Accounting Horizons American Accounting AssociationVol. 29, No. 2 DOI: 10.2308/acch-510682015pp. 423–429

Big Data Analytics in Financial StatementAudits

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

Big 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 isa retired Deloitte partner and a Senior Research Fellow at Rutgers, The State University of NewJersey.

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

Submitted: February 2015Accepted: February 2015

Published Online: February 2015Corresponding 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).

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digitally, and thereby turned into data—a process that Mayer-Schonberger 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-Schonberger 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 seunless 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 apetabyte is.

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

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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.html6 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

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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-Schonberger 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.pdf.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 (Pilaszy and Tikk2009).

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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

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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-Schonberger 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.

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