ABSTRACT:Recent changes in the accounting profession require students to enter the workforce with technical and critical-thinking skills using large datasets. In an audit setting, an important skill is the ability to identify anomalies and risk factors in the client's data. This instructional case provides students with experience using visualization to identify anomalous transactions for further substantive testing based on relationships between financial data (revenues) and nonfinancial data (weather patterns). Students must also create a memo for the workpaper files that documents their findings, including recommendations for the audit team to select specific sales transactions for substantive testing. Aside from gaining experience with Tableau visualization software, this case will improve students' problem-solving and analytical skills by encouraging them to work independently and to break a complex problem into manageable pieces. This case is most applicable for implementation in undergraduate or graduate auditing or internal auditing courses.

Keywords:audit risks; Big Data; data analytics; data visualization; identifying anomalies; revenues.THE CASE

Company Background

You were recently promoted to audit senior at your firm, Aoife & Josephine LLP, and one of your primary clients is Souper Bowl Inc. Souper Bowl (''the company'') is a privately held business headquartered in Maine, and has a fiscal year-end of December 31. The company has been in business for nine years and prides itself on offering creative soups at a reasonable price and that are made with locally sourced ingredients. The most popular soups include sweet potato corn chowder, curried root vegetable and lentil, and maple-roasted butternut squash. Souper Bowl typically experiences increased sales during winter months since soup hits the spot on a cold and snowy day. To further encourage sales on days when customers often avoid venturing outside, the company provides a delivery service and guarantees that soup can be delivered to anyone no matter what the weather. The company found this strategy to be particularly successful in 2015 when New England (including Maine) experienced record snowfall during February and March.

Souper Bowl sells their soup at several restaurant locations throughout Maine. The company employs three managers that direct the day-to-day operations for a group of stores that are organized by approximate geographic region: northern Maine (Store Type 1), mid-Maine (Store Type 2), and coastal Maine (Store Type 3). Appendix A provides a map of these store locations. Each manager knows their local market well and has the flexibility to advertise and offer promotions with the overall goal of increasing sales year over year. If total sales at the end of the year exceed total sales from the prior year for that manager's set of locations (i.e.,''Store Type''), then the manager earns a monetary bonus from the company.

We appreciate the helpful and constructive feedback from Valaria P. Vendrzyk (editor), Ronald F. Premuroso (associate editor), and two anonymous reviewers. We are also grateful to the 2016 Deloitte Foundation Trueblood Seminar for providing us with the inspiration for this case study. Lauren M. Cunningham received financial support from the PwC INQuires Grant for the purpose of integrating technology into accounting programs.

Supplemental material can be accessed by clicking the link in Appendix C. Editor's note: Accepted by Valaria P. Vendrzyk.

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Submitted: December 2017 Accepted: May 2018 Published Online: May 2018

34Cunningham and Stein

An audit of the company is required to comply with debt covenants related to a large bank loan that the company entered into when it began operations. Specifically, Souper Bowl must provide audited annual financial statements to the bank within 90 days of the fiscal year-end. The company must also provide unaudited quarterly financial statements to the bank within 45 days of the end of each quarter. The debt contract includes a financial covenant that requires pre-tax income in each quarter to be greater than zero. If not met, the bank has multiple remedies at its disposal, including calling the loan such that the entire balance is due immediately, seizing the company's assets that are posted as collateral, or providing a waiver for the violation. Souper Bowl's net income for the year ended December 31, 2016 is $468,810, while net income for the prior year ended December 31, 2015 was $825,229.

Auditing Revenues

As part of your new role as audit senior, you will be performing a large portion of the planning and testing of sales for the 2016 audit of Souper Bowl. AU-C Section 240.26 states that''when identifying and assessing the risks of material misstatement due to fraud, the auditor should, based on a presumption that risks of fraud exist in revenue recognition, evaluate which types of revenue, revenue transactions, or assertions give rise to such risks.''During planning for the audit, the partner and manager determined that the following three management assertions represent significant risks for revenues:

(1) recorded sales occurred;

(2) sales are accurately recorded; and

(3) sales are recorded in the proper period.

In prior years, the audit approach relied on random sampling to test revenues. However, the partner wanted to develop more focused procedures in the current year to hone in on potentially riskier sales transactions. As a result, the plan is to perform disaggregated sales analytics to identify unusual trends in the daily sales data with the goal of identifying sales on specific days at specific store locations that should be subjected to substantive testing due to heightened risks. The remainder of the population would then be sampled using a random sampling approach.1

Based on your experience from prior audits, you know that Souper Bowl's daily sales fluctuate with temperature and snow accumulation. To perform your revenue analytics, you request a file from the client that includes daily sales by store location for both 2016 (current year) and 2015 (prior year). You also retrieve daily weather data from the National Oceanic and Atmospheric Administration's (NOAA) website for the weather centers closest to Souper Bowl's store locations. Total revenue for the current year ended December 31, 2016 is $18.8 million, while total revenue for the prior year ended December 31, 2015 was $19.1 million. The audit team's workpapers include the following lead sheet for revenue testing, and the total balances for each year agree to the trial balance and the company's draft financial statements for 2016.

Souper Bowl Inc. Revenue Lead Sheet December 31, 2016

Revenue, Store Type 1 Revenue, Store Type 2 Revenue, Store Type 3

Your manager stated that Tableau is a popular data visualization tool that your firm recently adopted and she instructed that you learn how to use it to perform these sales analytics. Since she is busy overseeing the planning and testing of other audit areas, she wants you to take the first pass and then document your results in a memo for her review. The manager wants you to provide thoughtful analyses and a thorough exploration of the possible relationships in the data. You are eager to impress her with your work, especially following your recent promotion to senior.

Requirement

1. Use the daily sales by location as provided by the client (2016 and 2015) and the weather data from NOAA to perform disaggregated sales analytics in Tableau. Your goal is to develop visualizations that identify potential outliers in the 2016 daily sales data related to the significant risks identified by the partner and manager. Using the memo template in Appendix B, document your analyses and conclusions as to the specific daily sales from certain locations that you recommend selecting for focused substantive testing.

Note: Your conclusion needs to be precise enough to pull specific transactionsfor example, you would not list the''month of March''in store 1010 because this would result in too many observations to feasibly test. Also, you should not recommend testing observations from 2015. Your engagement team completed that audit in the prior yearinstead, you are using 2015 data as a component of your baseline prediction for 2016.