Accounting Horizons American Accounting Association
Vol. 29, No. 2 DOI: 10.2308/acch-51068
2015
pp.

423

–429

Big Data Analytics in Financial Statement
Audits

Min Cao, Roman Chychyla, and Trevor Stewart

SYNOPSIS: Big Data analytics is the process of inspecting, cleaning, transforming, and
modeling Big Data to discover and communicate useful information and patterns,
suggest conclusions, and support decision making. Big Data has been used for
advanced analytics in many domains but hardly, if at all, by auditors. This article
hypothesizes that Big Data analytics can improve the efficiency and effectiveness of
financial statement audits. We explain how Big Data analytics applied in other domains
might be applied in auditing. We also discuss the characteristics of Big Data analytics,
which set it apart from traditional auditing, and its implications for practical
implementation.

Keywords: Big Data; analytical methods; auditing.

INTRODUCTION

B
ig Data includes datasets that are too large and complex to manipulate or interrogate with
standard methods or tools. It is characterized by ‘‘three Vs’’: volume, velocity, and variety
(McAfee and Brynjolfsson 2012). Volume refers to the sheer size of the dataset, velocity to

the speed of data generation, and variety to the multiplicity of data sources; the three Vs tend to be

interrelated.1 Traditional datasets utilized by auditors and academia, such as Compustat, CRSP, and

Audit Analytics, are not Big Data. Big Data is a relatively recent phenomenon, the product of a

technological environment in which almost anything can be recorded, measured, and captured

Min Cao is an Assistant Professor at Rutgers, The State University of New Jersey, New Brunswick,
Roman Chychyla is a Visiting Assistant Professor at the University of Miami, and Trevor Stewart is
a retired Deloitte partner and a Senior Research Fellow at Rutgers, The State University of New
Jersey.

The authors gratefully acknowledge the advice, help, and comments received from many individuals including
Khrystyna Bochkay, Alexander Kogan, Miklos Vasarhelyi, and seminar participants at Rutgers, The State University of
New Jersey. We also thank the editors, Arnold M. Wright, Paul A. Griffin, and Brad M. Tuttle, as well as two
anonymous reviewers for their helpful and insightful comments.

Submitted: February 2015
Accepted: February 2015

Published Online: February 2015
Corresponding author: Trevor Stewart

Email: trsny@verizon.net

1 Some also refer to the four Vs of Big Data, the fourth being ‘‘veracity’’ (Zhang, Yang, and Appelbaum 2015).

423

mailto:trsny@verizon.net

digitally, and thereby turned into data—a process that Mayer-Schönberger and Cukier (2013) refer

to as ‘‘datafication.’’ Datafication may track thousands of simultaneous events; be performed in real

time; involve a multiplicity of numbers, text, images, sound, and video; and require petabytes

(thousands of terabytes) of storage capacity.2 Examples of Big Data include more than 1 million

customer transactions every hour at Walmart, more than 50 billion photos on Facebook, and 200

gigabytes of astronomical data collected per night.3 Big Data has been used in marketing to target

potential customers, in political campaigning to study voter demographics, in sports to evaluate

teams and players, in national security to identify threats, in biology to study DNA, and in law

enforcement to identify crime suspects (Mayer-Schönberger and Cukier 2013).

Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data
to discover and communicate useful information and patterns, suggest conclusions, and support

decision making. For the purposes of this article, we assume that the auditor focuses on the

transactions, balances, and disclosures that underlie the financial statements and related

management assertions. In the auditing of financial statements in accordance with International

Statements on Auditing (ISAs), numerous potential opportunities arise for Big Data analytics. For

example, the following audit activities are likely to benefit from Big Data analytics:

� Identifying and assessing the risks associated with accepting or continuing an audit
engagement, for example, the risks of bankruptcy or high-level management fraud.

� Identifying and assessing the risks of material misstatement of the financial statements due to
fraud, and testing for fraud with regard to the assessed risks (ISA 240, IAASB 2014a).

� Identifying and assessing the risks of material misstatement through understanding the entity
and its environment (ISA 315, IAASB 2014b). This includes performing preliminary

analytical procedures, as well as evaluating the design and implementation of internal

controls and testing their operating effectiveness.
� Performing substantive analytical procedures in response to the auditor’s assessment of the

risks of material misstatement (ISA 520, IAASB 2014c).
� Performing analytical procedures near the end of the audit to assist the auditor in forming an

overall conclusion about whether the financial statements are consistent with the auditor’s

understanding of the entity (ISA 520, IAASB 2014c).

In this article, we hypothesize that a financial statement audit can potentially be improved by

analytical methods that use Big Data. In such audits, the data are transactions and balances that

usually reside in the entity’s ERP and data warehouse systems. These data are not Big Data per se
unless they are accumulated over a significant period of time or are complemented with additional

facts. Therefore, most Big Data opportunities discussed in this paper come from auxiliary data that,

after processing, may reveal matters of audit interest. The Big Data of potential audit interest

includes social media information, surveillance videos, and stock market transaction data.

EXAMPLES OF BIG DATA ANALYTICS

Since there are few, if any, current applications of Big Data analytics in external auditing, and

none that we are aware of, we describe examples from other disciplines and hypothesize how

similar applications could be implemented in external auditing.

2 See, http://archive.wired.com/science/discoveries/magazine/16-07/pb_intro for a good illustration of how much a
petabyte is.

3 When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first
few weeks than all data collected in the history of astronomy.

424 Cao, Chychyla, and Stewart

Accounting Horizons
June 2015

http://archive.wired.com/science/discoveries/magazine/16-07/pb_intro

First, the new availability of voluminous and informative sources of data has resulted in new

approaches to predict stock price averages. For instance, Bollen, Mao, and Zeng (2011) measure

global public mood based on Twitter data and successfully use it to predict daily fluctuations of the

Dow Jones Industrial Average (DJIA). They utilized Google’s Profile of Mood States and

academically developed OpinionFinder (Wilson et al. 2005) tools to generate daily time series of

the public mood shifts based on nearly 10 million public tweets posted by approximately 2.7

million users. By doing this, the authors were able to predict shifts in the DJIA three to four days

ahead. In addition to social media, news articles are also known to predict movements of stock

prices (Chan 2003; Mittermayer 2004). It is conceivable that similar data sources can be used to

predict bankruptcy or assess the overall financial state of a firm. Such tools might be used to better

identify and evaluate engagement risk and thus reduce litigation risk.

Second, demographic and weather data have been used to predict customers’ behavior.

OfficeMax, a large retailer of office supplies, uses LivePredict, a system built by online technology

provider Monetate, to personalize online landing pages based on customers’ demographics.4

Interestingly, this system tries to predict customers’ political views, and adjusts accordingly. The

system uses IP addresses to identify customers’ locations and U.S. census data to create

demographic profiles. In a weather-related application, Walmart analyzed its terabytes of

transactional data to determine that when hurricanes threatened, customers not only bought

additional flashlights, but that sales of strawberry Pop-Tarts (a popular breakfast snack) increased

sevenfold.5 This and similar findings from Big Data analytics help Walmart to better manage

inventories. Geographical and demographic data have a potential to reasonably predict revenues

and sales in individual business locations. The resulting estimates may be used as a benchmark to

assess sales amounts by locations. In addition, peer-based metrics can be utilized to draw attention

to possibly problematic branches. Similar analytics may improve the audit process by focusing

resources on more risky parts of the business.

Third, Big Data analytics commonly involves combining several sources of data, some

structured and others unstructured, including numbers, text, images, sound, and video—the

processing of which requires a combination of different analytical methods from different

disciplines. An example is Ayata’s Prescriptive Analytics, which is used in oil and gas exploration

to predict optimal drilling sites based on data such as images from well logs, videos of fluid flows

from hydraulic fractures, sounds from drilling operations, text from driller’s notes, and numbers

from production reports.6 The challenge of integrating different sources of Big Data including

news, audio and video streams, cell phone recordings, social media comments, and using them for

audit purposes is discussed by Moffitt and Vasarhelyi (2013), who propose using such data to

obtain new forms of evidence, confirm existence of events, and validate reporting elements.

Fourth, the Los Angeles Police Department analyzes data from crime scenes, including time,

location, nature, and actors in order to predict the most likely timing and location of crimes on that

day and to deploy forces most effectively.7 The result has been a significant improvement in the

LAPD’s ability to forestall criminal activity and neutralize potential perpetrators such as gang

members in the predicted area. Similar analytics that relies on information about a firm’s past

activities or outcomes of past audits could be used by auditors to identify fraud risks and direct audit

effort aimed at fraud detection.

4 See, http://www.forbes.com/sites/lydiadishman/2013/08/08/forget-ab-testing-office-max-uses-livepredict-to-
segment-red-and-blue-voters/

5 See, http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html
6 See, http://www.wired.com/insights/2014/01/big-data-analytics-can-deliver-u-s-energy-independence/
7 See, http://www.huffingtonpost.com/2012/07/01/predictive-policing-technology-los-angeles_n_1641276.html

Big Data Analytics in Financial Statement Audits 425

Accounting Horizons
June 2015

http://www.forbes.com/sites/lydiadishman/2013/08/08/forget-ab-testing-office-max-uses-livepredict-to-segment-red-and-blue-voters/

http://www.forbes.com/sites/lydiadishman/2013/08/08/forget-ab-testing-office-max-uses-livepredict-to-segment-red-and-blue-voters/

http://www.wired.com/insights/2014/01/big-data-analytics-can-deliver-u-s-energy-independence/

http://www.huffingtonpost.com/2012/07/01/predictive-policing-technology-los-angeles_n_1641276.html

Fifth, the SEC is investing in Big Data analytics applications to monitor market events, seek

out financial statement fraud, and identify audit failures. For example, Market Information Data

Analysis System (MIDAS), rolled out by the SEC in January 2013, collects about one billion

records a day from the proprietary feeds of each of the 13 national equity exchanges, time-stamped

to the microsecond. The data is extremely voluminous, challenging to process correctly, and

requires specialized data expertise. In July 2013, the agency announced the formation of Financial

Reporting and Audit Task Force to strengthen the effort to identify securities law violations relating

to the preparation of financial statements, issuer reporting and disclosure, and audit failures. The

task force uses an analytical Accounting Quality Model (AQM), better known in financial services

as ‘‘RoboCop,’’ to scan routine regulatory filings and flag high-risk activities warranting closer
inspection by SEC enforcement teams. At the same time, the SEC also announced the formation of

the Microcap Fraud Task Force to investigate fraud in the issuance, marketing, and trading of

microcap securities. The task force will monitor websites and social media because microcap

fraudsters frequently employ them to prey on unsophisticated investors.8 Similar analytics could be

used by auditors to identify fraudulent or high-risk activities by auditees.

Finally, we note that internal audit groups at some large companies are utilizing Big Data

within their organizations. For example, the internal audit team at BlueCross and BlueShield of

North Carolina uses Big Data analytics to identify duplicate insurance claims from millions of

claims each month.9 KPMG, Deloitte, and PwC all have publications on their websites explaining

how internal auditors can use data analytics to improve both efficiency and effectiveness. For

example, KPMG suggests that ‘‘With data analytics, organizations have the ability to review every
transaction—not just a sample—which enables a more efficient analysis on a greater scale’’ (KPMG
2013, 1). Many internal audit activities mirror those of external financial statement audits and

similar Big Data analytics can be applied.

CHARACTERISTICS OF BIG DATA ANALYTICS

There are certain characteristics of Big Data analytics that are causing users to rethink how data

are used. First, it is increasingly possible to analyze ALL or almost all the data rather than just a

small, carefully curated subset or sample. This can lead to models that are more robust than before.

For example, if an auditor wants to determine what characteristics of journal entries are indicators

of risk of error or fraud, then it is possible to analyze all the journal entries for however long records

have been kept and use this information to identify current journal entries that are truly unusual.

Whereas in the past one had to be very careful to eliminate polluted data, when all the data are

available a certain degree of messiness is acceptable.10

A second shift in thinking is from causation to correlation. Instead of trying to understand the

fundamental causes of complex phenomena, it is increasingly possible to identify and make use of

correlations. For example, Mayer-Schönberger and Cukier (2013, 132) report that ‘‘researchers at
the University of Ontario Institute of Technology and IBM are working with a number of hospitals

on software to help doctors make better diagnostic decisions when caring for premature babies . . .
The software captures and processes patient data in real time, tracking 16 different data streams,

such as heart rate, respiration rate, temperature, blood pressure, and blood oxygen level, which

8 http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.U0X3m6HD_IU (last accessed Janu-
ary 16, 2014).

9 See, https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/big-data-oceans .
10 We recognize that polluted data may be more of a problem in some applications than in others. For example, more

data dramatically help in the area of computational linguistics, even if data are messy (Weikum et al. 2012).
However, data quality may be more important than data size in movie-recommending systems (Pilászy and Tikk
2009).

426 Cao, Chychyla, and Stewart

Accounting Horizons
June 2015

http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.U0X3m6HD_IU

http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.U0X3m6HD_IU

https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/big-data-oceans

together amount to around 1,260 data points per second.’’ While these observations may allow

doctors to eventually understand fundamental causes, simply knowing that something is likely to

occur is arguably more important than understanding exactly why. It is not hard to imagine an

analogous auditing application in which restatements or other adverse events are correlated with

indicators culled from every public company filing and other information.

The ability to use correlation models with vast amounts of high-velocity data, in order to

pinpoint transactions or events of audit interest, becomes significantly more useful when applied

continuously. Continuous auditing and monitoring systems are likely to become particularly

relevant in this Petabyte Age, transforming audit practice (Vasarhelyi and Halper 1991; Alles,

Brennan, Kogan, and Vasarhelyi 2006) where, for example, statistical relationships between

different business elements and processes may be monitored continuously to detect irregular events

(Kogan, Alles, Vasarhelyi, and Wu 2011).

IMPLEMENTING BIG DATA ANALYTICS IN AUDITS

Implementing Big Data analytics is not a trivial endeavor. It requires individuals with expertise

in data analytics, as well as appropriate hardware and software resources. As a result, many

businesses outsource their Big Data applications to solutions providers such as Teradata, IBM, and

Wipro that offer specialist services. Similarly, the training related to Big Data analytics may go well

beyond the scope of the academic and professional level of an auditor. The auditing profession will

have either to hire new analytically trained professionals, or more likely to use the services of third-

party solutions providers for Big Data analytics. Relying on third-party solutions providers raises a

privacy concern, but this issue is not new—the profession already relies on third parties, such as

banks, when carrying out audits.

In identifying anomalies and exceptions for further audit investigation, current implementa-

tions of analytical methods sometimes generate more false positives than can feasibly be

investigated by the audit team, and result in information overload (Debreceny, Gray, and Rahman

2003; Alles, Kogan, and Vasarhelyi 2008). One of the opportunities of Big Data analytics is the

possibility of dramatically reducing the number of false positives through more accurate

identification of true anomalies and exceptions together with better systems of prioritization (Issa

and Kogan 2013).

There are several issues that the auditing profession will need to deal with related to Big Data

analytics. First, making successful use of Big Data requires a paradigm shift. Instead of using some

data in small clean datasets and focusing on causation (plausible relationships in ISA terms), the

auditor using Big Data will tend to use ‘‘all’’ the data in large relatively messy datasets, and will

focus more on correlation than causation. The degree to which this approach is implemented in

audit will vary according to the stage of an audit: using messy data is more tolerable for planning

and risk assessment as opposed to substantive procedures. For example, Big Data analytics can be

used to identify business patterns and trends, traditional audit analytics and computer-assisted audit

techniques can be used to conduct a more detailed analysis of potential issues, and conventional

auditing judgment can be used to determine the impact of findings on financial reporting. In

addition, messy data might not be appropriate for analytical procedures that are sensitive to noise.

Nevertheless, this thinking is somewhat foreign to the profession. It will certainly require significant

new guidance and education, and may even require auditing standards themselves to be modified.

Second, the volume of Big Data introduces significant computational challenges. Many

common analytical techniques used in auditing could not be applied to Big Data. The solution is

either to use simple analytical techniques that require less computational resources, or to select

subsets of data that could be managed by more complex analytical tools. The latter case is using Big

Big Data Analytics in Financial Statement Audits 427

Accounting Horizons
June 2015

Data to carefully select a subset that is more valuable for an audit. For example, there are methods

to select subsets of data that result in more accurate analytical models (e.g., see Settles 2009).

Third, privacy is a potential concern when Big Data is used. Some analytics may require

clients’ nonpublic information beyond that usually released to auditors. Others would benefit from

information about previously conducted audits, perhaps of other clients. The usage of such sensitive

information in Big Data applications presents a challenge, although this concern is not specific to

auditing. For example, the European Union is scrutinizing Google over a raft of antitrust and

privacy concerns related to its use of Big Data (Mayer-Schönberger and Cukier 2013).

Finally, when ‘‘all’’ the data are processed through the auditor’s analytical systems and there is
a failure to identify fraud or error, there is a risk that the auditor will be second-guessed. It is always

easy for others who have the benefit of hindsight to identify indicators that the auditor missed and to

connect the dots—just as the U.S. intelligence community was castigated for not connecting, in

advance, the dots that would have led to the apprehension of the bombers of the 2013 Boston

Marathon. This is not an entirely new problem, but auditors have traditionally based their work on

samples, and it is accepted that there is a statistical risk that fraud or error will not be identified.

Last, a change to Big Data analytics could identify fraud or error that was missed in the past. Again,

this is not a new problem, but it is an issue that auditors adopting Big Data analytics will likely have

to deal with.

Besides using Big Data analytics to perform audits, audit firms can potentially use it for

internal purposes. For example, since most audit working papers are electronic, there is an

opportunity for the firm to analyze audits across an entire portfolio in search of anomalies and

potential quality issues.

CONCLUDING REMARKS

Big Data is revolutionizing many fields at an increasing rate, and it seems only a matter of time

before the auditing profession adopts similar analytical methods. In this paper, we provide examples

of Big Data analytics and suggest analogous auditing applications. We briefly discuss certain

characteristics of Big Data analytics that are relevant to audit and identify some of the opportunities

and challenges of implementation.

REFERENCES

Alles, M., G. Brennan, A. Kogan, and M. Vasarhelyi. 2006. Continuous monitoring of business process

controls: A pilot implementation of a continuous auditing system at Siemens. International Journal
of Accounting Information Systems 7 (2): 137–161.

Alles, M., A. Kogan, and M. Vasarhelyi. 2008. Putting continuous auditing theory into practice: Lessons

from two pilot implementations. Journal of Information Systems 22 (2): 195–214.
Bollen, J., H. Mao, and X. Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational

Science 2 (1): 1–8.
Chan, W. 2003. Stock price reaction to news and no news: Drift and reversal after headlines. Journal of

Financial Economics 70 (2): 223–260.
Debreceny, R., G. Gray, and A. Rahman. 2003. The determinants of Internet financial reporting. Journal of

Accounting and Public Policy 21 (4): 371–394.
International Auditing and Assurance Standards Board (IAASB). 2014a. The auditor’s responsibilities

relating to fraud in an audit of financial statements. ISA 240. In Handbook of International Quality
Control, Auditing, Review, Other Assurance, and Related Services Pronouncements, Vol. 1. New
York, NY: International Federation of Accountants. Available at: http://www.ifac.org

International Auditing and Assurance Standards Board (IAASB). 2014b. Identifying and assessing the risks

of material misstatement through understanding the entity and its environment. ISA 315. In

428 Cao, Chychyla, and Stewart

Accounting Horizons
June 2015

dx.doi.org/10.1016/j.accinf.2005.10.004

dx.doi.org/10.1016/j.accinf.2005.10.004

dx.doi.org/10.2308/jis.2008.22.2.195

dx.doi.org/10.1016/j.jocs.2010.12.007

dx.doi.org/10.1016/j.jocs.2010.12.007

dx.doi.org/10.1016/S0304-405X(03)00146-6

dx.doi.org/10.1016/S0304-405X(03)00146-6

dx.doi.org/10.1016/S0278-4254(02)00067-4

dx.doi.org/10.1016/S0278-4254(02)00067-4

http://www.ifac.org

Handbook of International Quality Control, Auditing, Review, Other Assurance, and Related
Services Pronouncements, Vol. 1. New York, NY: International Federation of Accountants.
Available at: http://www.ifac.org

International Auditing and Assurance Standards Board (IAASB). 2014c. Analytical procedures. ISA 520. In

Handbook of International Quality Control, Audititing, Review, Other Assurance, and Related
Services Pronouncements, Vol. 1. New York, NY: International Federation of Accountants.
Available at: http://www.ifac.org

Issa, H., and A. Kogan. 2013. A Predictive Ordered Logistic Regression Model for Quality Review of
Control Risk Assessments. Working paper, Rutgers Accounting Research Center.

Kogan, A., M. Alles, M. Vasarhelyi, and J. Wu. 2011. Analytical Procedures for Continuous Data Level
Auditing: Continuity Equations. Working paper, Rutgers Accounting Research Center.

KPMG. 2013. Data Analytics for Internal Audit. Available at: https://www.kpmg.com/CH/en/Library/
Articles-Publications/Documents/Advisory/pub-20130412-data-analytics-internal-audit-en

Mayer-Schönberger, V., and K. Cukier. 2013. Big Data: A Revolution That Will Transform How We Live,
Work, and Think. Boston, MA: Eamon Dolan/Houghton Mifflin Harcourt.

McAfee, A. and E. Brynjolfsson. 2012. Big data: the management revolution. Harvard Business Review 90:
60–66.

Mittermayer, M. 2004. Forecasting Intraday Stock Price Trends with Text Mining Techniques. Proceedings
of the 37th Annual Hawaii International Conference on System Sciences, Hawaii, HI, January 5–8.

Moffitt, K., and M. Vasarhelyi. 2013. AIS in an age of Big Data. Journal of Information Systems 27 (2): 1–
19.

Pilászy, I., and D. Tikk. 2009. Recommending new movies: Even a few ratings are more valuable than

metadata. In Proceedings of the Third ACM Conference on Recommender Systems, 93–100. New
York, NY: RecSys 2009.

Settles, B. 2009. Active Learning Literature Survey 1648. Computer Sciences Technical Report, University
of Wisconsin–Madison.

Vasarhelyi, M., and F. Halper. 1991. The continuous audit of online systems. Auditing: A Journal of
Practice & Theory 10 (1): 110–125.

Weikum, G., J. Hoffart, N. Nakashole, M. Spaniol, F. Suchanek, and M. Yosef. 2012. Big data methods for

computational linguistics. IEEE Data Engineering Bulletin 35 (3): 46–64.
Wilson, T., P. Hoffmann, S. Somasundaran, J. Kessler, J. Wiebe, Y. Choi, C. Cardie, E. Riloff, and S.

Patwardhan. 2005. OpinionFinder: A system for subjectivity analysis. In Proceedings of HLT/
EMNLP on Interactive Demonstrations, 34–35. Stroudsburg, PA: Association for Computational
Linguistics.

Zhang, J., X. Yang, and D. Appelbaum. 2015. Toward effective Big Data analysis in continuous auditing.

Accounting Horizons 29 (2).

Big Data Analytics in Financial Statement Audits 429

Accounting Horizons
June 2015

http://www.ifac.org

http://www.ifac.org

https://www.kpmg.com/CH/en/Library/Articles-Publications/Documents/Advisory/pub-20130412-data-analytics-internal-audit-en

https://www.kpmg.com/CH/en/Library/Articles-Publications/Documents/Advisory/pub-20130412-data-analytics-internal-audit-en

dx.doi.org/10.2308/isys-10372

Copyright of Accounting Horizons is the property of American Accounting Association and
its content may not be copied or emailed to multiple sites or posted to a listserv without the
copyright holder’s express written permission. However, users may print, download, or email
articles for individual use.

Page 1 of 5

I. Title: Using Visualization Software in the Audit of Revenue Transactions to
Identify Anomalies

II. Introduction:

In this project, you will use Tableau, data visualization software, to audit revenue
transactions to identify anomalies.

The project includes a group component to reflect
the collaborative nature of accountants who must work together on audit teams. You
will be randomly placed into an online discussion group in LEO. The purpose of the
online discussion group is to help one another learn how to use Tableau and make
decisions regarding the results of your data analysis. Participation in the online
discussion group is mandatory.

After collaboratively discussing the case with your colleagues (classmates) in the online
discussion group, you will write and submit an individual report written in your own
words to your boss (professor).

Scenario:
You are a senior auditor at the CPA firm of Aoife & Josephine, LLC. Your manager
(professor) calls you into her office to discuss the use of Tableau, data analytic and
visualization software, on an upcoming audit for a client. She highly suggests you learn
how to use Tableau to perform data analytics on sales revenue. Further, she suggests
you read the following articles to prepare for this audit:

▪ Cao, M., R. Chychyla, and T. Stewart. 2015. Big Data analytics in financial
statement audits. Accounting Horizons 29 (2): 423–429.
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/lo
gin.aspx?direct=true&db=bth&AN=103541034&site=eds-live&scope=site

▪ Raphael, J. 2017. Rethinking the audit. Journal of Accountancy 223 (4): 28–32.
http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/lo
gin.aspx?direct=true&db=heh&AN=122600698&site=eds-live&scope=site

▪ On the ICAEW Website, you may download an excellent report:
ICAEW.com. (2016). Data analytics for external auditors. [online] Available at:
https://www.icaew.com/-/media/corporate/files/technical/iaa/tecpln14726-iaae-data-
analytics—web-version.ashx [Accessed 13 Jul. 2019].

III. Steps to Completion

1. Read the articles recommended by your manager (professor) to gain an
understanding about how big data, data analytics, and new technologies are
transforming external audits.

2. Read the case: Using Visualization Software in the Audit of Revenue Transactions to
Identify Anomalies, which is posted in LEO: Contents>Course Resources>Projects &
Rubrics.

http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=bth&AN=103541034&site=eds-live&scope=site

http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=bth&AN=103541034&site=eds-live&scope=site

http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=heh&AN=122600698&site=eds-live&scope=site

http://ezproxy.umuc.edu/login?url=http://search.ebscohost.com.ezproxy.umuc.edu/login.aspx?direct=true&db=heh&AN=122600698&site=eds-live&scope=site

Page 2 of 5

3. Review resources to learn how to use Tableau in Appendix A on the last page of this
document. Collaborate with your online discussion group to learn Tableau tips. Watch
the Tableau video tutorials help videos as needed, which can be found in Appendix A.

4. Complete the case requirements that start on page 35 of Using Visualization
Software in the Audit of Revenue Transactions to Identify Anomalies.

5. Complete the following one additional requirement, which is not included in the case
study. Prepare an audio-enhanced presentation using MS PowerPoint with audio.
Adding a video of yourself using a Webcam is optional in the lower right corner of your
PowerPoint recording is optional. The presentation file should present a summary of
your findings.

The PowerPoint presentation must include the following:

▪ Graphs, tables, and other data visualization tools to explain and support your
findings, and

▪ Speakers notes placed under each slide.

IV. Deliverables

1. Report
Prepare an 8-10-page (excluding the cover sheet) report to the boss in MS Word
that responds to questions 1 and 2 presented in the case study on page 35 of
Using Visualization Software in the Audit of Revenue Transactions to Identify
Anomalies.

a. Prepare the report using APA Style.
i.Be sure to include a cover sheet, separate reference page, and cite
all sources. Failure to adhere to APA will result in a lower grade.

b. Your report will be reviewed through TurnItIn.

2. Memo
To respond to question 3 on page 35 of Using Visualization Software in the Audit
of Revenue Transactions to Identify Anomalies, prepare a memo using the
template in Appendix B to document your analyses and conclusions as to the
specific daily sales from certain locations that you recommend selecting for
focused substantive testing.

a. Prepare the memo using an audit workpaper format. APA is inappropriate
for this memo requirement. Your memo should represent your individual
work written in your own words.

b. Your memo will also be reviewed through TurnItIn.

3. Presentation
Prepare an audio-enhanced PowerPoint presentation that summarizes your
findings for Deliverables 1 and 2. Adding a video of yourself in the lower corner
of your PowerPoint slides is optional.

Page 3 of 5

V. Rubric:

You will find the rubric in LEO under Contents>Course Resources>Projects & Rubrics.

VI. Helpful Hints

1. Read the grading rubric before beginning the project to fully understand the
requirements; ask questions about the requirements if needed.

2. Write your deliverables in your own words.
3. All writing assessments submitted in the Assignment folder will be

automatically submitted to TurnItIn (TII). Students are strongly advised to
review your TII Similarity Score and edit your submission if necessary.
TurnItIn.com offers many resources to help you learn how to interpret the
Similarity score. Citing what other authors’ have published is expected in
graduate courses. If you are unfamiliar with APA Style requirements for in-
text citations, the reference list, etc. please learn how to apply APA to all
deliverables.

4. Review.
▪ Developing your graduate level writing skills:

https://owl.english.purdue.edu/owl/section/1/2/
▪ What Constitutes Graduate Level Writing; source unknown. In LEO,

Content, Week 9.
5. Seek feedback before submitting your final version for a grade.

▪ Ask a classmate, friend, or family member to read your report, watch
your presentation, and share constructive feedback to help improve
your final version.

▪ Submit your draft MS Word documents to the graduate writing tutors at
least 2 weeks before the project due date to ensure you will have time
to review their feedback and edit your documents as needed.

▪ Graduate writing tutors are a FREE resource and can be accessed in
your LEO classroom under Content.

6. Submit all required files on or before the due date. Late assignment policies
apply and can be found in the syllabus. No assignments are accepted after
the last day of class.

7. Ask questions as needed in the weekly Ask the Professor forums.

https://owl.english.purdue.edu/owl/section/1/2/

Page 4 of 5

Reference

Christensen, B., R. Elder, and S. Glover. 2015. Behind the numbers: Insights into large

audit firm sampling policies. Accounting Horizons 29 (1): 61–81.
https://doi.org/10.2308/acch-50921 Using Visualization Software in the Audit of
Revenue Transactions to Identify Anomalies 35 Issues in Accounting Education
Volume 33, Number 4, 2018

https://doi.org/10.2308/acch-50921

Page 5 of 5

Appendix A

Tableau Tutorials

Go to the Tableau.com Website and type Free Training Videos into the search field.
https://www.tableau.com/learn/training

The Tableau Video Library includes over 9 hours of videos:

▪ Getting Started – 3 videos
▪ Connecting to Data – 11 videos
▪ Visual Analytics – 24 videos
▪ Dashboards and Stories – 8 videos
▪ Mapping – 11 videos
▪ Calculations – 16 videos
▪ Why is Tableau Doing That? – 5 videos
▪ How To – 10 videos

Finding the more relevant video resources is an excellent opportunity for your online
discussion group to help each other learn Tableau.

https://www.tableau.com/learn/training

A U D IT IN G / PROFESSIONAL DEVELOPMENT

Rethinking the audit
Innovation is transforming how audits are conducted—
and even what it means to be an auditor.
By Jon Raphael, CPA

T
oday’s audit profession is driving exciting and
unprecedented changes that are fundamen­
tally evolving the role o f the auditor and how

audits are performed.
Breakthrough innovations in areas such as arti­

ficial intelligence, workflow automation, and data
analytics are eliminating a number o f the tedious
and labor-intensive manual processes traditionally
associated with an audit. More importantly, innova­
tion is enabling auditors to deliver powerful insights
that simply weren’t possible before. These changes
can enhance audit quality and deliver higher value
for audit stakeholders— from clients and audit
professionals to investors and the capital markets
as a whole. (See the article “H ow to Enable Audit
Innovation,” page 33, for details on how Deloitte
nurtures in-house ideas to innovate audits.)

INSIGHTS ENHANCE QUALITY
Automation and other cutting-edge innovations
reduce the amount o f manual and time-consuming
data collection required for an audit. But th a t’s just
the beginning.

A n even bigger benefit o f audit innovation is
the ability to generate new kinds o f insights that
increase the value o f an audit and bring audit qual­
ity to a new level. Powered by innovative technolo­
gies and supported by a risk-based methodology,
auditors now have more resources, tools, and time
to strategically apply their most important skills—
professional skepticism and judgment— to business
issues, controls, and risks. W h a t’s more, auditors
are armed with advanced analytical tools to provide
deeper insights, including areas beyond the limits of
a more traditional audit.

For example, using the latest technologies,
auditors can analyze complete data sets rather than
samples. Advanced tools can be applied to all of
a company’s contracts related to an area o f audit
interest, or to metadata about an automated key
control. This can reduce audit risk by making it less

likely for an unusual transaction to slip through
the cracks. Also, given the transformational nature
o f advanced technologies and analytics, innovative
audit tools can readily reveal valuable insights
about a business for clients to consider, such as
operational inefficiencies and areas for poten­
tial improvement.

INNOVATIONS RESHAPE THE AUDIT
Technology and innovation are advancing at
breakneck speed with unprecedented computing
power to transform the audit. These advanced
technologies, sometimes referred to as “exponen­
tials,” represent technological breakthroughs at the
intersection o f information technology and science,
and they are increasingly a driving force behind
audit innovation. Here are some examples o f how
these exponential technologies and other forms
o f innovation are powering audits forward and
promise a bright future for audit professionals:

Artificial intelligence
Artificial intelligence (AI) involves the theory and
development o f computer systems able to perform
tasks that normally require human intelligence.
Because AI technologies (also called cognitive
technologies) can tackle many tasks performed
traditionally by humans, they can enable an audit
to avoid the typical trade-offs between speed and
quality. Two AI technologies that are especially
relevant to audit are natural language process­
ing (NLP), which enables a system to read and
understand key concepts in electronic documents,
and machine learning, which enables a system to
improve itself without being reprogrammed. As
audit evidence increasingly becomes more digitized,
these technologies, combined with workflow
automation, enable auditors to do significantly
more analysis in less time. This can allow auditors
to spend more time on tasks that add more value to
the audit. ►

journalofaccountancy.com April 2017 I 29

AUDITING / PROFESSIONAL DEVELOPMENT

W o r k f l o w a u t o m a t i o n

Through the creative use o f technology, many audit
activities that previously required time-consuming
manual processing by auditors can now be auto­
mated. As a result, much o f an audit’s tedium can
be reduced and enable analysis that is faster and
more comprehensive. Rather than burning the
midnight oil lost in piles o f paper, audit profession­
als can experience higher-level activities sooner in
their careers and make a faster impact in driving
quality and insights.

For example, electronic document review is one
important audit area that is already being trans­
formed with workflow automation and AI. Deloitte
auditors teach Argus, an application that runs on
a licensed cognitive technology, to enable rapid
audit-specific document interrogation and analysis.
This tool uses N LP and machine learning to read
and analyze any kind of electronic document.

After reviewing a population o f documents
(e.g., leases), Argus uses machine learning to help
identify and visualize items o f interest to an auditor,
and then extracts its findings into a working paper.
Argus can examine hundreds o f documents and
identify areas that were modified from a standard
form of the contract. In situations when the auditor
expects all documents to be consistent for a type of
transaction, Argus can find the needles in the hay­
stack with just a few clicks. Argus also completes
document reviews in a fraction o f the time, enabling
auditors to review and assess larger samples— even
up to 100% o f a population.

A practical use for Argus is the review o f execu­
tive compensation arrangements. An auditor can
use Argus to look at all o f the contracts and identify
what is different across the entire population. In a
large multinational audit, this could require days
to complete, or the auditor might have selected a

sample. W ith innovative technology, the auditor
can now read one agreement and then just look at
what changes across the population and focus on
the why, almost in real time. The insights that are
derived and the quality o f the questions increase,
and auditors can acquire greater understanding
about their clients. This benefits audit quality and
the client.

Since Argus’s release last year, Deloitte’s auditors
have used the application to review a wide range of
document types including, but not limited to:
■ Sales, leasing, and derivatives contracts;
■ Employment agreements;
■ Invoices;
■ Client meeting minutes;
■ Legal letters; and
■ Financial statements.

A n a ly tic s a n d v is u a l iz a t i o n

Analytics and data visualization enable auditors
to uncover valuable insights hidden within large
and complex data sets, and better inform the risk
assessment process.

A common audit area in which analytics can
streamline the audit is performing tests on massive
journal-entry populations to identify risks and items
o f audit interest. W ith this information, auditors are
uniquely positioned to generate powerful insights
about a client’s accounting functions, business
operations, and internal control processes, such as
how many manual entries are being booked across
an organization for very low dollar values. Aided by
advanced technology, an auditor can not only inter­
rogate millions o f journal-entry records in-memory
on a computer in real time, but also visualize the
journal-entry population in just a few clicks and
share real insights about how many journal entries
are booked for under $100, run a Benford analysis,

IN BRIEF

■ Technological developments such
as artificial intelligence, workflow
automation, and data analytics
are changing the way audits are
performed, elim inating some manual
processes from the list of duties

auditors must carry out.
■ These technologies present an

o pportunity to increase audit quality
through improved data analysis,
identification o f anomalies, and
communication o f value-added
insights to clients.

■ Although auditors don’t need to be

computer programmers, they need
to become comfortable using new
technological tools as they continue
to use their practical knowledge,
experience, and professional judgm ent
to deliver high-quality services.

To com m ent on this article o r to suggest an idea fo r another article, contact Ken Tysiac, editorial director, a t
Kenneth.Tysiac@aicpa-cima.com o r 919-402-2112.

30 Journal o f Accountancy April 2017

mailto:Kenneth.Tysiac@aicpa-cima.com

or evaluate the quantity of manual journal entries
at a particular location, which could highlight an
opportunity for automation or process improve­
ment. (For more about how to apply Benford’s Law
by using a common accounting technology tool, see
“Using Excel and Benford’s Law to Detect Fraud,”
page 44.)

W hile journal-entry testing is a good foun­
dational use case, analytics and visualization have
the potential to evolve the audit in so many ways.
For example, Deloitte has an advanced analytics
application that leverages data science to analyze
volumes of financial information from SEC filings
to identify and visualize potential accounting, fraud,
and failure risks for all public companies. Such
capabilities contribute to enhanced quality and
faster delivery o f insights to audit committees and
management for their consideration.

M o b ile
Mobile devices can have a major impact on how
audits are conducted. For example, mobile technol­
ogies are transforming the inventory count process,
a task performed by auditors that used to be as
old-fashioned as it gets: visiting a client and using
a pencil and clipboard to document information
related to inventory of materials and finished goods.

Now, Deloitte auditors can run a proprietary
mobile application called Icount on tablets and
smartphones to collect and consolidate inven­
tory count results automatically for real-time
consolidation and analysis in an online portal.
W hile conducting the count, the auditor can use
a voice-to-text capability to create working paper
review notes, take pictures of the inventory being
observed, and generate the working papers auto­
matically. The benefit o f mobile, however, is more
substantial than just using a smartphone to conduct
audit procedures, because the audit evidence is
captured digitally— and therefore is instantly avail­
able to all members o f the audit team for analysis.
Thus, an activity that could take weeks to compile
is transformed into essentially a real-time process.
Moreover, since it is standardized and scalable, it
focuses auditors on what matters most— observa­
tion and inquiry, not data collection and documen­
tation—because that is handled by the technology.

Advanced mobile capabilities such as these will
profoundly change the profession and how auditors
do their jobs. As we move toward a more connected
future with the Internet of Things (IoT), we will
likely see the emergence o f mobile audit applica­
tions featuring secure communication between

intelligent electronic devices with no human in­
volvement whatsoever—enabling auditors to focus
their time and attention on activities that are more
strategic and impactful. For example, with IoT a
machine can, in essence, report in real time its usage
and production, completely changing how depre­
ciation might be calculated and driving advanced
analytics and real-time auditing o f fixed assets. Also,
IoT data can be used to create substantive analytics
about revenue expectations and impairments, just to
name a few possibilities.

Blockchain
To stay in front o f the innovation curve, auditors
must monitor and understand emerging trends and
technologies with the potential to improve or even
disrupt our profession.

Blockchain is a technological breakthrough
being evaluated as a way to enable a wide range o f
financial transactions that, in theory, may be reliable
and potentially streamline the audit for certain
financial statement assertions. The digital currency
bitcoin is an application that uses blockchain. In
one example of a possible audit application for
blockchain, the processing o f derivative transac­
tions via the embedded code o f a “smart contract”
(an automated routine that executes specific code
when certain events occur—like a settlement o f
a derivative after a period o f time based on the
price o f particular stock) within a blockchain could
digitize and enhance components o f the audit
process. A t the same time, blockchain may present
new audit-related risks and opportunities— such as
the need for IT controls o f blockchain participants,
or even assurance over the blockchain itself—that
would require the profession to evolve quickly. For
example, would an independent auditor provide
assurance that smart contracts operate as intended?
Would members o f a blockchain consortium want
independent assurance about who can access a
private blockchain and whether the data are secure?

A N E W KIND OF AUDITOR
Innovation that is truly transformative requires
more than just technology—it also demands
fundamental changes in culture, processes, m eth­
odology, and talent. W ithout all five elements,
the profession cannot realize the full impact of
audit transformation.

Auditors will always need deep knowledge and
experience in traditional areas such as auditing
standards, financial accounting and reporting,
internal controls, IT, managerial accounting, and ►

journalofaccountancy.com April 2017 I 31

A U D IT IN G / P R O F E S S IO N A L D E V E L O P M E N T

About the
author

Jon Raphael
(jraphael@deloitte.
com) is a partner
and audit chief
innovation officer
of Deloitte&
Touche LLP.

taxation. However, these core capabilities are only a
starting point.

Auditors also need superior communication
skills, deep industry expertise, and the ability to
think critically and creatively while using technol­
ogy to manipulate and analyze client and external
audit data and find hidden risks and insights.
They will also need strong technology skills and
experience in key areas such as data analytics
and visualization.

Auditors don’t necessarily need to be technol­
ogy development experts or computer program­
mers; however, they do need practical knowledge,
experience, and a high level o f comfort using
cutting-edge, rapidly evolving technology to
manipulate and analyze data. Im portant technology
skills include:
■ M ining structured and unstructured data from a

wide range o f sources;
■ Identifying potential data risks and findings

(including security);
■ Working with relational and nonrelational

databases;
■ Applying statistical methods and advanced

analytics within tools to turn raw data into
useful insights;

■ Understanding how to leverage analytics to
perform robust risk assessments to identify areas
for further audit analysis; and

■ Using visualization tools to present complex data
analysis in a way that is compelling and easy to
understand.
Developing this new kind o f auditor will require

significant time and effort, with universities and
audit firms joining forces to identify what’s needed
and provide education and training, in the class­
room and on the job, that combines leading-edge

instruction with practical, hands-on experience. The
good news is that an innovative, technology-driven
approach to audit is a natural fit with today’s tech-
sawy students, giving them ample opportunities to
leverage their existing skills and interests quickly in
a way that delivers maximum value and impact.

Moreover, this new approach to education and
training starts to instill a culture o f innovation
among today’s new auditors. Audit firms need to
capitalize on this and nurture it, making a culture
o f innovation part o f a firm’s DNA. Case in point:
A t Deloitte, we have harnessed the power o f
crowdsourcing by holding an annual contest that
challenged our audit professionals to submit their
best ideas on how to innovate the audit. The ideas
presented by the finalists became the next chapter
o f our audit innovation strategy.

AN EXCITING TIM E
The audit profession has a reputation for be­
ing steady. In one sense, th at’s arguably the way
it should be, since an audit enhances trust and
confidence. However, that doesn’t mean the process
o f conducting an audit shouldn’t evolve to meet
the needs o f companies, investors, and the capital
markets. Today’s audit profession is aggressively
developing and implementing a wide range o f
innovations— including cutting-edge breakthroughs
in digital technology—that make audits more
streamlined and effective than ever. The results?
A transformed audit process. Auditors that add
more value. And a high-quality audit that is more
insightful than ever before.

This is truly a dynamic and exciting time for the
audit profession. ■

AICPA RESOURCES
Articles

“The N e x t F ro n tie r in Data
Analytics,'”JofA, A ug. 2016, tin y u rl.
c o m /z tk lo c v

“D riv in g Faster Decisions,” JofA,
A p ril/M a y 2015, tin y u rl.c o m /
h b 3 d 7 p n

“D ata A n a ly tic s Helps A u d ito rs
Gain D e e p Insight,” JofA, A p ril/
M ay 2015, tin y u rl.c o m /z 6 d w z k o

Publications

Audit an d Accounting M anual
(#AAMAAM16P, pa p erb a ck;
#W AM-XX, o n lin e s u b s c rip tio n )

Codification o f Statements
on Auditing Standards
(#AC0DSAS17P, p a p e rb a c k (was
e x p e c te d to be a vailable som e
tim e in March); #AC0DSAS16E,
e b o o k )

CPE self-study

A p p ly in g th e Risk A ssessm ent
Standards to Ensure a Q u a lity
A u d it (#733910, te x t; #164780,
o n lin e access; #GT-CL4ICRA,
g ro u p tra in in g )

A Firm’s System o f Q u a lity
C o n tro l (#164970, o n lin e access;
#GT-SQCS, g ro u p tra in in g )

Internal C o n tro l: F low Does
It Im p a c t an A udit? (#165800,
o n lin e access)

For m o re in fo rm a tio n o r to m ake
a purchase, g o t o aicpastore.com
o r call th e In s titu te at
888-777-7077.

Online resources

A u d it Data A n a ly tic s resource
page, tin y u rl.c o m /je y 6 o rs

32 l Journal o f Accountancy April 2017

Copyright of Journal of Accountancy is the property of American Institute of Ceritified Public
Accountants and its content may not be copied or emailed to multiple sites or posted to a
listserv without the copyright holder’s express written permission. However, users may print,
download, or email articles for individual use.

ISSUES IN ACCOUNTING EDUCATION American Accounting Association
Vol.

33

, No. 4 DOI: 10.2308/iace-52146
November 2018
pp. 33–46

Using Visualization Software in the Audit of Revenue
Transactions to Identify Anomalies

Lauren M. Cunningham
The University of Tennessee

Sarah E. Stein
Virginia Polytechnic Institute and State University

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.

Submitted: December 2017
Accepted: May 2018

Published Online: May 2018

33

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

2016 2015 Change

PBC

% Change

Revenue, Store Type 1 $4,062,390.97 $4,032,383.16 $30,007.81 0.74%

Revenue, Store Type 2 9,331,175.81 9,558,584.07 (227,408.26) �2.38%
Revenue, Store Type 3 5,425,421.53 5,546,767.89 (121,346.36) �2.19%
Total Revenue $18,818,988.31 $19,137,735.12 $(318,746.81) �1.67%

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.

1 As noted in Christensen, Elder, and Glover (2015), most of the large accounting firms emphasize the use of specific identification testing before random
sampling to increase the efficiency and effectiveness of substantive tests. Specific identification testing, also referred to as directed sampling, is usually
used to select and test individually significant items and those items identified as having a higher risk of misstatement.

34 Cunningham and Stein

Issues in Accounting Education
Volume 33, Number 4, 2018

Requirements

1. Read the articles assigned by your instructor to gain an understanding about how Big Data, data analytics, and new

technologies are transforming external audits. After reading these articles, provide a response to the following two

questions:

a. Discuss at least three specific ways in which Big Data, data analytics, and new technologies can enhance external

audits. How does each item discussed improve the effectiveness and/or efficiency of the audit?

b. On the other hand, what challenges do auditors face when using Big Data, data analytics, and new technologies

during an audit?

2. As noted in the case, auditing standards specifically require auditors to identify revenue recognition as a fraud risk in

most audits. Based on your understanding of the company, what factors may increase the risk of fraudulent financial

reporting in Souper Bowl’s 2016 revenues?

3. 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 transactions—for 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 year—instead,
you are using 2015 data as a component of your baseline prediction for 2016.

REFERENCE

Christensen, B., R. Elder, and S. Glover. 2015. Behind the numbers: Insights into large audit firm sampling policies. Accounting Horizons
29 (1): 61–81. https://doi.org/10.2308/acch-50921

Using Visualization Software in the Audit of Revenue Transactions to Identify Anomalies 35

Issues in Accounting Education
Volume 33, Number 4, 2018

https://doi.org/10.2308/acch-50921

APPENDIX A

Map of Store Locations for Souper Bowl Inc.

36 Cunningham and Stein

Issues in Accounting Education
Volume 33, Number 4, 2018

APPENDIX B

Example Memo Template*

Souper Bowl Inc.—December 31, 2016
Disaggregated Revenue Analytics

Purpose: The purpose of this memo is to document plausible trends and expectations for disaggregated revenue data and to
identify specific days and locations that warrant further substantive investigation.

Data: We obtained a listing of daily sales by location from the client’s IT system. We tested the details for mathematical
accuracy, as summarized in the table below:

Total Sales, 2015 Total Sales, 2016

Store Type 1 $ $

Store Type 2 $ $

Store Type 3 $ $

Total $ $

Procedures: Based on our risk assessment process, we identified the following assertions as significant risks related to
revenues/sales:

� Recorded sales occurred.
� Sales are accurately recorded.
� Sales are recorded in the proper period.

Because Souper Bowl’s operations are solely in the state of Maine, we obtained disaggregated data that reports daily sales

by store location and Store Type. Based on discussions with management and our review of the board of director minutes, we

are unaware of any new store locations or other major changes to operations during the year. Therefore, we expect the prior

year’s revenues to be a reasonable baseline expectation for this year’s revenues (e.g., similar seasonal trends). Because the

business can also be impacted by weather conditions, which vary by year, we also perform analyses that consider changes in

weather patterns to predict expected changes from the prior year’s sales. We performed several analytics to identify unusual

trends as compared to the prior year’s sales, taking weather conditions into consideration. The purpose of these analytics is to

identify specific observations (or specific sets of observations) to select for further substantive testing. The analytics that we

performed are as follows:

� Visualization Analysis #1: Title
[Provide a description of the relationship you expected to observe in the data, along with screenshots of the
visualization results. Clearly identify (using circles, arrows, etc.) the part of the visualization that leads you to believe
that a specific location/day is an anomaly. Ensure that all tables and graphics are properly labeled (x axis, y axis,
etc.).]

* Results: [In each of the ‘‘Results’’ sections, include a brief summary of your findings so that your manager can
see (in words) the way that you interpret the visualization screenshots.]

� Visualization Analysis #2: Title
* Results:

� [The number of analyses that you perform is up to you. Remember that you want to impress your manager, but you also
know that the manager’s time is valuable. Therefore, each analysis that you report should offer new information and
conclusions (e.g., avoid repeating the same type of analysis with different coloring, shapes, etc., if the conclusions
drawn are the same.)

Conclusion: Based on the procedures described above, the audit team will pull supporting sales information to substantively
test transactions from the following locations and days:

� This section of the memo can be achieved by using lists or tables, but regardless of the style of presentation, it should
clearly describe which item(s) you are recommending that the audit team look into further (based on all the analyses
above). For each item, you should reference which analysis # the item comes from. The item should be a specific
location on a specific day, or a sample of certain days from a set of observations that exhibit the same unusual trend
based on your analyses above (e.g., if you identify an unusual relationship for Q4 for location #1001, but you cannot
identify one specific day or set of days that is driving the unusual relationship, you may choose to sample from Q4

Using Visualization Software in the Audit of Revenue Transactions to Identify Anomalies 37

Issues in Accounting Education
Volume 33, Number 4, 2018

instead). Remember that it takes time and resources to test each selection, so be strategic in your selections and include
justification for your decisions in this section of the memo.

* NOTE: Appendix B is available as a downloadable Word document.

38 Cunningham and Stein

Issues in Accounting Education
Volume 33, Number 4, 2018

EXAMPLE MEMO TEMPLATE

Souper Bowl Inc.—December 31, 2016
Disaggregated Revenue Analytics

Purpose: The purpose of this memo is to document plausible trends and expectations for disaggregated revenue data and to identify specific days and locations that warrant further substantive investigation.

Data: We obtained a listing of daily sales by location from the client’s IT system. We tested the details for mathematical accuracy, as summarized in the table below:

Total Sales, 2015

Total Sales, 2016

Store Type 1

$

$

Store Type 2

$

$

Store Type 3

$

$

Total

$

$

Procedures: Based on our risk assessment process, we identified the following assertions as significant risks related to revenues/sales:

· Recorded sales occurred.

· Sales are accurately recorded.

· Sales are recorded in the proper period.

Because Souper Bowl’s operations are solely in the state of Maine, we obtained disaggregated data that reports daily sales by store location and store type. Based on discussions with management and our review of the board of director minutes, we are unaware of any new store locations or other major changes to operations during the year. Therefore, we expect prior year to be a reasonable baseline expectation for this year’s revenues (e.g., similar seasonal trends). Because the business can also be impacted by weather conditions, which vary by year, we also perform analyses that consider changes in weather patterns to predict expected changes from the prior year’s sales. We performed several analytics to identify unusual trends compared to the prior year’s sales, taking weather conditions into consideration. The purpose of these analytics is to identify specific observations (or specific sets of observations) to select for further substantive testing. The analytics that we performed are as follows:

·
Visualization Analysis #1: Title

[Provide a description of the relationship you expected to observe in the data, along with screenshots of the visualization results. Clearly identify (using circles, arrows, etc.) the part of the visualization that leads you to believe that a specific location/day is an anomaly. Ensure that all tables and graphics are properly labeled (x axis, y axis, etc.).]

· Results: [In each of the “results” sections, include a brief summary of your findings so that your manager can see (in words) the way that you interpret the visualization screenshots.]

·
Visualization Analysis #2: Title

· Results:

· [The number of analyses that you perform is up to you. Remember that you want to impress your manager, but you also know that the manager’s time is valuable. Therefore, each analysis that you report should offer new information and conclusions (e.g., avoid repeating the same type of analysis with different coloring, shapes, etc., if the conclusions drawn are the same.)

Conclusion: Based on the procedures described above, the audit team will pull supporting sales information to substantively test transactions from the following locations and days:

· This section of the memo can be achieved using lists or tables, but regardless of the style of presentation, it should clearly describe which item(s) you’re recommending that the audit team look into it further (based on all the analyses above). For each item, you should reference which analysis # the item comes from. The item should be a specific location on a specific day, or a sample of certain days from a set of observations that exhibit the same unusual trend based on your analyses above (e.g., if you identify an unusual relationship for Q4 for location #1001, but you can’t identify one specific day or set of days that is driving the unusual relationship, you may choose to sample from Q4 instead). Remember that it takes time and resources to test each selection, so be strategic in your selections and include justification for your decisions in this section of the memo.