| Course | ACC 691 Detection and Prevention of Fraudulent Financial Statements |
|---|---|
| Module | Module 3 |
| Paper type | graduate milestone analyzing fraud red flags and the Beneish M-score |
| Length | About 1,080 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MS Accounting |
| Updated | October 2026 |
Free sample paper for ACC 691 Module 3
Red Flag Analysis of the 2024 Annual Report
[Student Name]
Southern New Hampshire University
ACC 691: Detection and Prevention of Fraudulent Financial Statements
Milestone One
[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.
Red Flag Analysis of the 2024 Annual Report
Introduction
This milestone examines the company's 2024 annual report as an outside reader could have in March 2025, before the internal investigation began. The company reported revenue of $640 million, exactly matching guidance issued early in the year, and earnings of $2.10 per share. The goal is to judge whether the statements contained signals that warranted closer testing and, if so, which accounts. The analysis uses comparative ratios for 2023 and 2024, the eight-variable model developed by Beneish (1999) and nonfinancial measures from the annual report and investor presentations. It does not assume the later findings; each flag is weighed against an innocent explanation.
Comparative Ratio Analysis
The table compares key relationships across the two years. Amounts are in millions of dollars.
Selected ratios, 2023 and 2024
| Measure | 2023 | 2024 | Change |
|---|---|---|---|
| Revenue | 598 | 640 | +7.0% |
| Accounts receivable | 75 | 125 | +66.7% |
| Days sales outstanding | 46 | 71 | +25 days |
| Gross margin | 33.0% | 33.4% | +0.4 points |
| Net income | 47 | 52 | +10.6% |
| Cash flow from operations | 55 | 18 | -67.3% |
| Warranty reserve as % of revenue | 2.4% | 1.4% | -1.0 points |
| Allowance for doubtful accounts as % of receivables | 3.2% | 2.0% | -1.2 points |
| Other noncurrent assets | 30 | 41 | +36.7% |
Three patterns stand out. First, receivables grew nearly ten times as fast as revenue, so customers were taking much longer to pay or the receivables were not real sales. Second, net income rose while cash from operations fell by two thirds; the gap of $34 million is unusually large for a manufacturer with stable terms. Third, two reserves moved in the direction that raises earnings even though risk was rising: the warranty reserve fell as a percentage of sales during a year of record shipments, and the allowance for bad debts fell while receivables aged. Each change alone has a possible explanation, but all three increase reported profit, and none is accompanied by a clear disclosure of why the estimate changed.
Beneish M-Score
The M-score combines eight indexes, each comparing 2024 with 2023, into a single score. Beneish (1999) reported that firms scoring above about -1.78 resembled the manipulators in his sample.
Beneish index calculation for 2024
| Index | What it measures | Value | Weighted |
|---|---|---|---|
| DSRI | Receivables relative to sales | 1.557 | 1.432 |
| GMI | Gross margin deterioration | 0.988 | 0.522 |
| AQI | Share of assets that are neither current nor fixed | 1.120 | 0.452 |
| SGI | Sales growth | 1.070 | 0.954 |
| DEPI | Slowing depreciation | 1.020 | 0.117 |
| SGAI | Selling and administrative cost relative to sales | 0.962 | -0.165 |
| TATA | Total accruals to total assets | 0.059 | 0.274 |
| LVGI | Change in debt relative to assets | 1.042 | -0.341 |
| Constant | -4.840 | ||
| M-score | -1.594 |
The score of about -1.59 sits above the cutoff, while the same calculation for 2023 gives roughly -2.6, well inside the range of nonmanipulators. Almost all of the change comes from two indexes. The receivables index of 1.56 is the largest single contributor, and total accruals of nearly 6 percent of assets add the second. The asset quality index also rose because capitalized engineering costs pushed other assets up by $11 million. Gross margin and sales growth are unremarkable, which is itself informative: the company did not look like a firm under pressure, which is exactly what a firm hiding a shortfall would want.
Nonfinancial Measures
Brazel et al. (2009) found that a gap between reported revenue growth and the growth in nonfinancial measures such as employees and facilities helps distinguish fraud firms from their competitors. The company's own disclosures allow a version of that test. Production employees rose from 2,140 to 2,155, under 1 percent. The investor presentation reported plant labor hours flat at about 4.3 million, and no new lines or facilities were added. Unit shipments disclosed in the presentation grew 2 percent. Revenue growth of 7 percent with flat capacity and 2 percent unit growth implies either a large price increase, which the company did not report and which gross margin does not show, or revenue that did not come from shipments. This gap is the most persuasive flag in the analysis because it is hard to explain without one of the reported numbers being wrong.
Innocent Explanations
A fair reading must test other explanations. Receivable days could rise if the company had offered extended terms to win a large customer, but the annual report described terms as unchanged. Cash flow could fall if inventory were built for an expected 2025 surge, yet inventory rose only $6 million. The warranty reserve could fall if product quality had improved, but the report mentioned no quality program, and warranty claims paid actually increased. Capitalizing development costs can be appropriate for software embedded in equipment, though the policy note had not changed and the amounts were new. Hogan et al. (2008) observe that fraud indicators work best in combination, and here the innocent explanations do not hold together: each requires a different story, and none is disclosed.
Accounts to Test First
Based on this analysis, three areas deserve detailed testing, in this order. Revenue and receivables come first, especially fourth-quarter sales, credits issued after year end and confirmations of balances and terms with dealers and large customers. Reserves come second: the warranty and bad debt estimates should be rebuilt from claims and aging data to see whether the reductions were supported. Capitalized engineering costs come third, by tracing the amounts to project records and testing whether the work met the criteria for capitalization. Inventory existence is a fourth area if receivable testing reveals unshipped goods.
The order matters for a practical reason. Revenue testing is the cheapest way to confirm or dismiss the largest flag, because dealer and customer confirmations can be sent within days and answer two questions at once: whether the balances exist and whether the terms match the contracts. If confirmations come back clean, the receivables index may reflect a slow-paying customer rather than a scheme, and the reserves become the priority. If they reveal side agreements, the same evidence tells the investigator which quarters to reopen and which people to interview.
Conclusion
The 2024 statements carried enough signals to justify a targeted examination: receivables that outran sales, earnings that outran cash, reserves that fell as risk rose and growth that capacity could not explain, captured in an M-score above the model's threshold. What tips the balance is not any single ratio but the absence of a coherent innocent story that covers them all. None proves fraud, and the analysis should be read as a ranking of where to look rather than a finding. Milestone Two will examine how the board and audit committee responded to the same information.
References
Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296
Brazel, J. F., Jones, K. L., & Zimbelman, M. F. (2009). Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, 47(5), 1135-1166. https://doi.org/10.1111/j.1475-679X.2009.00349.x
Hogan, C. E., Rezaee, Z., Riley, R. A., & Velury, U. K. (2008). Financial statement fraud: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 27(2), 231-252. https://doi.org/10.2308/aud.2008.27.2.231
What the ACC 691 Module 3 instructions ask for
Milestone One in ACC 691 asks you to read a company's financial statements as a fraud examiner would before the facts are known. You compute ratios across at least two years, apply a prediction model such as the Beneish M-score, look for patterns that a single ratio would miss and decide which accounts deserve testing. The guidelines usually want the calculations shown, an interpretation of each flag and an honest note on what could explain it innocently. Some versions add nonfinancial measures, such as employee counts or capacity, as a check on reported growth. Treat it as the foundation for the later milestones, since the accounts you rank here become the ones you examine in Modules Six and Nine.
How this ACC 691 Module 3 milestone one example is built
The paper takes the 2024 annual report of a kitchen equipment maker that had told investors to expect $640 million of sales and reported exactly that. It shows receivable days climbing from 46 to 71, net income of $52 million against operating cash flow of only $18 million, a warranty reserve falling from 2.4 to 1.4 percent of sales, and other assets up by a third after engineering costs were capitalized. A table works through all eight Beneish indexes to a score near -1.59, above the -1.78 threshold the model's author proposed. Flat production hours and headcount make the growth hard to believe, and the paper ranks revenue, reserves and capitalized costs as the first accounts to test.
Where the ACC 691 Module 3 rubric puts the points
The Milestone One rubric typically gives marks for accurate ratio calculations, correct application of the prediction model, interpretation of each red flag, use of nonfinancial evidence, consideration of other explanations and a clear set of next steps. The strongest submissions show every input so a reader can rebuild the M-score, explain what each index measures rather than only its value, and connect flags that point at the same account. A paper that announces fraud from one ratio loses credit for judgment, while one that lists ratios without interpretation loses credit for analysis. Citations to the original model and to research on fraud indicators support the reasoning, and tables keep the numbers readable.
ACC 691 Module 3 help: the mistakes that cost points
Errors in the M-score are the most frequent problem: students invert the gross margin index, use total liabilities where the model expects current liabilities plus long-term debt, or compute total accruals from net income instead of income from continuing operations. A second issue is treating a score above the cutoff as proof; the model was built to rank firms for further work, and it misclassifies some honest growth companies. Third, papers forget the nonfinancial side, even though a sales jump with flat staff and capacity is often the most persuasive flag. Finish by ranking accounts, because Milestone Two builds directly on that list.
Get ACC 691 Module 3 written to your instructions
Send the ACC 691 Milestone One guidelines and the company's statements. You get the ratio work, the full M-score with each index shown, nonfinancial checks and a ranked list of accounts to test. Most come back inside two days, and we write the first sample at no charge. 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 691 Module 3 questions, answered
Where can I find a free ACC 691 Module 3 Milestone One sample?
This page carries a complete ACC 691 Milestone One paper that applies ratio analysis and the Beneish M-score to a kitchen equipment maker's 2024 report.
What is the Beneish M-score?
A probit model from 1999 that combines eight indexes, such as receivable growth relative to sales, margin decline and total accruals, into one number used to rank firms by their likelihood of having manipulated earnings.
What M-score suggests manipulation?
In the original eight-variable version a score above about -1.78 places a firm in the group the model associates with manipulators, though later work often uses -2.22, and neither is proof on its own.
Why compare net income with operating cash flow?
Earnings that run well ahead of cash for more than a year can mean revenue or expenses are being recorded on paper without cash behind them, which is typical of revenue and reserve schemes.
What nonfinancial red flags matter in a fraud analysis?
Measures such as employees, plant hours, square footage or units shipped that should move with sales; research finds that a gap between those measures and reported growth helps predict fraud.