Analyzing An Audit Population Via Either Excel Pivot Tables and/or R Language Cluster Analysis Free

“Analytical review procedures” were described in 1972 via Statement on Auditing Procedures No. 54 as “analytical review of significant ratios and trends and resulting investigation of unusual fluctuations and questionable items” (AICPA 1972). Forty years later, the audit use of analytical procedures continued to be dominated by basic analyses such as comparison of current period to prior periods and ratio analysis (Trompeter and Wright 2010). The audit profession's adoption of various technologies has substantially lagged behind utilization by management (Dai and Vasarhelyi 2016). One of the reasons for the lag in the adoption of audit data analytics are firm-level decisions about whether to invest resources. Another reason is auditor specific: the steep learning curve for techniques and software (Gray and Debreceny 2014).

At the current time, both U.S. and international auditing standards require that auditors systematically address the risk of material misstatements arising from either fraud or error (PCAOB 2010b; IAASB 2019). The large number of fraud detection-related audit failures over recent decades suggests that auditors have not been particularly successful in addressing the risk of material misstatements. The CEOs of the large international audit networks state that there is an “expectation gap” when it comes to material fraud and the ability of auditors to uncover it at reasonable cost (DiPiazza et al. 2006, 12).

In recent years, there has been an increasing recognition that audit data analytics hold great promise for the auditing profession. Audit data analytics has been recently defined as “the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization for the purpose of planning or performing an audit” (Byrnes, Criste, Stewart, and Vasarhelyi 2015, 92).

A recent survey indicates that executives think the audit profession has and will continue to adopt new technologies and approaches. “The audit has become digitized over the past several years, as audit firms and their clients are able to share higher-quality data. The age of smart audits is dawning with advanced technologies enabling auditors to deliver enhanced audit quality” (KPMG 2018, 3).

Both U.S. and international auditing standards specify a “risk-driven” audit where auditors identify risks, evaluate their significance, and potentially gather evidence to see if the risks have been realized. The PCAOB Auditing Standard No. 12 states, “The objective of the auditor is to identify and appropriately assess the risks of material misstatement, thereby providing a basis for designing and implementing responses to the risks of material misstatements” (PCAOB 2010a).

An anomaly is a data point (or group of data points) that poses a risk that it may be significantly different from the overall population. There are a wide variety of anomaly detection techniques. Anomaly detection techniques can vary from supervised, which requires a training data set, to unsupervised, which does not require a training data set. One common anomaly detection technique is cluster analysis, which is an approach that can identify groups within a dataset or be used to identify suspicious transactions or observations.

K-means clustering is a simple, well-known clustering algorithm that is less computer intensive than other clustering approaches, thus preferable for large datasets (Thiprungsri and Vasarhelyi 2011). Cluster analysis is a bottom-up (undirected) technique that seeks to find relationships between any variables in the population. The population clusters are not pre-selected. “The auditor must interpret the underlying meaning of the sub-groupings in clustering using their knowledge of the accounting model and the business models employed by the client” (Gray and Debreceny 2014, 361). Clustering has been identified as one of the “most promising” unsupervised audit methods (Appelbaum, Kogan, and Vasarhelyi 2017, 10).

Cluster analysis could potentially identify material misstatements because the misstatements may have characteristics that differentiate them from the rest of the population. A few possible unique characteristics of fraudulent transactions could be:

• Transaction originated at a remote or unusual location

• Transaction occurred outside normal business hours

• Amount of transaction is either higher or lower than normal

Cluster analysis can promote “critical thinking” by students regarding fraud/material misstatement possibilities and how to find them. Students have to make a judgment as to how many clusters to develop, a judgment that depends on the audit purpose of the analyses. They then have to hypothesize about what is in the clusters and, therefore, what type of testing of clusters is appropriate. They can test their hypothesis by examining some of the cluster items before setting up testing.

Regression analysis is a supervised learning technique for which auditing standards require the auditor to develop a plausible model in order to identify outliers, basically a deductive approach. The requirement for a plausible model limits auditors to relationships they have an understanding of. As an unsupervised learning technique, and inductive approach, cluster analysis provides a useful counterpoint to teaching regression analysis as an audit technique, since no plausible model is required before applying the technique.

This case was developed to provide a mechanism to promote students' critical thinking about a potential fraud/material misstatement case setting. The case can be completed via either Excel Pivot Tables and/or R language K-means cluster analysis. Excel Pivot Tables provide a relatively simple approach to analyzing the data based on a deductive analysis of variable relationships. R language K-means cluster analysis provides an opportunity to show students an alternative to traditional approaches such as segmenting a population based on known variables or applying either sequential or simple random sampling of a large population.

Pistil Storage Inc. is a fully integrated Delaware corporation that was formed on January 1, 1999 to own, operate, manage, acquire, develop, and redevelop professionally managed self-storage properties (“stores”). Common stock is traded on the New York Stock Exchange under the symbol “PIS.”

“Self-storage” lets a person or business store things that the person or business does not have space for at a storage facility (“store”). Storage facilities rent storage space (typically called “storage units” or “storage lockers”) to tenants on a short-term basis, sometimes month-to-month, but often on one-year leases. Larger storage facilities may offer access 24-hours a day and seven days a week. Storage facilities typically have a variety of security features to protect the storage units. Many facilities require that the tenant secure their unit with their own lock and key so that only the tenant has actual access to the unit. Self-storage is a rapidly growing industry, as in addition to individuals storing things, many businesses rent storage units to store inventory or equipment.

The self-storage industry is characterized by fragmented ownership. The top ten self-storage companies in the United States operated approximately 20.1 percent of the total U.S. stores, and the top 50 self-storage companies operated approximately 30.2 percent of the total U.S. stores as of December 31, 2017.

Pistil Storage owned and operated 1,531 stores as of December 31, 2017. These stores are located in 44 states and contain approximately 37 million square feet of net rentable space in approximately 306,000 storage units, and currently serve a customer base of approximately 250,000 tenants. Recorded revenue for 2017 was $499,200,823.

The company operates throughout the United States but divides the country into five distinct districts through a planned expansion plan that seeks to establish approximately the same number of stores in each district. Each of the five districts is managed by a district manager. The district managers receive a base salary plus a small bonus based on meeting or exceeding budget targets. The districts and number of stores in each district as of December 31, 2017 are shown in Exhibit 1.

Many of the stores are clustered in and around large cities in each district. As many as 30 stores are located in and around individual larger cities. This makes it easier for the district managers to individually visit each store manager at least once per quarter, as per company policy. Individual store managers may be responsible for all the stores around a city, as many as 30 stores.

Stores offer month-to-month storage space rental for personal or business use and are a cost-effective and flexible storage alternative. Operating costs are minimized by requiring customers to sign an electronic contract and set up all rentals with electronic payment via either credit or debit card. Payments are made at time of rental contract signing based on a prorated charge for remaining days in the rental month. Full charges apply on the first day of each succeeding month. Customers can cancel their contracts by emptying their storage unit and going online to cancel the electronic contract prior to the first day of the month when the next billing occurs. Per the electronic contract, the last rental month is a full month's charge, as there is no prorated charge based on days occupied for the last rental month.

Tenants rent fully-enclosed spaces that can vary in size according to their specific needs and to which they have unlimited, exclusive access. Tenants have the responsibility for moving their items into and out of their units. Stores have on-site managers who, with their employees, supervise and run the day-to-day operations, providing tenants with assistance as needed. Self-storage unit sizes vary from 25 square feet to 200 square feet, with interior heights of 8 feet to 12 feet. The stores are designed with either 100, 200, or 300 units with a standard mix of sizes. The standard unit mix with pricing as of December 31, 2017 is shown in Exhibit 2.

You are a staff auditor working for the Pistil Storage Inc.'s independent CPA firm, Jacobsen & Jacobsen, which is conducting its first annual audit of the company. Recent prior year audits were done by a “Big 4” audit firm that was dropped due to a poor PCAOB quality control review rating. The date is February 2018 and the firm is working hard on year-end substantive testing.

One aspect of the audit is to examine the company's analysis of calls received via the toll-free anonymous whistleblower hotline. This is normally done earlier in the audit but this audit work was delayed due to late approval of the Jacobsen & Jacobsen firm to conduct the audit. A review of the hotline activity for the year reveals two hotline calls were received that are concerning. The calls were analyzed by Pistil Storage's internal audit department but no action was taken due to the vague nature of the calls, the fact that no phone numbers were left by the callers, and there is no way to trace the originator of the calls due to the privacy setup on the hotline.

The first concerning recorded call occurred in February 2017 and was transcribed as, “Hey dudes, I had a hard time finding a number for Pistil Storage, but found this number on your website. Why does my 2016 W-2 show seven months of salary during 2016 when I only worked for five months? I am not going to report wages for money I did not receive and I am not going to pay back my unemployment compensation for those two months. At least you got the address right. Call me back about this.”

The second concerning recorded call occurred in June 2017 and was, “Hello, I just thought you should know that your manager is giving a free month's unit rental in exchange for paying the first month in cash. That is a pain for people like me who don't carry much cash. It seems odd since automatic billing via a credit or debit card is required for the remainder of the contract months. Just saying, because I am mad that I had to go to a bank teller to get the cash so I would qualify for the discount.”

Due to your graduation from a prestigious business school that has a reputation for teaching cutting-edge audit theory and practice, the partner in charge of the audit has selected you to apply audit data analytics to see if the company data provide any signal about possible problems in the areas mentioned in the calls. She stated, “This is a shot-in-the-dark assignment, but you are one of the best and brightest auditors I know and I am sure you will figure out if we have any problems to deal with.”

Your firm's information technology specialist prepared a data file for you that reflects the 2017 revenue data and various operation metrics for the 1,531 stores. The name of the data file is “Pistil Storage Data 1531 items.csv.” A listing of the first five records in this data file is shown in Exhibit 3.

1. Read the following publications to gain a better understanding of Excel Pivot Tables and cluster analysis possibilities in auditing, company “hotline” responsibilities, and auditor revenue recognition responsibilities. Watching the video is only necessary if your instructor assigns Excel Pivot Tables as part of the case analysis. Be prepared to discuss the suggested readings in class:

   • Six minute video on Excel Pivot Table basics, available at: https://www.youtube.com/watch?v=qu-AK0Hv0b4

   • Thiprungsri, S., and M. Vasarhelyi. 2011. Cluster analysis for anomaly detection in accounting data: An audit approach. The International Journal of Digital Accounting Research 11: 69–84.

   • Findlaw. Sarbanes-Oxley “hotline” procedures: Who should be doing the listening? Available at: https://corporate.findlaw.com/litigation-disputes/sarbanes-oxley-hotline-procedures-who-should-be-doing-the.html

   • PCAOB. 2017. Matters related to auditing revenue from contracts with customers. Staff Audit Practice Alert No. 15. October 5, 2017. (20 pages)

   • PCAOB. 2012. Maintaining and applying professional skepticism in audits. Staff Audit Practice Alert No. 10. December 4, 2012. (15 pages)

2. Answer the following questions:

   • What is a whistleblower hotline and why do companies have them?

   • What is the auditor's responsibility with respect to fraud?

   • What is the auditor's responsibility with respect to revenue recognition?

   • What is the auditor's responsibility with regard to the two hotline calls?

   • How does professional skepticism relate to these responsibilities?

   • Assume a population of 1,500 revenue locations has 30 locations for which there exist material misstatements. What is the probability of detecting a material misstatement if a sample of one location is randomly selected from the 1,500 locations? Sample selection of 30 locations?

   • Assume the previous population has been segmented into five equal size subpopulations and one of the subpopulations has been identified as high-risk for the material misstatements. What is the probability of detecting a material misstatement if a sample of 30 locations is randomly selected from the high-risk subpopulation?

Although cluster analysis statistically develops population groupings (clusters) without pre-specification of the groupings, Pivot Table analysis requires the auditor to think about and compute informative data groupings. This may involve creating new variables or transforming variables. Auditors must think about what variable relationships may be informative.

1. Install a recent version of Excel on your computer.

2. Load the data file into Excel via the menu commands: File → Open → “Pistil Storage Data 1531 Items”.

3. One possible method of analysis would be to segment the data by the five store districts indicated in the case narrative. The data file has location numbers for the individual stores that indicate which store district each store is located in. To analyze results by store district it will be necessary to recode the locations by their district numbers, 1 through 5, and put this code in a new column. One simple way this can be easily accomplished is by using a logical “If” command from the Excel formula menu bar. For example, if the specific location is less than 401 create a “1” for that store as it is in district 1. The recoding for all the stores can be accomplished by going to an empty column and inserting the following nested “If” command and pasting it down the column for all items in the file (this assumes row B2 has store locations):

   =IF(B2<401,1,IF(B2<801,2,IF(B2<1201,3,IF(B2<1601,4,5))))

   The previous command will convert all specific locations into district numbers, 1 through 5. This column should be labeled “District.”

4. A blank Pivot Table can then be created by going to the menu bar and selecting Insert → Pivot Table → OK.

5. Drag the Pivot Table field “District” on the right hand side of the sheet down to the area below it, labeled “Rows.”

6. Drag the Pivot Table field “Revenue Per Unit” on the right hand side of the sheet down to the area below it, labeled “Values.”

7. The Excel sheet itself will then display the Pivot Table “Sum of Revenue Per Unit” by “Row Labels,” which are the districts.

8. The total revenue by district is not particularly informative so it needs to be changed to “Average Revenue Per Unit.” This can be easily accomplished by going to the “Value” box at the bottom right hand side of the sheet and clicking on the down arrow to the right of “Sum of Revenue Per Unit,” selecting “Value Field Settings” from the menu that appears, and then setting the values to “Average.” This will now have created a Pivot Table with “Average Revenue Per Unit” by store districts 1 through 5. Analysis of this table will indicate if any of the five store districts had unusually high or low “Average Revenue Per Unit.”

9. Follow the previous instructions and create other Pivot Tables using groupings of different variable combinations that may provide insightful audit information.

To perform K-means cluster analysis on the data file, students must download and install either RStudio Desktop (free) or RStudio Server integrated development environment to run the detailed R language commands. Once students have downloaded the free software, they should enter in the Command window the detailed R code necessary to perform the case analyses. The following numbered lines provide suggested basic R commands that students will have to write in an executable format before RStudio will execute them. This list is provided to help students complete the case while reducing the amount of time it will take them:

1. Load the data file either by using the “Import Dataset” tab in the RStudio Environment window or execute a R “read.csv” command to download the case data file from the locations provided by your course instructor into RStudio.

2. Execute the R “str” command to create a data frame and allow you to determine that your file has the correct number of records, variable names, and individual data types.

3. Execute the R “sum” command to sum total revenue in the file and see if it agrees with the recorded book figure.

4. Execute the R “summary” command on the data file in order to obtain basic statistical measures on the case variables so you will better understand the nature of the individual variables.

5. Execute the R “hist” command on the individual variables to create a histogram of each.

6. Install and library the “cluster” package to load it into the working library.

7. Install and library the “factoextra” and “ggplot2” packages to load them into your working library.

8. Execute the R “plot” command for different pairs of the individual variables to create scatter plots of their relationship.

9. K-means clustering randomly starts, so use the R command “set.seed(123)” to start the random number generator at the same point each time. This will allow you to recreate the same work.

10. Execute the R “scale” command to create a scaled data file (normalized) containing the normalized data to facilitate your cluster analysis.

11. Use the R function “fviz_nbclust()” [in “factoextra” package] to create a plot estimating the optimal number of clusters via the bend (knee) in the plot.

12. Use the R function “kmeans” to compute cluster statistics for three to six clusters in order to view how the within-cluster sum of squares percentage changes as the clusters increase. Be sure to use the “nstart=25” option for kmeans so that you get stable solutions.

13. Use the R function “print” to examine the various cluster means.

14. Use the R function “fviz_silhouette()” [in “factoextra” package] to create a silhouette plot for what you determine to be the optimal number of clusters per the previous bend plot. Examine the plot for negative coefficients as these indicate that observations are not in the right cluster.

15. After considering all evidence from the previous steps, make a judgmental determination of the optimum number of clusters for detailed analysis.

16. Execute the R function “fviz_cluster()” [in “factoextra” package] to generate a cluster plot for your optimal number of clusters.

17. Use the R function “aggregate” to assign clusters to unscaled data and compute cluster means for the unscaled data.

18. Use the R function “cbind” to attach cluster designation derived from the scaled data to the unscaled data.

19. Use the R function “summary” to print out the cluster means and statistics for the unscaled data.

20. Visually analyze the cluster means for the unscaled data to see if any clusters have unusually high or low values for the individual variables.

21. Use the R function “subset” to create cluster subsets of the overall data.

22. Use the R function “summary” to view summary statistics for each of your subsets (clusters).

23. Use the summary information to compute unit revenue in each cluster by dividing mean revenue by mean units.

24. Use the R function “cut” to examine the distribution of districts in the individual clusters.

25. Perform any other R analyses you deem appropriate.

1. For the Excel Pivot Table analysis, answer the following questions:

   • What factors might create data differences for the individual districts?

   • Did you find any significant differences in revenues or expenses for the Pivot Tables you created? What was the nature of these differences?

   • What is your recommendation to the audit partner about the best way to proceed on the two hotline calls? What other alternatives are there?

   • What risks might remain after implementing your recommendation about the best way to proceed?

2. For the R Language K-means cluster analysis, answer the following questions:

   • How many significant clusters are there? What led you to this conclusion?

   • Did you find any significant differences in revenues or expenses for the clusters you identified? What was the nature of these differences?

   • What is your recommendation to the audit partner about the best way to proceed on the two hotline calls? What other alternatives are there?

   • What risks might remain after implementing your recommendation about the best way to proceed?

Pistil Storage Data 1531 Items.csv: http://dx.doi.org/10.2308/CIIA-2019-502.s01

1. Students/professionals develop critical thinking skills in an audit setting.

2. Students/professionals understand planning issues in the substantive testing phase of an audit.

3. Students/professionals apply Excel Pivot Tables and/or cluster analysis in an audit context.

4. Students/professionals understand how cluster analysis can provide a basis for setting up improved population stratification and sample selections.

This case is applicable to auditing courses where there is a desire to utilize audit data analytics in a hands-on mode for active learning. It might also be useful for an in-house CPE course for auditors. The detailed solutions provided should make it easy for instructors to take advantage of the case.

Excel is a widely used software platform. The Pivot Table function enables a significant degree of relatively easy data manipulation. Auditors must think about the relationships between the data elements in order to develop useful Pivot Tables.

R is a well-developed, simple, widely used open-source programming language for statistical computing and graphical techniques. It runs in the free RStudio integrated development environment. R is an interpreted language that can execute written code on a line-by-line basis. RStudio includes a user-friendly desktop console, syntax highlighting editor as well as plotting, debugging, and workspace management tools. There are thousands of previously developed R applications freely available on the internet.

For the case to proceed with maximum student learning and minimum student frustration, students assigned this case would ideally have prior lectures and assignments introducing them to Excel Pivot Tables, the R programming language, and the RStudio environment. They also should have been introduced to cluster analysis and the R commands for performing K-means cluster analysis.

The simplest possible approach to the case is to only have the students do the Excel Pivot Table analysis. This significantly lowers the time and effort that students must spend on the case and still provides them the opportunity to identify audit areas requiring further investigation. It also provides the opportunity for them to think about how the audit should progress based on the data analysis.

A more complicated approach would involve doing both the Excel Pivot Tables and the R commands. For this approach, the case explains how to create Pivot Tables and names the R commands that the students must execute to obtain a base case solution. It does not provide the students with the detailed R commands. The students will need to identify the correct format and details for the R commands before they can execute the commands. This approach was taken because the students for which the case was developed had R language and RStudio instruction prior to commencing the case and the case provided additional practice in those mediums.

An alternative approach to the case for students who have no or minimal R language and RStudio background is to provide the students with the detailed R commands provided in the case solution. The students then just have to cut and paste these commands into RStudio and then execute them to obtain the base case solution. This approach enables students to obtain a case solution with a minimum of prior training and frustration. This approach could also be used for a live class, hands-on, case solution by instructor and students. The instructor could discuss each command as the instructor and students simultaneously execute the individual commands, then discuss the outputs received.

It is also possible to ignore both Excel Pivot Tables and the R language approach and assign the case using other software, such as SPSS®, SAS/STAT®, or Tableau© if the students have previously been exposed to such software. That would eliminate the need to introduce the students to Excel Pivot Tables and/or R programming language and RStudio.

The R language and RStudio version of the case was assigned to students in a master's level team-taught auditing course titled “Digital Auditing,” which focused on audit data analytics at the Norwegian School of Economics during Spring Semester 2018 and Spring Semester 2019. Four different professors taught significant segments of the course. There were also a number of technical guest speakers from the public accounting profession. The course was an elective during 2018 but was changed to mandatory in 2019. Class size was 38 graduate students in 2018 and 95 graduate students in 2019. The 2018 and 2019 face-to-face case coverage by the case author occurred on different days at two different locations in Norway. The students in one of the locations were primarily residential students who were completing a master's degree on top of an undergraduate degree and had little accounting/auditing experience. The students at the second location were primarily full-time experienced accounting/audit professionals working for large international accounting firms. That was functionally equivalent to two different populations of students with each population using the case in both 2018 and 2019.

In this course, the author lectured about and illustrated R solutions for cases on:

• Cluster analysis

• Decision trees

• Regression

• Neural networks

At the conclusion of the author's coverage of the previous four techniques, the students were assigned four audit cases that involved using the previous techniques in different audit settings, one of which was the cluster case presented in this article.

Students self-assigned to teams of 3–4 to work the four cases. Teams were randomly selected in each semester to present their case solution to the class. During both semesters, the presenting teams for the cluster analysis case correctly analyzed the data, identified the problem areas, answered the questions, and recommended appropriate follow-up audit testing. There were a total of four different student presentations of the case due to the case coverage in two different locations each year over the two-year period. The student class presentations included extensive class discussions designed to ensure that students understood both the cluster analysis concepts along with the relevant audit relationships.

Student feedback was provided via direct comments by students to the course instructor both during and subsequent to the case presentations. Since the course was a new offering, the department head also conducted live interviews with selected students at the conclusion of each course. All students also had an opportunity to complete end-of-the-semester course assessments.

Student feedback during the first course offering in 2018 indicated that students liked the case and its hands-on nature, but some students struggled with using R and finding reasonable solutions for the cluster case. Based on this feedback, the 2019 version of the course included significant additional R training before the students were assigned the case as well as more detailed suggestions for how to proceed in R with the cluster analysis.

Student feedback during 2019 was much more positive with students indicating they experienced few problems with using R and had gained a valuable understanding about the audit possibilities for cluster analysis. The end of the semester exam for the 2019 course included a cluster analysis case that was 25 percent of the course grade. This case placed students in the role of an auditor in a large international accounting firm that was auditing a large group life insurance company. The students were provided company and audit background information, the layout for a claims file with more than 10,000 claims, and thirteen R printouts of results from cluster analysis of the file. They then had to answer five questions related to risks, the cluster analyses, and recommendations for how to proceed with the audit. The extremely good overall student success on the exam case indicated that they had appropriately mastered concepts related to applying and understanding cluster analysis in an audit setting.

The case author was very surprised that even though they had prior R instruction, the first time the case was utilized, due to an apparent lack of sufficient R hands-on application, some of the student teams had trouble with the sequence of steps needed to obtain a case solution and this prevented two teams from obtaining a reasonable case solution. The second offering of the case provided students with step-by-step R general commands but not the detailed code needed to run the commands. This enabled all the teams to achieve a reasonable case solution, which, in turn, facilitated critical thinking and case discussions. Several teams even successfully explored using other techniques to identify the population segments that were troublesome.

Another issue was getting students to apply their knowledge of statistical concepts in the context of audit planning. For example, they knew what population stratification was but had only conceptualized it in a sampling context as a way to test different strata at different frequencies. They had not thought of it as a way to potentially reduce audit testing via applying different types of testing for different strata according to their risk characteristics. Feedback indicated that the case discussions about audit planning were very helpful to students' understanding.

This case includes a realistic company setting and live data file that can be assigned to help students further their knowledge of auditing and audit data analytics. The case questions help students frame the critical audit issues. The live data file allows students to use either Excel Pivot Tables and/or K-means cluster analysis, a widely used unsupervised learning approach, to identify population subsets for a more efficient approach to detailed audit testing in response to fraud/material misstatement risks. The lively and spirited class discussions surrounding the student case presentations confirmed the case effectiveness in promoting critical thinking and a deeper understanding of auditing.

Teaching Notes and the Student Version of the Case are available only to non-student-member subscribers to Current Issues in Auditing through the American Accounting Association's electronic publications system at http://meridian.allenpress.com. Non-student-member subscribers should use their usernames and passwords for entry into the system where the Teaching Notes can be reviewed and printed. The “Student Version of the Case” is available as a supplemental file that is posted with the Teaching Notes. Please do not make the Teaching Notes available to students or post them on websites.

If you are a non-student-member of AAA with a subscription to Current Issues in Auditing and have any trouble accessing this material, then please contact the AAA headquarters office at [email protected] or (941) 921-7747.