Abstract:   Financial statement fraud can be costly. So, early detection is important to mitigate losses. This article describes a screening tool known as the Beneish model that can help clients quickly assess the likelihood of earnings manipulation. It uses eight financial metrics to determine the probability that revenue has been overstated and expenses have been understated.

Screening for signs of fraud

Beneish model helps detect earnings manipulation

Financial statement manipulation is the costliest type of occupational fraud. The 2018 Report to the Nations published by the Association of Certified Fraud Examiners found that the median loss from financial statement fraud was $800,000, compared to median losses of $114,000 for asset misappropriation and $250,000 for corruption.

With any type of fraud, the sooner it’s detected, the more likely losses can be mitigated. Here’s a tool to help clients quickly assess the likelihood of earnings manipulation.

Model at work

The Beneish model measures the probability that a company’s revenue has been inflated and its expenses have been understated. The model generally computes an “M score” from comparisons between consecutive financial reporting periods of various metrics, including:

  • Days sales in receivables (accounts receivables ÷ sales),
  • Gross margin [(sales – cost of sales) ÷ sales],
  • Asset quality {1 – [(current assets + fixed assets) ÷ total assets]},
  • Sales growth,
  • Depreciation [depreciation ÷ (fixed assets + depreciation)],
  • Sales general and administrative (SG&A) expenses (SG&A expenses ÷ sales),
  • Leverage [(current liabilities + long term debt) ÷ total assets], and
  • Total accruals (working capital – cash – current taxes payable – depreciation and amortization) to total assets in the current reporting period.

These financial metrics are designed to capture the effects of earnings manipulation or the preconditions that can prompt a company to engage in earnings manipulation. Each metric is weighted using coefficients. For example, the days sales in receivables index has a positive coefficient because disproportionate increases might indicate an overstatement of receivables to artificially boost revenue.

Cautionary notes

The economics professor who created the Beneish model admits some important limitations. Notably, the model can’t reliably be applied to privately held businesses, because it was developed using public company data. Additionally, his sample involved manipulation to overstate earnings. Therefore, the model isn’t useful in circumstances where it could prove advantageous to reduce earnings — for example, to push revenue into the next quarter to help meet a target for that quarter or to boost revenue prior to a prospective merger.

Some distortions in financial statement data also could have a cause that’s unrelated to earnings manipulation. A metric might be distorted by, say, a material acquisition during the period examined, a material shift in the company’s strategy for maximizing value or a significant change in the relevant economic environment.

Just a red flag

Because it’s relatively easy to use, the Beneish model can be an efficient screening tool for earnings manipulation. But a high M score doesn’t prove fraud. Rather, it suggests that further investigation is necessary. Contact a forensic accounting expert for more information.

© 2018