'Income smoothing' becomes fraud

A team of fraud fighters uses three different analytical tools for measuring accruals (amounts that aren't cash transactions) in a company and finds that some managers had added fraudulent loans to boost the numbers and keep the shareholders happy.

Solzarity Inc. was a mid-sized, well-respected community company that provided loan services to individuals. All looked fine on the surface. The financial statements painted a rather rosy future. However, the shareholders eventually questioned the financial information. Some of the financial ratios — such as the current ratio, working capital and working capital turnover — suggested lower liquidity issues. (This is an actual case, but we've changed the name of the firm.)

Now, the combination of lower liquidity and higher profitability isn't necessarily indicative of financial statement manipulation. For example, a company may be using cash earned from the profits to increase and/or improve capital assets. However, at some point, the available cash must be used to pay its bills and capital assets must shrink.

Yet in Solzarity's operations, capital assets weren't increasing so there wasn't a reasonable explanation for the inconsistency. Our team used various analytical tools to measure the accruals within the financial statements to determine if the company was manipulating the financial statements that caused the inconsistency or if management needed to revise its strategic plans for the upcoming year.

Ultimately, after we analyzed seven years of loan portfolio actions we found multiple management misrepresentations. When we reviewed our findings with the company, it said that some managers had added fraudulent loans during the years in which earnings had slipped from previous years. Some members of management felt that the shareholders' and the company's reputations were at risk if the company hadn't shown growth. Unfortunately, they had decided that they were going to grow only on paper — not in reality. (For more information on common financial ratios, see the sidebar at the end.)

What are accruals and how can we test them?

Accruals in financial statements play an important role in determining the overall financial health of a company, but they also open opportunities for misrepresentation of a company's performance. An accrual is any amount that isn't a cash transaction, such as accounts receivable and accounts payable. Allowances and reserves also are considered accruals and are based upon management's estimates. All of these areas are subject to financial statement manipulation, so measuring accruals concentrates on their effects on current income and future cash flow.

Analysts generally use three techniques to measure the impact of accruals in financial statements: Dechow-Dichev Accrual Quality, Sloan's Accruals and Jones Nondiscretionary Accruals. These methods can identify manipulation of earnings or possible "income smoothing" — better known as earnings management. While income smoothing might not constitute fraud, intentional manipulation of financial statements to mislead the readers of those statements does.

Dechow-Dichev accrual quality technique

The Dechow-Dichev Accrual Quality method measures the quality of accruals in the financial statements based on realized cash flow for future periods. Lower accrual quality can show that the accruals represented in the financial statements are unrelated to future cash flows, which suggests the possibility of an error or possible intentional misstatement of the financial statements.

Lower accrual quality also suggests the possibility of fluctuating sales, unpredictable cash flows and continued losses of earnings when any one of these events or a combination of these events apply pressure upon management to improve their earnings without actually receiving the needed cash for continued operations. (See The Quality of Accruals and Earnings: The role of Accrual Estimation Errors, by Patricia M. Dechow and Ilia D. Dichev, a 2002 supplement to Accounting Review, Volume 77, pages 35-59.)

For those who want to know the equation for this method (and I know that might not include all readers), according to Dechow and Dichev in the Accounting Review supplement, the actual measurement combines the change in working capital and cash flow from operations for the current year compared to the average of total assets for both the current year and the prior year:

(cy Operating Cash Flow + ∆ cy WC) ÷  (cy TA + py TA)

cy = Current Year

py = Prior Year

∆ = change from prior year

WC = Working Capital

TA = Total Assets

In the same supplement, Dechow and Dichev noted an easy method for calculating the Dechow-Dichev earnings described as earnings after short-term accruals but before long-term accruals:

cy Operating Cash Flow + ∆ cy WC = Earnings

Because the Dechow-Dichev earnings separate short-term accruals from long-term accruals, comparing this calculation to net income should show a relatively stable relationship. Both of these analytical tools are most effective when comparing these calculations to net income by using a dual-axis chart in Excel.

Figure 1: Dechow-Dichev Accrual Quality compared to net income

We observed several items about the financial statements when we used the Dechow-Dichev method for analyzing accruals. Figure 1 shows the calculations of accrual quality to net income for Solzarity Inc. and provides some interesting points to consider.

Precise estimates in Dechow-Dichev's research do imply future cash flows while imprecise measurements won't likely produce future cash and produce instability in the accrual quality. Figure 1 indicates instability in the accrual quality by showing variances in the calculation ranging from -.02 to +.05, which indicates that the accruals aren't precise and possibly contain errors and/or misrepresentations.

From year 2009 to year 2010, net income decreased while the Dechow-Dichev accrual quality increased, which is contradictory to the normal relationship between the two. The same contradiction applies from year 2010 to year 2011 when net income increased while the Dechow-Dichev accrual quality decreased. The significant change from year 2011 to year 2012 is a red flag that suggests errors or possible manipulation in the accruals.

Figure 2: Dechow-Dichev earnings compared to net income

Figure 2 shows a relatively stable relationship between the Dechow-Dichev earnings and net income from year 2006 through 2008. However, beginning from 2009 through year 2011, the figure shows an unstable relationship. In year 2010, net income decreases while the Dechow-Dichev earnings increase. In year 2011, net income increases while the Dechow-Dichev earnings decrease.

Both Figures 1 and 2 show anomalies in the financial statements beginning in year 2009 through year 2011 and contradictory relationships in years 2010 and 2011. Again, Figure 2 also indicates a significant change from year 2011 to year 2012. Although the contrasting relationships are a red flag by themselves, the significant change from year 2011 to 2012 is also a warning sign of potential earnings manipulation.

Sloan's accruals method

Professor Richard G. Sloan developed a method of testing accruals by focusing on the reflection of stock prices based on the accrual and cash flow component of earnings. (See "Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?" by Richard G. Sloan, Accounting Review, Vol. 71, No. 3, July 1996, page 293.)

Sloan based his model on the calculation of the implied cash component of earnings from changes in current net operating assets and their relationships to net income. The calculations are somewhat complex but easily can be accomplished using formulas in Excel to calculate each step of the formula. The accrual component of the model is the change in the current net operating assets. A positive accrual component indicates accruals have increased net income while a negative accrual component indicates that accruals have decreased net income.

To calculate the implied cash component simply add or subtract the accrual component to net income based on whether the accrual component either increased or decreased income. According to Sloan's Accounting Review article, here are the formula steps:

• Implied cash component = net income +/- changes in current net operating assets.
• Current net operating assets = current operating assets - current operating liabilities.
• Current operating assets = total current assets - cash and cash equivalents.
• Current operating liabilities = total current liabilities – short-term debt – current portion of long-term debt – income taxes payable.

From a forensic analysis perspective, the best method to use for analyzing accruals is a comparison of net income (loss), the implied cash component and the accrual component looking for higher levels of accruals compared to net income and the implied cash component of earnings.

A high level of an accrual component when compared to net income and the implied cash component is a red flag. By comparing these components with a visual graph as in Figure 3 below, the Sloan Accrual calculations for Solzarity, we can easily see the impact of the accruals and the implied cash component to net income.

Figure 3: Sloan Accrual calculations

One important discovery in using Sloan's Accruals is the significant impact of the accrual component to net income in year 2012 when compared to the other years under study. Specifically, the accrual component is significantly higher compared to the implied cash component and net income that has increased over the prior year. This relationship is a red flag for possible earnings manipulation. Sloan's analysis supports the significant change noted in the Dechow-Dichev analyses of the financial statements. Now, two different types of analytical techniques point to the possibility of manipulation of the financial statement information.

Jones nondiscretionary accruals method

Jones Nondiscretionary Accruals measures accruals generally required for financial statement presentations such as accrued taxes and accrued payroll. (See Earnings Management During Import Relief Investigations, by Jennifer J. Jones, Ph.D., Journal of Accounting Research, Volume 29, No. 2, Autumn 1991, page 211.)

The fraud examiner can easily calculate these accruals by using existing financial information so the chance of error is slim. However, Jones believes that by measuring nondiscretionary accruals, we are also measuring discretionary accruals that rely primarily on management's assessments and estimations. These include such items as management's bonuses, allowances and reserves, which companies often use to facilitate income smoothing. Although the formula for measuring nondiscretionary accruals isn't as complex as Sloan's accruals, there are still several steps involved in the measurement:

(1 / TA py) + ((Rev cy - Rev py) / TA cy) + (PPE cy / TA py)

TA = Total Assets

Rev = Revenue

PPE = Property, plant and equipment, gross

cy = current year

py = prior year

See Figure 4 below for the calculations of the Jones Nondiscretionary Accruals method.

Figure 4: Jones Nondiscretionary Accruals calculations

Remember these calculations are for nondiscretionary accruals, so the lower amount of nondiscretionary accruals shown in Figure 4 indicates a higher amount of discretionary accruals. Along with looking at totals, it's also very important to look at the movement to determine if in certain years discretionary accruals increase from the prior year. For example, from year 2006 through 2008, nondiscretionary accruals decrease indicating that discretionary accruals are increasing therefore subjecting the financial statement to possible manipulation or income smoothing. In fact, from 2009 through 2013, the trend showing increases one year and then decreases the second year for the nondiscretionary accruals is consistent and presents a red flag to the possibility of managements' manipulation of discretionary accruals. 

Three methods aren't adequate for searching specific areas

All three different analytical techniques show warning signs relating to the possibility of financial statement manipulation and/or income smoothing, but none of them indicate specific areas for the fraud examiner or financial forensic analyst to search. We must use other analytical techniques to find specific areas that are creating these warning flags from the accrual testing. Our team also used some of the components of the Beneish M-Score (a mathematical model that uses financial ratios and eight variables to detect whether a company has manipulated its earnings) that pointed to anomalies in interest income, allowances for loan losses, and loans receivable. The question then became whether management was utilizing income-smoothing techniques to show consistent earnings growth from year to year or if they included intentional misstatements in the financial statements to increase earnings.

Ensuing terminations and new internal controls

After we analyzed the seven-year loan portfolio and found multiple management misrepresentations — which some managers implemented because they felt that that company's reputation would suffer if they didn't show growth — the shareholders dismissed those involved in the scheme (including one member of senior management responsible for financial reporting, along with the chair of the audit committee) and pursued legal action against those involved in the misrepresentations. The shareholders also established new internal control procedures to address a thorough review of the financial statement information, along with various analyses for monthly review instead of quarterly.

The company disallowed cross-access of computer information menus among employees and further restricted access rights depending on job functions. The shareholders also required management to develop an in-house internal audit function for the company. (Previously, an external auditor provided the internal audit function.) We calculated that the company lost about $500,000 because of the fraud. The case is still ongoing.

The three analytic techniques we used to measure accruals at Solzarity Inc. can't be, of course, the only tools for finding fraud, but they're excellent components of many fraud examinations.

Pam Mantone, CFE, CPA, MAFF, is senior manager for Decosimo Advisory Services and author of "Using Analytics to Detect Possible Fraud: Tools and Techniques," published by Wiley in September 2013.

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