Review Engagements: Designing and Performing Effective Analytical Procedures
By: Russ Madray
September 20, 2017
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One of the most common review engagement issues noted in peer review is deficiencies in the performance and documentation of analytical procedures. Under AR-C section 90, Review of Financial Statements, when performing a review of financial statements, the accountant’s objective is to obtain limited assurance as a basis for reporting whether the accountant is aware of any material modifications that should be made to the financial statements for them to be in accordance with the applicable financial reporting framework, primarily through the performance of inquiry and analytical procedures.
Analytical procedures involve the study, comparison, and evaluation of relationships among financial and nonfinancial data at a point in time and the trend in those relationships over a period of time. A basic premise underlying analytical procedures is that plausible relationships among data may reasonably be expected to exist in the absence of known conditions to the contrary.
Analytical Procedure Process
Understanding financial and nonfinancial relationships is essential in planning and evaluating the results of analytical procedures and, generally, requires knowledge of the client and the industry in which the client operates. An understanding of the purposes of analytical procedures and the limitations of those procedures also is important. Accordingly, the identification of the relationships and types of data used, as well as conclusions reached when recorded amounts are compared with expectations, requires judgment.
The use of analytical procedures in a review engagement can be envisioned as a process that consists of three phases: developing expectations related to an account balance or financial relationship; comparison and evaluation of differences between expectations and the recorded amounts; and investigation, if necessary.
Phase 1: Develop an Expectation
Effective analytical procedures begin with the development of an expectation. The accountant uses knowledge of the client and its business to develop expectations for plausible relationships among financial and nonfinancial data.
Practice Note: The accountant cannot, under any circumstances, perform effective analytical procedures without first developing expectations related to the results of those analytical procedures.
To perform efficient and effective analytical procedures, the expectation should be sufficiently precise. The precision of an expectation is a measure of the closeness of the developed expectation to the actual amount (the actual amount is not necessarily the recorded amount). In other words, precision is the closeness of that expectation to the “correct” amount.
Practice Note: Expectations developed by the accountant in performing analytical procedures in connection with a review of financial statements may be less precise than those developed in an audit.
There are a variety of factors that influence the effectiveness of an analytical procedure:
· Accountant’s knowledge of accounting relationships
· Nature of the account or assertion
· Accountant’s knowledge of the client’s business and industry
· Number of factors impacting the expectation
· Reliability and other characteristics of the data used to develop the expectation
· Inherent precision of the expectation method used
Accountant’s Knowledge of Accounting Relationships
When developing expectations for an account balance or ratio, the accountant should consider the accounting relationships impacting that account or ratio. For example, there is a predictable relationship between inventory and cost of goods sold. An overstatement of inventory is usually related to an understatement of cost of goods sold. Similarly, there is a predictable relationship between accounts receivable and sales. An overstatement of sales is commonly related to an overstatement of accounts receivable. One potential cause of an improvement in the gross margin percentage is an overstatement of inventory. Another potential cause of an improvement in gross margin percentage is an overstatement of accounts receivable.
The accountant also should consider the relationships between ratios. For example, assume a client posts a fraudulent entry to debit accounts receivable and credit sales. In that circumstance, the gross margin percentage would increase and day’s sales in accounts receivable also would increase. The accountant should be alert for combinations of changes in ratios that might suggest a misstatement.
Nature of the Account or Assertion
Analytical procedures are based on relationships between data, for example, how this year compares with last and how amounts on a balance sheet relate to income and expense items. The more predictable the relationships are, the more precise the expectation will be. For example, expectations developed for income statement accounts tend to be more precise than expectations for balance sheet accounts because income statement relationships generally are more predictable. In addition, expectations formed under stable economic conditions (e.g., stable interest rates) or stable environmental factors (e.g., no regulatory changes) tend to be more precise relative to an unstable economy or environment.
Knowledge of the Client’s Business and Industry
One of the key considerations in developing expectations for analytical procedures is the accountant’s understanding of the client’s business and environment. Analytical procedures are a natural extension of the accountant’s understanding of the client’s business and add to his or her understanding because the key factors that influence the client’s business may be expected to affect the client's financial information. For example, when performing analytical procedures related to revenue, the accountant should understand factors such as the following:
· The nature of the client’s product
· The demand for the client’s product
· The market for the client’s product (e.g., is the market growing, stable, declining)
· The number of competitors and how they compete (e.g., do they compete on price or other product attributes)
· The client’s capacity to increase sales volume (e.g., what is the manufacturing capacity of the client’s facility, how much warehouse space is available)
As another example, factors the accountant should understand when developing expectations regarding payroll expense may include:
· The number, types, and mix of employees
· The method of compensation (e.g., salary, hourly rate, commissions, performance bonuses)
· Noncash compensation (e.g., stock options)
Number of Factors Impacting the Expectation
In a perfect world, there would be only one possible cause of a deviation from the accountant’s expectation—a misstatement. This would be achieved if the accountant identified every factor impacting the account or ratio and accurately incorporated each of those factors into the expectation. However, this is rarely possible. As a result, deviations from expectations generally have multiple possible explanations, some of which may be non-misstatement causes. If there are a large number of potential non-misstatement explanations for a particular analytical procedure, the use of that procedure may not be efficient. On the other hand, if the most likely causes for a significant deviation for a particular analytical procedure are misstatements, that analytical procedure may be very efficient.
Reliability and Other Characteristics of the Data
In forming an expectation, an accountant should consider two broad factors related to the characteristics of the data included in the account: the level of detail on which the accountant is able to base his or her expectation and the reliability of the data.
In general, the more disaggregated the data, the more precise the expectation. For example, the use of monthly instead of annual data tends to improve the precision of the expectation. Preparing an expectation by store or division also is more precise than an expectation based on consolidated data.
The more reliable the source of the data, the more precise the expectation. The following are factors related to the reliability of data that the accountant may consider in forming the expectation:
· Strength of the client’s accounting system. The stronger the client’s accounting system (including controls over the accounting system), the more reliable the data generated.
· External versus internal data and degree of independence. Data from more objective or independent sources are more reliable (e.g., third-party generated versus management generated).
· Nonfinancial versus financial data. The use of reliable nonfinancial data (e.g., store square footage or occupancy rates) improves the precision of the expectation.
· Data that has been subjected to other procedures versus data that has not been subjected to other procedures. The use of data that has been subjected to other procedures improves the precision of the expectation.
Inherent Precision of the Expectation Method Used
Expectations can be developed with methods as simple as using the prior-year sales balance (adjusted for expected changes) as the expectation for current year sales or as complex as multiple regression analysis that incorporates both financial (e.g., cost of goods sold) and nonfinancial data (e.g., store square footage) to predict retail sales. Determining which type of expectation method is appropriate is a matter of professional judgment; however, the inherent precision of the expectation method used is a consideration in developing the expectation.
Trend analysis is the analysis of changes in an account balance over time. Simple trends typically compare last year’s account balance to the current balance. More sophisticated trends encompass multiple time periods.
Trend analysis is most appropriate when the account or relationship is fairly predictable (e.g., sales in a stable environment). It is less effective when the client has experienced significant operating or accounting changes. The number of years used in the trend analysis is a function of the stability of operations. The more stable the operations over time, the more predictable the relations and the more appropriate the use of multiple time periods.
Trend analysis at an aggregate level (e.g., trend analysis of a client’s operating units on a consolidated basis) is relatively imprecise because a material misstatement is often small relative to the natural variation in an aggregate account balance. As a result, performing trend analysis on a disaggregated level (e.g., by segment, product, or location, and monthly or quarterly rather than on an annual basis) may improve precision.
CPEA Observation: In using trend analysis, it is important for the accountant to understand the volatility of the environment related to the accounts being tested. For example, using only the prior-year balance without considering whether it is the most appropriate expectation can lead to a bias toward accepting the current data as fairly stated, even when they are misstated.
Ratio analysis is the comparison of relationships between financial statement accounts (between two periods or over time), the comparison of an account with nonfinancial data (e.g., revenue per order or sales per square foot), or the comparison of relationships between firms in an industry (e.g., gross profit comparisons). Ratio analysis entails a comparison of interrelations between accounts, nonfinancial information, or both. Another example of ratio analysis (which is sometimes referred to as common size analysis) is the comparison of the ratio of shipping costs or other selling expenses to sales from the prior year with the current year ratio, or the comparison of shipping costs to sales with the ratio for a comparable firm in the same industry.
Ratio analysis is most appropriate when the relationship between accounts is fairly predictable and stable (e.g., the relationship between sales and accounts receivable). Ratio analysis can be more effective than trend analysis because comparisons between the balance sheet and income statement can often reveal unusual fluctuations that an analysis of the individual accounts would not reveal. Comparison of ratios with industry averages (or with comparable firms in the same industry) is most useful when operating factors are comparable.
Ratio analysis at an aggregate level (i.e., consolidated operating units or across product lines) is relatively imprecise because a material misstatement is often small relative to the natural variations in the ratios. Performing ratio analysis on a disaggregated level (e.g., by segment, product, or location) may improve precision.
Reasonableness testing is the analysis of account balances or changes in account balances within an accounting period that involves the development of an expectation based on financial data, nonfinancial data, or both. For example, an expectation for hotel revenues may be developed using the average occupancy rate, the average room rate for all rooms, or room rate by category or class of room. Also, using the number of employees hired and terminated, the timing of pay changes, and the effect of vacation and sick days, the model could predict the change in payroll expense from the previous year to the current balance within a fairly narrow dollar range.
In contrast to both trend and ratio analyses (which implicitly assume stable relationships), reasonableness tests use information to develop an explicit prediction of the account balance or relationship of interest. Reasonableness tests rely on the accountant’s knowledge of the relationships, including knowledge of the factors that affect the account balances. The accountant uses that knowledge to develop assumptions for each of the key factors (e.g., industry and economic factors) to estimate the account balance. A reasonableness test for sales could be explicitly formed by considering the number of units sold, the unit price by product line, different pricing structures, and an understanding of industry trends during the period. This is in contrast to an implicit trend expectation for sales based on last year’s sales. The latter expectation is appropriate only if there were no other factors affecting sales during the current year, which is not the usual situation.
Regression analysis is the use of statistical models to quantify the accountant’s expectation in dollar terms, with measurable risk and precision levels. Regression analysis attempts to determine the strength of the relationship between one dependent variable and a series of other changing variables (known as independent variables). The accountant applies regression analysis by selecting the dependent variable, for example, the amount of sales at each of the client’s locations. Next, the accountant selects the relevant independent variables, that is, those factors that the accountant knows from experience with the client and industry will be useful predictors of the dependent variable. For example, the level of inventory, the number of staff, or the square feet of floor space at each location might be correlated with the amount of sales. Regression analysis is not typically used in review engagements.
Relationship of the Methods to the Precision of the Expectation
Of the four types of expectation methods, trend analysis generally provides the least precision because this expectation method does not take into consideration changes in specific factors that affect the account (e.g., product mix). For example, using prior year’s sales (or an average of the time series) as the implicit expectation for current sales does not provide a precise expectation because it omits relevant information about additional products and changes in the economic environment. Regression analysis, on the other hand, provides potentially the highest level of precision because an explicit expectation is formed in which the relevant data can be incorporated in a model to predict current year sales.
The precision of ratio analysis and reasonableness testing typically falls somewhere in between that of trend analysis and regression analysis. However, reasonableness tests generally provide better precision because they involve the formation of explicit expectations similar to regression analysis. That is, reasonableness tests can employ multiple sources of data, both financial and nonfinancial, across time. Ratio analysis is similar to trend analysis in that it employs an implicit expectation. That is, when using a reasonableness test, the accountant may begin with the idea of predicting the balance, whereas for ratio analysis, the expectation formation process is implicit—as the ratio is compared with budget, industry, or other relevant benchmarks.
Phase 2: Compare and Evaluate
The next phase in performing analytical procedures involves comparing the expected amount to the recorded amount and calculating the fluctuation (i.e., the difference). If the fluctuation is not significant, the accountant accepts the recorded amount. If the fluctuation is significant, the accountant investigates the cause or causes of the unexpected fluctuation.
Comparing recorded amounts to expectations consists of the accountant developing guidelines on what defines a significant deviation. For example, a client’s gross profit percentage might be 47 percent this year compared with 45 percent last year, and the accountant has the expectation that, in general, there is no reason to expect a change in the gross profit percentage. In this case, the 2 percentage-point difference may or may not be considered significant, depending on the level of precision of the analysis.
In determining whether a difference between an actual result and an expectation is significant, the accountant relates the item being analyzed to materiality thresholds established for the engagement. For example, in the case of the gross profit percentage difference of 2 percentage points, the accountant may have determined that an item is potentially material if operating income could be misstated by 7 percent or current assets could be misstated by 10 percent. Initially, the 2 percentage point difference may or may not be treated as an error. If it is determined to be an error, the accountant converts it to a dollar amount and relates it to (a) operating income (cost of goods sold understated and tax expense understated) and (b) current assets (ending inventories overstated).
CPEA Observation: This materiality analysis is preliminary and is performed to provide the accountant with some direction. If the assumed error is clearly immaterial, the accountant generally would not investigate the matter further. If the assumed error is material or approaches the materiality threshold, the accountant should investigate such differences. In the preceding example of the unexpected increase in the gross profit percentage, the accountant may decide to inquire further about the client’s inventory count procedures or perform further analytics on the costing for the inventory summarization.
Practice Note: It’s also important to note that in some circumstances the cause of a difference may be due to a lack of precision in the development of the expectation. In other words, the accountant may not have considered one or more factors that affect the balance or ratio.
Phase 3: Investigation
If analytical procedures identify fluctuations or relationships that are inconsistent with other relevant information or that differ from expected values by a significant amount, the accountant should investigate these differences by inquiring of management and performing other review procedures if considered necessary in the circumstances. Review evidence relevant to management’s responses may be obtained by evaluating those responses, taking into account the accountant’s understanding of the client and its environment, along with other review evidence obtained during the course of the review. Although the accountant is not required to corroborate management’s responses with other evidence, the accountant may need to perform other procedures when, for example, management is unable to provide an explanation or the explanation, together with review evidence obtained relevant to management’s response, is not considered adequate.
In addition, the accountant should be mindful of the following judgment biases and traps when discussing with management:
· The accountant may fail to consider alternative explanations. When the explanation provided by management is not the actual cause of the misstatement and the accountant does not recognize that the client explanation is erroneous, the analytical procedure will be ineffective.
· The accountant may get “framed.” Management generally will provide a non-misstatement explanation. This may “frame” the accountant into only considering non-misstatement causes. As a result, the accountant may fail to consider potential misstatement causes for the unexpected fluctuation.
· The accountant may be prone to the confirmation bias and search for, and place more weight on, information to confirm management’s explanation and ignore, or place less weight on, information that would disconfirm management’s explanation.
Accountants often perform analytical procedures “mechanically” and surmise that more ratios lead to a more effective engagement. Before performing an analytical procedure, the accountant should ask what type of useful evidence this procedure will provide. Improved effectiveness and efficiency can be achieved in a review engagement by using the following approach:
1. Identify account balances or classes of transactions to which other accounting services (e.g., bookkeeping or payroll services) have been applied. Consider the evidence already obtained and whether any material errors are likely to remain. If current and relevant review evidence for those account balances has been obtained from other services to provide limited assurance that they are not materially misstated, do not apply analytical procedures to them.
2. Identify immaterial account balances or classes of transactions. Apply no analytical procedures to them.
3. For the remaining account balances, develop expectations for them. For larger balances in which the accountant believes there may be higher risks of material misstatement, the accountant may want to design and perform analytical procedures that are more precise in order to obtain review evidence that the account is not materially misstated. For smaller balances or when the risks of material misstatement are lower, the accountant may design and perform analytical procedures that are less precise.
4. Consider how close the recorded account balance or class of transaction comes to the expectation. If the differences are within an acceptable range, based on professional judgment, then no additional evidence is needed.
5. If the differences are large, material errors could exist. As a result, inquire about valid business reasons for the differences. If the results of inquiry are plausible and agree with other evidence, then the accountant may conclude that no additional evidence is necessary in the circumstance.
Practice Note: Remember that analytical procedures often will provide a basis for additional inquiries because the procedures may identify other significant matters affecting the financial statements that might otherwise not have been apparent.
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