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The use of analytical procedures.

Auditors facing competitive market pressures for audit services continually reassess the efficiency of audit procedures while maintaining the overall effectiveness of the audit plan. Current guidance for the application of analytical procedures as part of the audit is found in Statement on Auditing Standards no. 56, Analytical Procedures. In 1993, the auditing standards board formed a task force to consider certain issues related to SAS no. 56 and the need for additional guidance. Although the task force concluded that SAS no. 56 did not need to be amended, it recommended an auditing procedures study be developed to aid practitioners in applying analytical procedures. The purpose of this article is to discuss common concerns expressed to the task force about the use of such procedures in practice and to emphasize some of the cautions about their use.

THE NEED FOR AN EXPECTATION

An expectation is an estimate of an account balance based on

* The auditor's analysis of the trend of the account.

* Related financial ratios.

* Explicit financial models of factors that affect the account.

One question posed to the task force was whether an expectation is a prerequisite to performing analytical procedures. Paragraph 5 of SAS no. 56 says, "Analytical procedures involve comparisons of recorded amounts, or ratios developed from recorded amounts, to expectations developed by the auditor." (Emphasis added.) Proper application of analytical procedures in accordance with SAS no. 56 requires the development of an expectation. This is true regardless of the audit phase (planning, substantive testing and final review) in which analytical procedures are used. The expectation is compared with the recorded amount--or other benchmarks derived from recorded amounts--to assess the potential for misstatement.

Without an expectation as the first part of an analytical procedure, the procedure is potentially biased by other irrelevant information. For example, a comparison of current and prior year balances is biased by the presumption that prior year balances are relevant predictors of what current balances should be. Using the current-to-prior-year comparison approach increases the chance auditors will not properly identify an account for which the balance should have changed significantly, for example, because of the effect a sharp increase in utility rates has on utility expenses.

Using analytical procedures without starting with an expectation can be compared to a medical doctor performing a routine physical on a patient without consulting the patient's medical records. The patient's blood pressure and weight as observed in a physical, for example, cannot be interpreted properly outside the context of his or her complete medical history. Moreover, for the doctor to consult the records after having observed the patient introduces bias, as the doctor naturally has already begun to consider potentially irrelevant and distracting hypotheses for the patient's observed condition.

SOME EXPECTATIONS ARE BETTER THAN OTHERS

Auditors commonly use three broad types of analytical procedures (or methods) to form an expectation:

1. Trend analysis. The comparison of a current account balance or item with the prior year balance or with a trend in two or more prior periods' balances.

2. Ratio analysis. The comparison of a ratio calculated for the current year with a related ratio for a prior year, an industry average or budget. Ratios commonly have financial statement data in the numerator and the denominator.

3. Model-based procedures. The use of client operating data and relevant external data (industry and general economic information) to develop an expectation for the account balance or item. There are two types of procedures--reasonableness tests and regression analysis.

Model-based procedures differ from ratio and trend analyses in two key ways:

1. While expectation formation is implicit in trend and ratio analyses, expectation formation is explicit in model-based procedures.

2. Model-based procedures use operating and external data in addition to financial data to develop the expectation.

While all three procedures are widely used in auditing, they differ significantly in their ability to identify potential misstatement. Trend analysis is the weakest; it relies on data for only a single account. In contrast, ratio analysis incorporates directly the expected relationships between two or more accounts. For example, turnover ratios are useful because there typically is a stable relationship between sales and other financial statement accounts, especially receivables and inventory. Thus, ratio analysis is more likely than trend analysis to identify potential misstatement.

Both ratio and trend analyses are limited in that the development of expectations is implicit. The presumption is that the balance or ratio should compare with the prior year or with the industry average. Since model-based procedures incorporate expectation development explicitly, they are likely to be much more effective at signaling misstatement. Putting the expectation formation step first helps emphasize the importance of assumptions implicit in the estimate (if the prior year's balance is still relevant for a comparison) and also will focus the auditor's attention on the means to make the estimate (and therefore the analytical procedure) most effective, as described in SAS no. 56.

Moreover, the modelling approach is more effective because it links financial data directly to relevant operating data. When, as often is the case, changes in operations are the principal cause of changes in the financial statements, model-based procedures provide an effective way of incorporating die relevant operating data. In effect, model-based procedures are a direct test of the consistency between the operating and financial data--an important test in many types of financial statement assertions such as completeness. An example is the test of rental revenues for a real estate management firm. In this case, the use of an analytical procedure to form an expectation for rental revenues based on capacity, occupancy rates and rental charges should provide reliable evidence about the accuracy and completeness of recorded rental revenues. The possibility of unrecorded revenues is likely to be identified in this way.

Research on the effectiveness of analytical procedures shows that model-based procedures outperform ratio and trend analyses. Similarly, research has shown that explicitly incorporating expectations significantly improves the auditor's use of analytical procedures. Thus, a model-based procedure can increase the effectiveness of an analytical procedure. Other ways to improve an expectation's precision, and thereby the effectiveness of an analytical procedure, are discussed below.

THE IMPORTANCE OF PRECISION

Precision is the auditor's measure of the potential effectiveness of an analytical procedure, and therefore of the degree of reliance that can be placed on the procedure and the audit assurance (reduction in audit risk) derived from it. Effectiveness refers to the procedure's ability to identify accounts with or without misstatement--that is, to correctly identify whether a given fluctuation in an account balance or ratio results from a misstatement. For example, a fluctuation in interest expense could be due to a misstatement or to a change in interest rate or outstanding balance. The greater the precision of the expectation, the more likely the analytical procedure will identify correctly whether or not a misstatement is the cause of a fluctuation.

The auditor's consideration of the degree of precision needed for an expectation depends on whether the analytical procedure is used in planning, as a substantive test or in the final review. Precision is important in all three phases but is most important in the substantive testing phase because the procedure is relied on to provide audit assurance. In the planning and review phases, analytical procedures developed with greater precision are desirable as they will more effectively identify the accounts and items with the greatest potential for misstatement.

There are four key factors, discussed in the exhibit above, that an auditor should consider when assessing the precision of a given expectation. Specific consideration of each factor is necessary to determine the degree of assurance derived from a procedure. Other factors that do not affect an analytical procedure's precision but that affect the reliance and assurance derived from it include the nature of the assertion tested, the auditor's assessment of control risk for the entity and the results of the auditor's investigation and evaluation of significant differences.

Nature of assertion tested. SAS no. 56 cautions that for certain assertions, analytical procedures may not be as effective or efficient as tests of details in providing the desired assurance level. Examples of such assertions are rights and obligations. Conversely, SAS no. 56 recognizes that for other assertions, analytical procedures are effective in providing the appropriate level of assurance. One example is the completeness assertion in which analytical procedures are likely to be very useful in examining for unrecorded sales.

Control risk. Although the auditor's assessment of such risk does not affect the expectation's precision, it can affect die level of assurance derived from an analytical procedure. There may be cases when the performance of analytical procedures as the exclusive test of an assertion is not appropriate due to the absence of effective controls. The AICPA audit and accounting guide Consideration of the Internal Control Structure in a Financial Statement Audit points out that a test of details usually is required when testing a material assertion if control risk and inherent risk for the assertion are assessed at the maximum, even though a very effective" analytical procedure could be developed.

Significant differences. SAS no. 56 says auditors should evaluate significant unexpected differences. This investigation and evaluation should begin with an inquiry of management, followed-up ordinarily with corroboration by other evidential matter. If the unexpected difference cannot be satisfactorily explained, the auditor performs additional audit procedures to determine whether the difference is a likely misstatement.

IMPORTANT AUDIT TOOL

Analytical procedures are an important audit tool, made even more effective by proper attention to expectation formation and assessment of the precision of the expectation. However, analytical procedures should not be used as an easy and inexpensive audit approach. Although many think analytical procedures generally do not provide much audit assurance, we believe they are effective substantive tests that provide assurance commensurate with the planning and effort invested in their development and execution.

RELATED ARTICLE: The Three Steps in Performing Analytical Procedures

First: Develop an expectation for the account balance or item using trend or ratio analysis or a model-based procedure, such as a reasonableness test or regression.

Second: Compare the expected amount with the recorded balance.

Third: Based on the difference between the recorded and expected balance and considering the precision with which the expectation was developed, determine the desired nature and extent of further audit testing.

RELATED ARTICLE: EXECUTIVE SUMMARY

* CURRENT GUIDANCE ON ANALYTICAL procedures is found in Statement on Auditing Standards no. 56, Analytical Procedures. An auditing standards board task force concluded amendments to the statement were not necessary but the practitioners did need additional help in applying the procedures.

* AN EXPECTATION--AN ESTIMATE OF an account balance based on an auditor's analysis of the account--should be the first step in an analytical procedure. Otherwise, the procedure is potentially biased by irrelevant information.

* TO FORM EXPECTATIONS, AUDITORS commonly use three broad types of analytical procedures, including trend analysis, ratio analysis and model-based procedures. There are two types of model-based procedures--reasonableness tests and regression analysis.

* PRECISION--THE AUDITOR'S MEASURE of the potential effectiveness of an analytical procedure--represents the degree of reliance that can be placed on the procedure and the audit assurance that can be derived from it. Auditors consider four key factors when assessing precision: the type of method (procedure) used to form the expectation, the reliability of the data, aggregation and the predictability of the account.

* OTHER FACTORS THAT DO NOT AFFECT an analytical procedure's precision but do affect the reliance and assurance derived from it include the nature of the assertion tested, the auditor's assessment of control risk for the entity and the results of the auditor's evaluation of significant differences.

EDWARD BLOCHER, PhD, is professor of accounting at the Kernan-Flagler School of Business, University of North Carolina at Chapel Hill, and a member of the American Institute of CPAs auditing standard's board's analytical procedures task force. GEORGE F. PATTERSON Jr., CPA, is a partner of Ernst & Young in Los Angeles. He is a former member of the ASB and chair of the analytical procedures task force.

RELATED ARTICLE: Key Factors Affecting the Precision of an Expectation

1. Methods (procedures) used to form an expectation. The auditor chooses among three methods of forming an expectation--trend analysis, ratio analysis and model-based. Model-based methods provide the most precise expectations.

2. The reliability of data used. The data used in an analytical procedure may consist of client financial data, operating data or external industry and economic data. The more accurate the data, the more reliance can then be placed on the procedure. The reliability of the financial and operating data will depend in part on the effectiveness of the client's internal control systems for this data, while the accuracy of external data will depend on the credibility of the data source. The auditor makes a judgment of the reliability of the data based on the process used to develop the data. For example, U.S. Census data are developed from known procedures, and therefore have a known reliability, but industry survey data may have very little or very high reliability, depending on the survey methods used.

3. Aggregation. An expectation has greater precision if the analysis is done at a relatively detailed account level. For example, expectations formed at the product line or plant level are more likely to be effective than those formed at the entity level since there are more factors influencing an entity-level balance. For the same reason, an expectation formed using monthly data is more precise than one formed using quarterly or annual data.

4. Predictability of the account. The development of an expectation for some accounts is likely to be more precise and effective than for others. SAS no. 56 points out, "As higher levels of assurance are desired from analytical procedures, more predictable relationships are required to develop the expectation." The statement says that in a stable environment, relationships usually are more predictable than in a dynamic or unstable environment. Relationships involving income statement accounts tend to be more predictable than those involving balance sheet accounts since the former represent transactions over a period of time; balance sheet accounts represent amounts at a point in time. Relationships involving transactions subject to management discretion can be less predictable. For example, it may be difficult to form a precise expectation for cash because of the influence of nonoperating factors and management discretion.
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Title Annotation:by auditors
Author:Patterson, George F., Jr.
Publication:Journal of Accountancy
Date:Feb 1, 1996
Words:2377
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