records and shift schedules supplied directly by the scale software vendor with signed control totals. Record counts matched the vendor's report, the ticket sequence had no gaps, every operating day was present and cash recorded net of voids reconciled to bank deposits within $2,140. Hash values were recorded on receipt, and all analysis was performed on working copies. These steps establish that the data are a complete record of what the scale system captured (Nigrini, 2020).
Analytic Results
Exhibit 1. Summary of Tests
| Test | Operator 3 | Other operators |
|---|---|---|
| Share of cash voids compared with share of cash shifts | 72% of voids on 31% of shifts | 28% of voids on 69% of shifts |
| Median time from sale to void | 14 minutes | 1 minute |
| Voids after 3 p.m., when the supervisor leaves | 81% | Spread evenly |
| Customer dispute code used without a dispute record | 1,079 of 1,091 | Rare |
| Voided fees on cash tickets | $212,400 | Not material |
Each result alone has possible innocent explanations. Together they describe a specific pattern: cash tickets only, removed well after payment, clustered in the unsupervised late shift, explained by a code that left no record.
Corroboration
Three independent sources were examined.
Camera footage. The scale-house cameras keep 30 days of footage. For the 52 voided cash tickets under Operator 3's ID in that period, footage shows the customer's truck on the scale and unloading at the tipping face in 50 cases. In the other two, the truck turned around before unloading, consistent with a legitimate void.
Login records. The scale system's login log shows Operator 3's ID was used only during his scheduled shifts and never from another workstation, which makes use of his credentials by someone else on his days off unlikely, though a coworker on his shift could have used an unlocked terminal.
Customer confirmations. Through counsel, ten repeat cash customers whose tickets were voided were asked whether they paid on the dates in question. Nine confirmed paying cash and receiving a printed ticket; one did not recall.
Quantification
Exhibit 2. Estimated Loss
| Basis | Voids | Amount |
|---|---|---|
| All voided cash tickets under Operator 3's ID | 1,148 | $212,400 |
| Excluding voids made in the first two minutes after a sale | 987 | $183,100 |
The low estimate removes voids that could plausibly be corrections, based on the other operators' timing. The video sample suggests that at least 96 percent of the remaining voids removed completed transactions. The estimate does not include any cash taken by other means, such as trucks admitted without tickets; a comparison of scale weight events with tickets found no such crossings.
Limitations
Camera footage covers only the most recent 30 days. The analysis attributes voids to a user ID, not to a person. The customer sample is small. The interview with the operator, planned with counsel, will give him the opportunity to explain, and the findings will be revisited if he offers facts that can be checked.
How the Pieces Fit
No single piece of evidence in this report would be enough alone. The data analysis identifies a pattern under one user ID but cannot show that trucks unloaded or that customers paid. The video shows trucks unloading but covers only one month. The customer confirmations show payment but cover only ten customers. The login records show the ID was used only on the operator's shifts but cannot rule out a coworker using his open session. Taken as a set, the four sources agree and cover one another's blind spots: the video and customers confirm that the voids removed paid transactions, and the logins tie the voids to the operator's working hours. Golden et al. (2006) describe this convergence of independent sources as the core of a credible forensic finding, because it makes innocent explanations for each piece progressively harder to sustain.
Next Steps
Three steps remain before any decision about the operator. Counsel should review the report and decide whether to interview him, with the data, video stills and customer confirmations ready to show. The company should file notice with the carrier that writes its crime and employee theft policy, since late notice can defeat a claim. And the workstation at the scale counter should be imaged by a certified examiner, because it may hold evidence the vendor's records do not, such as a private record of the skimmed amounts.
Recommendations
The company should require supervisor approval in the software for any void of a cash ticket after five minutes, print a void slip that the customer signs, give each operator a unique login with automatic timeout, schedule two people at the scale house until closing or close the cash lane at 3 p.m., run a weekly report of voids by user and time with review by the controller, and extend camera retention to 90 days. Survey data from fraud examiners consistently link proactive data monitoring with smaller losses and faster detection (Association of Certified Fraud Examiners, 2024), which supports making the weekly report permanent.
Conclusion
Data analysis and three independent forms of corroboration indicate that most of the 1,148 voided cash tickets under one operator's ID removed paid transactions, with an estimated loss of $183,100 to $212,400. The findings support an interview and counsel's review of recovery options, and the recommended controls would close the method used.
References
Association of Certified Fraud Examiners. (2024). Occupational fraud 2024: A report to the nations. Author.
Golden, T. W., Skalak, S. L., & Clayton, M. M. (2006). A guide to forensic accounting investigation. Wiley.
Nigrini, M. J. (2020). Forensic analytics: Methods and techniques for forensic accounting investigations (2nd ed.). Wiley.
What the ACC 427 Module 7 instructions ask for
Project Two in ACC 427 usually asks you to report the results of a computer-based investigation to a client or manager. Expect to describe the data sources and how completeness was established, the analytic tests and their results, any corroborating evidence from outside the data, the quantification of loss, conclusions and recommendations. Most versions want a professional format with an executive summary, exhibits and limitations. Present findings so that a nontechnical reader understands them, but keep enough detail that another analyst could repeat the work. Distinguish what the data show from what corroboration establishes, and state the loss as a range when the evidence supports more than one reasonable estimate.
How this ACC 427 Module 7 project two example is built
The report opens with a summary of findings for the landfill's owner. It describes the vendor-supplied scale data, validated against control totals, the ticket sequence and bank deposits. Five tests show that one operator's user ID made 72 percent of cash voids on 31 percent of shifts, mostly late in the day and long after the sale, with unsupported dispute codes. Corroboration follows: camera footage for the last 30 days shows trucks unloading on 50 of 52 voided tickets; the ID was never used when the operator was off shift; and 9 of 10 repeat cash customers contacted through counsel confirm they paid. The loss range is $183,100 to $212,400. Recommendations address scale-house controls.
Where the ACC 427 Module 7 rubric puts the points
Rubrics for ACC 427 Project Two typically score the executive summary, the description of data and validation, the presentation of tests and results, corroboration, quantification, conclusions and limitations, recommendations and overall clarity. Top papers make the analytic chain easy to follow, present corroboration from independent sources, quantify loss with stated assumptions and a range, and avoid conclusions about guilt that the evidence cannot carry. Graders reward exhibits that a nontechnical reader can understand and recommendations tied to the weaknesses found, each with an owner. Common deductions include reports that read like analysis logs, a single loss figure with no basis, missing limitations and accusatory language.
ACC 427 Module 7 help: the mistakes that cost points
Data-driven reports most often fall short by presenting the analysis in the order it was done rather than the order a reader needs, by burying the conclusion and by treating a strong data pattern as proof without corroboration. Another frequent gap is the loss estimate: give a range and explain the assumption that separates its ends. If your project concerns payroll, procurement or expense fraud, the same report structure applies and can be built around your tests. Write the executive summary last and keep it to one page; if the owner reads nothing else, it should still tell him what happened, how sure the investigators are and what to do next.
Get ACC 427 Module 7 written to your instructions
Send the ACC 427 Project Two guidelines, your analysis and the rubric. The report will present sources and validation, the tests and results, corroborating evidence, a loss range, conclusions within the evidence and control recommendations, in a format a client can use. Your first one costs nothing; allow about two days. 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.
More ACC 427 papers and related BS Accounting samples
- ACC 427 Module 1 Discussion: Where the Digital Evidence Lives
- ACC 427 Module 2 Data Acquisition Assignment: Getting a Complete Copy of the Scale Data
- ACC 427 Module 3 Data Analysis Assignment: Voids, Gaps and the 3 p.m. Pattern
- ACC 427 Module 4 Project One: Fuel Cards Matched to Truck GPS
- ACC 427 Module 5 Email and Document Review Assignment: Searching a Supervisor's Mailbox Defensibly
- ACC 427 Module 6 Discussion: How Far May an Employer Look?
- ACC 427 Module 8 Digital Forensics Assignment: Imaging the Scale-House Computer and Building a Timeline
- ACC 201 Module 4 Inventory Costing Short Paper
- ACC 315 Module 1 Discussion: How One Delivery Ticket Becomes Revenue
- ACC 405 Module 3 Equity Method Assignment: A 30 Percent Stake in a Maple Drinks Startup
- ACC 202 Module 4 Project Milestone Two
ACC 427 Module 7 questions, answered
Where can I find a free ACC 427 Module 7 Project Two sample?
This page holds a complete ACC 427 Module 7 Project Two report on voided cash tickets at a landfill scale house, with analytics and corroboration.
What should a data-driven fraud report include?
An executive summary, data sources and validation, tests and results, corroborating evidence, quantification of loss, conclusions within the evidence, limitations and recommendations.
Why is corroboration needed for data findings?
Data show patterns tied to user IDs or accounts, not necessarily to people or events. Video, documents, logs and witness statements establish what actually happened.
How should a loss be estimated when evidence is incomplete?
As a range with stated assumptions, such as including all suspicious voids at the high end and excluding those that could be corrections at the low end.
Should a fraud report name the person responsible?
It should report facts about whose credentials, access or actions are involved, but leave conclusions about guilt to decision makers and courts.