| Course | ACC 693 Investigating with Computers |
|---|---|
| Module | Module 2 |
| Paper type | graduate assignment applying data analytics tests to accounts payable |
| Length | About 1,010 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MS Accounting |
| Updated | October 2026 |
Free sample paper for ACC 693 Module 2
Accounts Payable Data Analytics, 2023-2025
[Student Name]
Southern New Hampshire University
ACC 693: Investigating with Computers
Module Two Assignment
[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.
Accounts Payable Data Analytics, 2023-2025
Introduction
In June 2025, the internal auditor of a Colorado commercial roofing contractor received an anonymous note suggesting that "someone in project management has their own subcontractor." The note named no one. Rather than search at random, the auditor extracted three years of accounts payable data and ran a set of tests designed to detect shell vendor and billing schemes. This paper describes the data, the preparation, seven tests and their results, and the leads that emerged.
Data and Preparation
The extract covered January 1, 2023, through May 31, 2025: 51,412 invoices totaling $211.8 million, paid to 2,380 vendors. Three related files were obtained: the vendor master with its change log, the employee master from the human resources system and the approval history for each invoice. The payables total was reconciled to the general ledger for each year, with differences under $1,000 traced to timing. Addresses were standardized, phone numbers reduced to digits and bank account numbers compared in full. Debreceny and Gray (2010) stress that data preparation decides whether analytics are credible, since tests run on unreconciled data can miss the very items they seek.
The Seven Tests
Payables tests and results
| Test | Scheme targeted | Rule | Hits |
|---|---|---|---|
| Vendor to employee match | Shell vendor owned by an employee or relative | Same phone, address or bank account | 12 phone, 2 address, 0 bank |
| New vendor growth | Shell vendor | First paid after 2022 and over $250,000 within two years | 9 |
| Threshold clustering | Avoiding higher approval | Over 30 percent of a vendor's invoices between $22,000 and $24,999 | 3 |
| Consecutive invoice numbers | Vendor with one customer | Ten or more invoices numbered in an unbroken sequence | 4 |
| Single approver | Collusion or control by one person | All of a vendor's invoices approved by one employee, over 20 invoices | 6 |
| Taxpayer number check | Fictitious vendor | Name and number do not match IRS records | 2 |
| Duplicate payments | Double billing | Same vendor, amount and date within 30 days | 41 pairs |
Resolving the Hits
Most hits were innocent, and resolving them took the bulk of the work, about four days against less than one day for running the tests themselves. Eleven of the twelve phone matches were vendors owned by employees' relatives that had been disclosed and approved under the company's conflict-of-interest policy. Both address matches were employees who had previously worked for the vendor. Eight of the nine fast-growing new vendors were large subcontractors hired for a hospital reroofing project. Three of the four vendors with consecutive invoice numbers were small local suppliers whose own records explained the pattern. The forty-one duplicate pairs, totaling $38,000, were mostly double entries of the same invoice that the vendor had already credited. Bierstaker et al. (2006) note that analytics are most effective when paired with professional skepticism, and here skepticism also meant accepting innocent explanations once they were documented.
Convergence on One Vendor
One vendor appeared in five of the seven tests: a sheet-metal flashing subcontractor first paid in March 2023. Its phone number matched the emergency contact on a senior project manager's personnel record. It received seventy-four invoices totaling about $1.1 million in twenty-six months. Thirty-one of those invoices, 42 percent, fell between $22,000 and $24,999, just under the project manager's $25,000 approval limit. Its invoice numbers ran from 1001 to 1074 without a gap, implying the company was its only customer. Every invoice was approved by the same project manager. The taxpayer check returned a match, because the LLC had a real number, which is a reminder that a passing test proves little.
The subcontractor's billing pattern added detail beyond the formal tests. Its invoices described work in general terms, such as "counterflashing and coping, per change order," without quantities or linear footage, while the company's legitimate sheet-metal subcontractors itemized materials and labor. Its invoices also arrived on the last two business days of each month, matching the project manager's own schedule for submitting job cost reports. Neither feature proves anything alone, but both are the kind of detail an examiner notices only after analytics have pointed to the right vendor, and both will be useful when the project manager is eventually asked to explain the billing.
Tests flagging the flashing subcontractor
| Test | Result |
|---|---|
| Vendor to employee match | Phone matches project manager's emergency contact |
| New vendor growth | $1.1 million within 26 months |
| Threshold clustering | 31 of 74 invoices between $22,000 and $24,999 |
| Consecutive invoice numbers | 1001 to 1074, no gaps |
| Single approver | All 74 approved by the same project manager |
Nigrini (2012) describes this kind of convergence, where independent tests identify the same entity, as far more meaningful than any single hit.
Limits of the Analysis
The results do not show that the subcontractor performed no work, that the project manager owns it or that any invoice was false. A legitimate small subcontractor could have a sequential invoice series and could have been introduced by an employee's relative. The tests establish where to look next: project records showing whether flashing work was performed, the vendor master change log, invoice documents and bank records.
Recommendations
The internal auditor should preserve the project manager's email and laptop before any contact, as Milestone One will plan; pull job files for the fourteen projects billed; obtain the LLC's state registration; and ask the company's bank, through counsel, for the payee account details. The other leads should be closed with memoranda documenting their resolution. Two improvements to the analytics themselves are also worth making. The vendor-to-employee match should be rerun monthly as a standing control, using the emergency contact and beneficiary fields that produced the decisive hit, since those fields are often overlooked. And the threshold test should be extended to change orders, which follow a separate approval path and may hold more of the scheme than the invoices do.
Conclusion
Seven tests across 51,412 invoices produced dozens of hits, nearly all innocent and resolved with documentation. One vendor failed five tests, with seventy-four invoices totaling about $1.1 million, all approved by one project manager. That convergence justifies a full investigation, and the steps above set its direction without yet alerting the person whose approvals the tests point to.
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
Debreceny, R. S., & Gray, G. L. (2010). Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems, 11(3), 157-181. https://doi.org/10.1016/j.accinf.2010.08.001
Nigrini, M. J. (2012). Benford's law: Applications for forensic accounting, auditing, and fraud detection. Wiley.
What the ACC 693 Module 2 instructions ask for
The Module Two assignment in ACC 693 asks you to apply data analytics to an accounting data set, often accounts payable, payroll or journal entries, to identify possible fraud. You typically describe the data and how it was prepared, choose and justify a set of tests, report the results and interpret them. Graders expect each test to be tied to a fraud scheme it is designed to detect, and they expect you to report how many items each test flagged and what you did with them. Common tests include matching vendor and employee data, duplicates, threshold analysis, number patterns and unusual vendor activity. Reporting false positives honestly is part of good analysis.
How this ACC 693 Module 2 data analytics assignment example is built
The paper starts with data preparation: extracting payables, vendor master and employee records, checking that payments reconcile to the general ledger and standardizing addresses and phone numbers. Seven tests follow. Vendor-to-employee matching found twelve phone matches, eleven of them approved referrals. Fast-growing new vendors, invoices just under the $25,000 approval limit, consecutive invoice numbers and single-approver vendors each flagged a handful. A taxpayer number check found two mismatches, and duplicate testing found $38,000 of innocent double payments. One flashing subcontractor appeared in five of the seven tests, with seventy-four invoices totaling about $1.1 million, all approved by the same project manager, and a short list of next steps closes the paper.
Where the ACC 693 Module 2 rubric puts the points
The rubric for this assignment usually scores data preparation and validation, choice and justification of tests, correct execution, reporting of results, interpretation including false positives, and recommendations. Top papers reconcile the data to the ledger before testing, tie each test to a specific scheme, report populations and hit counts, explain how hits were investigated and identify items that fail several tests. They also recognize the limits of each test, noting for example that a taxpayer number match only shows that an entity exists, not that it did any work. Papers lose credit for running tests without explaining why, for reporting results without numbers, for treating every hit as fraud and for skipping the step of confirming the data are complete.
ACC 693 Module 2 help: the mistakes that cost points
A frequent weakness is skipping data validation; if the extract is incomplete, every result is suspect, so reconcile totals to the general ledger first and say so. Another is a list of tests with no rationale; explain what scheme each is meant to catch. Report the population, the rule and the hit count for every test, then explain how you resolved the hits, including how long it took, since that shows the reader the work behind the numbers. The most persuasive result is convergence, one vendor or employee flagged by several independent tests, so build a summary showing which items appear repeatedly. Finally, avoid presenting analytics as proof; the output is a set of leads for the next stage.
Get ACC 693 Module 2 written to your instructions
Send the ACC 693 Module 2 assignment and a description of your data. The paper will set out data preparation, the tests and their logic, results with hit counts, innocent explanations and the leads that remain. Usually two days; your first paper 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 2 questions, answered
Where can I find a free ACC 693 Module 2 Data Analytics sample?
This page offers a complete ACC 693 Module 2 assignment running seven payables tests at a roofing contractor and isolating a sham subcontractor.
What data analytics tests detect shell vendor fraud?
Matching vendor addresses, phone numbers and bank accounts to employee records, reviewing new vendors with rapid growth, looking for consecutive invoice numbers and checking taxpayer identification numbers.
Why validate data before running fraud tests?
Because an incomplete or altered extract can hide the transactions you are looking for, so totals should be reconciled to the general ledger before any test is run.
What is threshold testing in accounts payable?
Looking for invoices that cluster just below an approval limit, which can indicate that someone is splitting or sizing invoices to avoid a higher level of review.
What should be done with false positives from analytics?
Each should be reviewed and resolved with documentation, which both clears innocent items and shows the investigation was thorough.