Are you using substantive analytical procedures in your audits? Many auditors rely solely on tests of details when a better option is available. Substantive analytics, in some cases, provide better evidential matter. And they are often more efficient than tests of details.
In this article, I provide:
Professional standards define analytical procedures as evaluations of financial and non-financial data with plausible relationships. An example of such a relationship is salaries may be expected to be a certain percent of total expenses. In other words, numbers behave in particular ways. Because they do, we can use these relationships as evidential matter for our audit opinions.
The video below provides an overview of substantive analytical procedures. It comes from my YouTube playlist Audit Risk Assessment Made Easy.
Before we look at what substantive analytics are and how we use them, let’s see how analytical procedures are used in audits.
Auditors use analytics in three stages:
Preliminary analytics are performed as a risk assessment procedure. We use them to locate potential material misstatements. And if we identify unexpected activity, we plan a response. For example, if we expect payroll to go up 5% but it goes down 8%, then we plan further audit procedures to see why: these can include tests of details, substantive analytics, and test of controls.
At the completion of the audit, we use final analytics to determine if we have addressed all risks of material misstatement. Here we compare our numbers and ask, “Have we dealt with all risks of material misstatement?” If yes, fine. If not, then we may need to perform additional further audit procedures.
Less precision is necessary for preliminary and final analytics as compared to substantive analytics. Preliminary analytics locate misstatements and final analytics confirm the results of the audit. But substantive analytics are used to prove material misstatements are not present.
Substantive analytical procedures can, in certain cases, be more effective and efficient than a test of details.
For example, if the ratio of salaries to total expenses has been in the 46% to 48% range for the last few years, then you can use this ratio as a substantive analytic to prove the payroll occurrence assertion. If your expectation is that payroll would be in this range and your computation yields 48%, then your substantive analytic provides evidence that salaries occurred. And this is much easier than a test of details such as a test of forty payroll transactions (where you might agree hours paid to time records and payroll rates to authorized amounts).
For a small entity with six employees, one payroll substantive analytic might be sufficient, but you may need to disaggregate the payroll information for a larger company with six hundred people. For instance, you might divide departmental salaries by total salaries and compare those ratios to the prior year. Disaggregation adds more precision to the analytic, resulting in better evidential matter.
Another example of disaggregation is in relation to revenues. If the company has four major sources of revenue, disaggregate the substantive analytical revenue sources. You might use a trend analysis by revenue source for the last three years. Or you might recompute an estimate of one or more revenue sources based of units sold or property rented.
The type of substantive analytic is dependent on the nature of the transaction or account balance. If a company rents fifty apartments at the same monthly rate, computing an estimate of revenue is easy. But if a company sells fifty different products at different prices, you may need to disaggregate the substantive analytical data.
Additionally, consider disaggregating substantive analytics by region if the company has different geographic locations.
Are there audit areas where substantive analytical procedures should not be used alone? Yes. When responding to a significant risk. A test of details must be used when a significant risk is present. For example, a bank’s allowance for loan losses. This allowance is a highly complex estimate; therefore, a test of details is required. You could not solely compare the allowance to prior years, for example, though such a comparison could complement a test of details. In other words, you could perform a test of details and use a substantive analytic. But a substantive analytic alone would not do.
Now let’s consider how auditors use substantive analytics to respond to the risk of material misstatement.
Once you identify a risk of material misstatement, you plan further audit procedures including (1) test of details, (2) substantive analytical procedures, and (3) test of controls. Many auditors use a test of details without performing substantive analytics. Why? For many, it’s habit. We’ve always tested bank reconciliations, for example, so we continue to do so. But maybe we’ve never used substantive analytics to prove revenues or expenses.
A test of details is often used in relation to balance sheet accounts such as cash, receivables, and debt.
Substantive analytics, on the other hand, are sometimes more fitting for income statement accounts such as revenue or expenses. Why? Because income statement accounts tend to be more consistent from year to year. Here are some examples:
So consider using substantive analytics when the volume of transactions is high and the account balance is predictable over time. Additionally, use substantive analytics in lower risk areas, including some balance sheet accounts such as:
Audit standards tell us that substantive analytics are more appropriate when the risk of misstatement is lower. The higher the risk of misstatement, the more you should use a test of details. For instance, it’s better to use tests of details for significant receivable accounts. But substantive analytics may work well for prepaid insurance.
Additionally, substantive analytics can be combined with a test of details or a test of controls. If, for example, you’re planning a risk response for accounts payable and expenses, you might use a combined approach: a test of details for accounts payable (e.g., search for unrecorded liabilities) and substantive analytics for expense (e.g., departmental expenses divided by total expenses compared to the prior year).
Another common combined approach is a test of details sample along with substantive analytics. If the substantive analytics are effective, you can reduce the sample size, making the overall approach more efficient.
Certain substantive analytics provide higher levels of assurance. For example, computing expected rental income provides high assurrance. If your client rents fifty identical apartments at $2,000 a month, the computation is easy and the assurance is high.
Other types of analytics provide lower assurance: topside ratios or period-to-period comparisons at the financial statement level, as examples. You can, however, increase the substantive analytical assurance level by taking actions such as:
Comparing balances with a prior period and providing no explanations is not sufficient as a substantive analytic. Also, if the activity is unexpected, solely documenting client responses to questions is not sufficient. For example, these client answers will not do:
Vague responses are not evidential matter and can result in audit failure, or—worse yet—litigation against your firm.
Substantive analytics can be used in a wide variety of ways.
Here are examples of substantive analytics:
Now let’s see how to document your substantive analytics.
In performing substantive analytical procedures, document the following:
1. The reliability of the data
Document why you believe the data is trustworthy. Reasons could include your prior experience with the client’s accounting system and internal controls related to the information you are using. Though a walkthrough sheds light on those controls, a test of controls for effectiveness provides even greater support for the reliability of the data. Testing controls is optional, however.
Document the assertions being addressed and the related risks of material misstatement.
Document a sufficiently precise expected result of the computation or comparison. You can use a range. Document the expectation prior to examining the recorded numbers. Why? To reduce bias. If the current year expectation is different from the prior year, explain why. For example, if payroll has been stable over the last three years but is expected to increase eight percent in the current year, document why. A less precise expectation may be acceptable if a test of details is performed along with the substantive analytic.
Document if the substantive analytic is to be used alone or in conjunction with a test of details.
5. Acceptable difference
The acceptable difference is the amount that requires no further investigation. So, for example, if the analytic is $30,000 different from the recorded amount and the acceptable difference is $50,000, you are done. No additional work is necessary. Unacceptable differences require further investigation such as inquiries of management and other audit procedures. Consider the performance materiality for the transaction or account balance as you develop the acceptable difference amount. Also, consider the assessed risk of material misstatement. Higher risk requires a lower acceptable difference.
Document whether the computation or comparison falls within your expectation. Perform and document other procedures performed if the result is not within your acceptable difference. Your conclusion should include a statement regarding whether you believe the account or transaction balance is materially correct. After all, that’s the purpose of the substantive analytic.
Here are some concluding thoughts about substantive analytics.
Substantive analytics are not required. So, think of them as an efficient alternative to test of details.
If the company has weak internal controls or a history of significant misstatements, rely more on tests of details. Substantive analytics work better in stable environments. Additionally, if you, as the auditor, expect to make several material audit adjustments, record those prior to creating substantive analytics. This will help reduce the distortion from those misstatements.
Testing of controls for effectiveness lends strength to substantive analytics. If the controls are effective, you’ll have more confidence in the substantive analytics. For example, if you test the disbursement approval controls and find them to be effective, the expense analytics will be more trustworthy. If you are testing controls for effectiveness, you may want to do so before creating any related substantive analytics.
You may also want to see AU-C 520, Analytical Procedures in the audit standards.
Check out my new book: Audit Risk Assessment Made Easy. Click the book cover below to see it on Amazon. I provide a free YouTube video series that goes along with the book.
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Charles Hall is a practicing CPA and Certified Fraud Examiner. For the last thirty years, he has primarily audited governments, nonprofits, and small businesses. He is the author of The Little Book of Local Government Fraud Prevention and Preparation of Financial Statements & Compilation Engagements. He frequently speaks at continuing education events. Charles is the quality control partner for McNair, McLemore, Middlebrooks & Co. where he provides daily audit and accounting assistance to over 65 CPAs. In addition, he consults with other CPA firms, assisting them with auditing and accounting issues.
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