Big Data Analytics in the Financial Statement Audit A critical examination of the possible value to the auditors
Bachelor thesis Accountancy & Control
Ivar van den Boogert
10562079 29th of June 2016, final draft Professor Brendan O’Dwyer
University of Amsterdam, Amsterdam Business School
Statement of Originality
This document is written by Ivar van den Boogert who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
Currently the position of the financial statement auditor is under pressure, several reports
such as the Green Paper of 2010 pressure for change in the audit profession. In this thesis I
will answer the question whether big data analytics could be beneficial to the auditors, by
examining in which stages of the audit big data analytics could improve the audit in terms of
efficiency, cost reduction and quality. Big data analytics is a hype of the last five years, and
while interesting applications have found in several fields, there does not yet exist an
application for the assurance service industry. My contribution to the existing literature is
twofold. First of all I synthesize existing literature concerning big data in a fashion suitable to
the auditing profession. Second, in the Big Data forum of the journal Accounting Horizons
(2015) authors were highly positive about the possibilities of big data analytics in the
financial statement audit; however, the authors neglect to argue where the possible benefits
could be realized. In my thesis I will try to specify the areas where big data analytics could
indeed prove to be beneficial. I will answer this research question by performing a literature
review. The main finding is that, despite of the potential of big data analytics, the perceived
value for financial statement auditors is ambiguous in terms of efficiency and quality. Cost
reduction is most certainly not achieved at the moment.
Samenvatting
Momenteel staat de positie van de auditor ter discussie, zoals kan worden afgeleid van
onder meer het Groenboek 2010. De druk voor verandering is aanzienlijk hoger dan
voorheen, daarom onderzoek ik in deze thesis of big data analytics een waardevol
hulpmiddel kan zijn voor auditors in de financial statement audit. Big data is een hype waar
veel over wordt gespeculeerd en tevens zijn er interessante toepassingen waargenomen in
verschillende sectoren. Daarom wordt in deze thesis onderzocht, door middel van een
literatuurstudie, in hoeverre big data analytics waardevol is voor auditors in termen van
efficiëntere, goedkopere en kwalitatief betere audits. Mijn bijdrage aan de bestaande
literatuur is tweeledig. Ten eerste vat ik de bestaande literatuur met betrekking tot big data
samen, waarbij de toepassing op het accountantsberoep centraal staat. Ten tweede toets ik
de zeer positieve houding van auteurs die schreven in het forum Big Data van het journal
Accounting Horizons. De auteurs waren uitermate positief over de mogelijkheden van big
data analytics in de financial statement audit, maar beargumenteerden niet in welke fase(n)
van de audit deze potentiële waarde wordt gerealiseerd. Ik probeer dit in deze thesis wel te
specificeren. Uit mijn onderzoek blijkt dat het niet eenduidig is dat er efficiëntere en
kwalitatief betere audits worden gerealiseerd met big data analytics. Lagere kosten worden
hoogstwaarschijnlijk niet gerealiseerd.
Table of Contents
1. Introduction ................................................................................................................ 6 2 Overview of the theory ............................................................................................. 8 2.1 General context of auditing ......................................................................................... 8 2.2 Performing an audit ................................................................................................... 10 2.3 Big Data ........................................................................................................................... 11
3 How to assess the value of big data analytics for auditors ....................... 16 3.1 The Iron Triangle ......................................................................................................... 16 3.2 Audit Quality .............................................................................................................. 17 3.2.1 Audit input ............................................................................................................................... 17 3.2.2 Audit Process .......................................................................................................................... 18 3.2.3 Audit output ............................................................................................................................ 20
4 Examination of the possible value of big data analytics ............................. 22 4.1 Big Data Analytics and Efficiency ........................................................................... 22 4.2 Big Data Analytics and Audit quality .................................................................... 24 4.3 Potential challenges and hurdles ............................................................................ 28
5 Discussion .................................................................................................................. 30 References ........................................................................................................................ 33
1. Introduction
The auditing profession has been under considerable pressure the last fifteen years, as can
be concluded from the Green Paper issued in 2010 and from the paper of Power (2003, p.
379). First, the major fraud committed by the management of Enron, which became known
in 2001, causing billion-‐dollar damage and the bankruptcy of Arthur & Anderson (Brickley,
2003, p. 1). Arthur & Anderson used to be one of the big 5 auditing firms, but after their
failure with regard to the Enron scandal became public knowledge bankruptcy followed
soon in 2002 according to Brickley (2003, p. 2).
Second, since the global financial crisis, users of financial statements and regulators
of the auditing profession have become more sceptical about the role that the auditor fulfils
in the economy (Green Paper, 2010, p. 6). During and after the peak of the recent financial
crisis many financial institutions went bankrupt without any warning from the auditors
(Green Paper, 2010, p. 9). The auditor has the obligation, since 1989, to evaluate the viability
of the auditee (entity being audited) for a reasonable time, which is outlined in the
Statements on Auditing Standards (SAS) No. 59 (AICPA, 2002). The financial crisis made it
evident that auditors were failing to fulfil this obligation. Consequently, several measures
have been put in place: for example in the United States, the Sarbanes-‐Oxley Act has been
implemented and in Europe the Green Paper 2010 has been issued.
Pressure on the audit profession is not only coming from regulatory bodies,
increased competition in the audit profession is another important factor. The margins on
financial statement audits are extremely tight, because the auditing market is highly
concentrated and regulatory bodies have introduced more competition by mandating firm
rotation (Knechel, 2007, p. 387; AICPA, 2013). In order to remain competitive and credible,
the auditing profession would therefore benefit from new cost-‐reducing and quality-‐
enhancing techniques in financial statement audits.
An important innovation in the business environment the last couple of years is Big
Data. A survey performed by Gartner (2015) showed that 75% of the responding companies
expected to invest or were already investing in big data and analytical tools that could be
used to process big data. In an earlier survey, also performed by Gartner (2012), it was
estimated that the total amount invested in big data would reach 232 billion dollar by 2016.
An example of the application of big data analytics is described by Zang, Yang and
Appelbaum where the researchers successfully predict the change of the Dow Jones
Industrial Average stock exchange, using the mood on Twitter as explanatory variable (2015,
p. 425). Another example comes from Walmart, the retail corporation used weather forecast
information to guide their advertising of flashlights (Dezyre, 2013). By successfully using
information about storms and tornado’s and anticipating on a higher demand for flashlights,
Walmart’s was able to sell more flashlights. More examples can be found in the medical and
the insurance industry.
As shown above, there seems to be a variety of possibilities for big data analytics in
the business environment. However, by my knowledge no successful application can as of
yet be found in the assurance service industry, or more specifically, within the auditing
profession. As mentioned earlier, the auditing profession is under pressure and new
techniques might bring some reprieve to the profession. Therefore, I will research whether
big data analytics could be such a technique, by answering the question whether big data
analytics is beneficial for financial statement auditors in a financial statement audit.
The research will take the form of a literature review. My contribution to the
literature is twofold. First I examine whether the several studies that claim that that big data
analytics will be beneficial for the auditor are correct. Second, I synthesize the existing
literature covering the topic of big data with respect to the auditing profession, which can
serve as reference for further research.
Based on the literature study, it can be concluded that the value of big data analytics
for financial statement auditors is not as obvious as originally thought by authors of the Big
Data forum edition in Accounting Horizons. It is ambiguous whether more efficient audits
are achieved, and cost reductions are definitely not realized with the current competition for
data scientist. Furthermore, increased quality of financial statement audits is ambiguous. Big
data analytics has potential to increase quality, but currently the proven positive effects of
big data analytics are not in the scope of financial statement audits. Rather, more specialized
assurance services such as forensic audits could benefit from these new techniques.
The remainder of this paper is structured as follows. In the second chapter
background information is provided on the financial statement audit and how a financial
statement ought to be performed. Furthermore, chapter 2 will describe big data and several
analytical techniques to analyse big data. In chapter 3 the criteria to assess the value for
auditors of big data analytics are presented, which will be the basis for the analysis in
chapter 4. In chapter 5, a discussion of the results of chapter 4 is presented, followed by a
conclusion.
2 Overview of the theory
2.1 General context of auditing
The business environment has become more complex over the years, especially with the
increased amount of data available (Gray, 2002, p. 9). Therefore, the demand for assurance
services has increased to reduce the information risk associated with the more complex
business world (Arens, Elder & Beasley, 2014, p. 26). Arens et al. define information risk, as
the possibility that the information presented is not entirely truthful and could result in
wrong decisions by internal and external users of the information (2014, p. 26). The
(external) financial statement audit is one of those demanded assurance services and in
general when referred to auditing, this type of assurance service is meant (Arens et al.,
2014, p. 29). The ultimate purpose of the audit is to improve the level of confidence placed
in the financial statements by the users of the financial statements (IAASB, 2012).
Arens et al. present the following definition of auditing: “Auditing is the
accumulation and evaluation of evidence about information to determine and report on the
degree of correspondence between information and established criteria. Auditing should be
done by a competent, independent person” (2014, p. 24). Specifically, the financial
statement audit is performed to verify that the statements are in agreement with criteria
such as general accepted accounting principles (GAAP) (Arens et al., 2014, p. 34).
The above-‐presented definition of auditing will be used to explain which role
auditors fulfil in the business environment and how they fulfil it. According to Arens et al.
the auditor must obtain reasonable assurance about whether the financial statements are
free from material misstatements, and thus present a fair view of the underlying economics
of the entity (2014, p. 164). However, the auditor’s assurance concerns the historical
financial statements, which is termed in literature the ‘rear-‐view window check’ (AIPCA,
2015, p. 53). To increase the relevance of the financial statement audit, auditors are
required to make an assessment whether the auditee is likely to continue as an entity for a
certain period of time (AICPA, 1989, p. 2048).
The assurance, however, is given to the shareholders. So even though the auditee
orders and pays for the audit, it is actually executed for the shareholders of the auditee,
which is a rather unusual construction (Teeter, 2014, p. 2).
To enable the auditor to express an opinion about the financial statements, s/he has
to evaluate the auditee following a structured plan that can be referred to as the audit
approach (Arens et al., 2014, p. 441). There are four general phases in the audit identified by
Arens et al., but each audit firm has the liberty to develop their own specific methodology
that ultimately could lead to a competitive advantage (Jeppesen, 1998, p. 520). In the next
section these four phases will be discussed in depth.
Several important terms from the definition of auditing will be discussed in the
remainder of this section. Evidence as defined by Arens et al. is any form of information
used by the auditor to test assertions made by the management of the auditee (2014, p. 24).
Two aspects are important when discussing evidence within auditing, which are
appropriateness and sufficiency. Appropriateness consists of the relevance and reliability of
the evidence collected (Arens et al., 2014, p. 196). A more thorough explanation can be
found in the third section of this thesis. The auditor has several techniques to collect
evidence, for example physical examination (for inventory items) and analytical procedures
such as financial ratios for risk assessments (Arens et al., 2014, p. 199).
Sufficiency is about the question how much evidence the auditor should gather. The
method used by auditors to determine the amount of evidence that should be aggregated, is
the audit risk model (AICPA, 1983). The following equation adopted from Arens et al. is the
basic form of this method; Planned Detection Risk (PDR) = !""#$�!"#$ !"#$% !"#$ (!!")!"!!"!#$ !"#$ !" × !"#$%"& !"#$(!")
The outcome PDR, which indicates the risk that audit evidence fails at detecting
misstatements, is inversely related to the amount of evidence the auditors have to gather
(Arens et al., 2014, p. 279). Hence, a lower PDR requires more evidence. The next
component, AAR, reflects the risk the auditor (in general the managing partner) is willing to
take that the financial statements contain material misstatements after the audit is
completed (Arens et al., 2014, p. 280).
IR refers to the chance the auditor imputes to the possibility of material
misstatement before taking the internal controls into account (Arens et al., 2014, p. 279). CR
refers to the chance that the internal control system of the auditee is unable to detect
material misstatements. The model as presented above is described in the auditing
standards, which characterizes the auditing profession. These standards contain outlines
that dictate, for la large part, how the audit should be performed. Compared to other
professions, auditing is highly regulated.
Another important term is ‘reasonable assurance’. Auditors do not guarantee that
financial statements are free from material misstatements, since it would not be
economically feasible to check every transaction and every item. However, reasonable
assurance is said to be at least 95% sure that the financial statements do not contain
material misstatements.
Related to reasonable assurance is the term material misstatement. Auditors have
the responsibility to detect material misstatements and not every misstatement. Materiality
is highly subjective and is defined in the following manner: something is considered material
when omission or misstatement of the information is likely to change the decision of a
reasonable person (Chewning, Pany & Wheeler, 1989, pp. 80-‐81). Materiality can vary per
auditee, obviously the monetary material level of an organisation such as Apple Inc. is
different from the local fruit retailer.
2.2 Performing an audit
In this section the different phases of the audit are briefly discussed. As emphasized earlier,
while the precise methodology followed by an audit firm can differ from what is outlined
below, the content will generally be similar.
The first phase is the planning phase. In the planning phase the auditors examine
whether to accept the client by analysing the industry of the auditee and evaluate the
reasons for the audit (Arens et al., 2014, p. 231). Furthermore, the auditor achieves a
sufficient understanding of the business and the industry of the client in order to make a
proper business risk assessment, which will determine the AAR and the risk of material
misstatements (Arens et al., 2014, p. 239). Using the information obtained in the planning
phase, a materiality level is determined. Often a percentage of the net income is used as
value to classify irregularities as either material or immaterial (AICPA, IAS 320). At the end of
the first phase, based on the analysis of the client and its industry, an overall audit approach
is designed.
In the second phase, auditors carry out test of controls and substantive tests on
transactions (Arens et al, 2014, p. 442). By testing the specific internal controls of the
auditee, the auditors can determine the control risk, which is the CR in the audit risk model.
In the case of weak internal control more evidence has to be gathered to verify the
monetary amounts of transactions and balance sheet items in the subsequent phase (Arens
et al., 2014, p. 442).
The third phase of the audit consist of two main activities, which are analytical
procedures and tests of details of balances (Arens et al., 2014, p. 184). The analytical
procedures are used to find patterns and plausible relationships between different balance
sheet items. For example, a ratio of accounts receivable to sales is assumed to remain
stable, when deviations are found large enough the auditor should proceed with a test of
detail of balances. Those tests of detail consist of contacting customers of the auditee to
confirm certain accounts receivable amounts. Conversely to the second phase, evidence is
mostly retrieved from third parties (Arens et al., 2014, p. 184).
After the auditors have completed all procedures and acquired all the evidence to
meet the objectives of the audit, an overall verdict is reached. The auditors draw an overall
conclusion in the final phase of the audit, whether or not the financial statements are free
from material a misstatement, which is referred to as the auditor’s opinion (Arens et al.,
2014, p. 70). For simplicity’s sake one of two opinions can be expressed: either a clean
opinion or a modified opinion. When no material misstatements are detected the auditors
will express a clean opinion (Arens et al., 2014, p. 68). When material misstatements are
detected, the auditor will modify his/her opinion. While there are several different types of
modified opinions, for this thesis the broad distinction above will satisfy.
2.3 Big Data Big data and the analytics performed on them have been quite the hype in numerous
industries for the past few years (Deloitte, 2013, p. 2). But, as with any hype, its true value is
not as evident as people might think. In the introduction, two applications of big data
analytics were mentioned. To make an assessment of the possibilities of big data analytics in
the auditing profession, it is paramount to define big data as well as analytics in a fashion
that suits the auditing profession.
Different professionals in different industries use different definitions of big data
(Alles & Gray, 2015, p. 8). The Mckinsey Global Institute employs the following definition: as
soon as data cannot be captured, analysed and stored by the traditional information
systems, it should be labelled as big data (2011, p. 1). Using this definition any firm is
capable of generating big data if, for instance, when trends on Facebook are used as input
for decision-‐making. This type of information falls outside the scope of traditional
information systems according to Yoon et al. (2015, p. 431). The Mckinsey Global Institute
deliberately established a subjective definition, so that every industry has the liberty to
come up with a specific definition that is most suitable for their particular industry (2011, p.
1). Such a vague definition does make it questionable whether big data is fully understood
by anyone. Nevertheless, by synthesizing what is currently known of big data, I try to
establish an accurate description of big data and provide examples of data analytics relevant
for financial statement audits that can be performed with big data.
In the existing literature, definitions of big data can be divided into two broad
categories. The first category of definitions focuses on specific examples of big data (Alles &
Gray, 2015, p. 8). This definition, however, requires specific examples of big data that can be
used in auditing. Due to the lack of research, specific examples are not available, which
makes this definition unusable. In this thesis we will therefore rely on the second category of
big data definitions. This category, according to Alles and Gray, is based on specific
characteristics of big data (2015, p. 8). Those characteristics are commonly known as the 4
V’s. It must be noted that, since big data is a current issue it is likely more definitions and
characterizations will follow. For example, at the Big Data Summit in Boston two additional
V’s were presented (Normandeau, 2013).
Interestingly, the 4 V’s definition is derived from a blog (META group), now a part of
Gartner, that came up with the taxonomy in 2001, which was before the big data hype
actually started (ACCA, 2013, p. 11). META group defined the first 3 V’s, which are: volume,
velocity and variety, as cited by Alles and Gray (2015, p. 8). The fourth V, veracity, was later
added to these 3 V’s. Big data distinguishes itself from ordinary data due to the 4 V’s
(McAfee & Brynjolfsson, 2012, p. 62). The first 3 V’s will be explained in this section, the
fourth V will be explained in the analysis section.
The first V is volume, which refers to the size of the data, as shown in the figure
below. Moffit and Vasarhelyi argue that traditionally information was generated by the
information system of the auditee, but an increasing amount of information is generated by
other sources (2013). The lower left square of the figure below, which represents
transaction data, is currently the most important information for the auditor (Alles & Gray,
p. 10). However the auditee’s information system is not the only data-‐generating system.
Connely identifies the following two additional sources of information: human-‐sourced
information, such as the social medium Facebook, and machine-‐generated information,
which is information from data sensors and mobile tracking sensors (2012). These sources
are an alternative classification of ‘interaction’ and ‘observation’ used in the figure below
(Alles & Gray, 2015, p. 9). Volume also refers to the growth rate of information. According to
Deloitte, the amount of world data increased from 2,5 zettabytes (21 zero’s) to 8 zettabytes
in a five year time span (2013, p. 6). The auditors, when searching for information to test
assertions of the management, might want consider other forms than transactional data.
There will be a more elaborate discussion of the results of this characteristic as well as for
the other characteristics in the analysis section.
The second V, velocity, refers to the rapid pace at which data changes, which means
that information is continuously updated (Alles & Gray, 2015, p. 9). The third V, variety, is
related to the different forms of information that are included in big data. These forms
range from structured internal information, such as transaction history, to unstructured
external information such as social media information (Deloitte, 2013). This unstructured
type of information could be useful for financial statement auditors as described below. The
wide variety of information is a logical consequence of the different information generators
that were identified earlier.
Currently, auditors depend on structured financial information (GAAP-‐compliant
information) as evidence to support the opinion about the financial statements (Cao,
Chychyla & Stewart, 2015, p. 427). Therefore, the ‘new’ information big data adds to the
information currently used by the auditor is unstructured non-‐traditional information
(Moffit & Vasarhelyi, 2013, p. 2). However, without techniques to analyse the new data the
value to auditors derived from big data will be equal to zero. As stated by Alles and Gray,
value from (big) data is determined by the analytics performed with them (2015, p. 13).
Therefore, several data analytical tools are considered below that might be useful to
auditors to analyse big data and hence indicate the relevance of the 3 V’s as explained
above.
Data analytics (also termed business intelligence/artificial intelligence) have been
divided into three levels by Chen, Chiang and Storey in an often-‐cited article. The first level
consists of simple regression techniques on structured databases such as ERP systems of
enterprises (Chen et al., 2012, p. 1166). The second level has been largely developed under
the influence of the Internet, according to Chen et al. (2012, p. 1167). The authors argue
that with the Internet new kind of information came available, which required new
techniques and tools to analyze (2012, p. 1167). The third level is still in its developmental
stage, which incorporates the different information made available by smartphones and
other devices equipped with GPS and other applications (Chen et al., 2012, p. 1167).
Data analytics is the practices of selecting and cleaning data, modelling, and finding
patterns in datasets using data mining tools, which can be used to gain certain insides and
aid the auditor in, for example, risk assessments (Sharma & Panigrahi, 2012, p. 38). Below
several data mining techniques discussed in auditing literature are presented, note that the
list is by no means not exhaustive.
The first tool is neural network (NN); in contrast to standard logistic models, NN uses
non-‐linear models to analyse datasets (Sharma & Panigrahi, 2012, p. 40). By incorporating
complex algorithms multiple pieces of information can be evaluated at the same time
(Calderon & Cheh, 2002, p. 205). In terms of big data, neural network might be able to link
financial and non-‐financial data to find certain patterns or discrepancies (Chen et al., 2012,
p. 1170). A simple example of a discrepancy is higher reported sales, while the amount of
stores decreases. Assuming that Internet sales remain the same, it could indicate suspicious
accounting (Yoon et al., 2015, p. 435). Further, when considering social media, decreasing
popularity, indicated by likes and re-‐tweets, could be an indicator of going concern issues.
In contrast to neural networks the second tool, text mining, is a technique analysing
‘soft’ data rather than financial ‘hard’ data. Different approaches exist to analyse plain text:
searching for specific words, searching for specific word combinations or identifying any
other abnormality in plain text, which is termed text analysis (West & Bhattachrya, 2016, p.
55). This tool might be valuable when considering using social media, emails, management
letters etc. as information source to the auditors, since it consist largely of textual data.
The next tool discussed is process mining, which refers to analysing transactions and
event logs (West & Bhattachrya, 2016, p. 55). When a certain transaction has to be
completed, firms normally have certain protocols that should be followed (Jans, Alles &
Vasarhelyi, 2013). Most mid-‐sized and large firms have Enterprise Resource Planning (ERP)
systems that automatically record the steps taken to complete the transaction. By analysing
this data, auditors could verify whether the actions taken are indeed the actions that should
have been taken (West & Bhattachrya, 2016, p. 55).
Another tool is Benford’s law, which is an example of how suspicious accounts are
identified. The theory is about the probability that certain numbers appear in a certain
order, for example the ‘9’ appears only in 5% of the cases as the first number (Durtschi,
Hillison & Pacini, 2004, p. 19). Benford’s Law, however, has been established in 1938, but
has not been widely accepted as a proven theory, which made it until now a controversial
technique. According to Durtschi et al., Benford’s Law is merely an addition to existing
analytical techniques used by auditors today, without consensus that it actually aids the
auditor in mapping suspicious accounts (2004, p. 21). But its relative ease makes it appealing
to use, one can simply choose an account on the balance sheet/ income statement, which
should be analysed and let the ‘app’ do the work (Cleary & Thibodeau, 2004, p. 6).
All of the above indicates that big data is not easy to define, with all the
complementing and contradictions around. In my opinion Wu, Zhu, Wu and Ding describe
the process of identifying big data accurately by using the Hindu analogy of the giant
elephant. The analogy is about blind men trying to size up a giant elephant, but all of them
have only a limited area they can explore due to natural limitations (2014, p. 98). Restricted
by a limited perspective, each blind man will come to a different conclusion of what they
think they have in front of them (a wall or a tree are examples of the conclusions drawn). Big
data for now can be seen as the giant (growing) elephant, which we are trying to define.
Furthermore, Wu et al. acknowledge that currently no tools exist to fully analyse big data,
the aforementioned techniques only have the potential to analyse elements of big data
(2014, p. 102). The majority of the existing literature is therefore based on expected future
progress in analytics.
3 How to assess the value of big data analytics for auditors
In its most basic form, big data analytics can be seen as a tool for the auditor when
conducting the audit. An audit tool is any technique, manual or computerised, used in the
audit (Curtis & Payne, 2008, p. 105). This section will describe the considerations for the
auditors when they adopt a new audit tool. The umbrella criterion wills that big data
analytics should provide benefits to the auditors in some form. Therefore, this section tries
to define what ‘beneficial’ is for the auditors.
3.1 The Iron Triangle In the article of Vasarhelyi and Romero, the Iron Triangle is used to evaluate whether new
audit technology is beneficial to auditors, and hence should be adopted (2014). The Iron
Triangle will be used as a starting point in this thesis. It consists of three components:
efficiency/effectiveness, cost reduction, and quality (Vasarhelyi & Romero, 2014). As
demonstrated below, efficiency/effectiveness and cost reduction are rather straightforward;
quality, however, is a controversial topic when put in the auditing context (Fischer, 1996, p.
220) and will be discussed in more detail.
Efficiency and effectiveness are often used as complements of one another, which
comes down to the following definition: the degree to which established goals are realized,
and the amount of resources used to do so (IPPF, 2010, p. 2). This is a fairly general
definition and needs further specification in order to be useful for evaluating external
financial audit tools. Efficiency is defined in terms of the resources that are used (Rosenfeld).
One feature of auditing is the labour intensity of the job, with other words the resources
used. Therefore, decreasing the labour hours needed to achieve the same level of assurance
is a good way of defining efficiency without impairing effectiveness, which is maintaining a
certain assurance level. The IAASB further differentiates resources in qualitative and
quantitative resources (2012). For example, hours worked by a managing partner are
different in terms of quality than hours worked by a staff assistant.
Cost reduction is to some extent the logical consequence of fewer resources that are
used. However, there are more considerations with respect to cost reduction, which can be
derived from the diffusion of innovation theory (DOI). For instance, does the new tool
supersede other tools, hence can it replace current tools used (Rosli, Yeow & Eu-‐Gene, 2013,
p. 5). Moreover, education is required to enable auditors to use specific tools (Romero &
Vasarhelyi, 2014), which depends partly on the complexity of the audit tool (Rosli et al.,
2013, p. 5). The cost reduction should be seen in the long term, but the future is often
uncertain. Therefore, the pay-‐off, less resources used, and the cost, for instance of
education, can be hard to estimate (AICPA, 2015, p. 72).
3.2 Audit Quality
The last component of the Iron Triangle is quality. As opposed to other services, financial
statement audits are not transparent. This means that assessing the quality of the audit is
difficult when the audit report is the only outcome to go on (IAASB, 2012). The first
definition of audit quality is from DeAngelo, which underlies many of the other definitions of
audit quality established after DeAngelo (Al-‐Khaddash, Al Nawas & Ramadan, 2013, p. 207).
DeAngelo argues that quality is the joint probability that an auditor will both discover and
report a breach in the client’s accounting system, as cited by Al-‐Khaddash et al. (2013, p.
207).
Furthermore, each stakeholder of financial reporting (auditor, investors, regulators
etc.) will determine the quality of the audit on different criteria (Knechel, Krishman, Pevzner,
Shefchik, Velury, 2013, p. 386). Auditors value the perceived quality of their work by various
stakeholders as well as actual quality, since both will determine the relevance of the
auditor’s work (Al-‐Khaddash et al., 2013, p. 211). Therefore, ‘quality’ should be assessed
from multiple perspectives and not only from the auditor’s perspective.
In the remainder of this section a framework will be presented that tries to capture
a balanced view on different measures of audit quality. The framework will distinguish input
of the audit, the audit process, and the output of the audit when considering audit quality.
Overall, quality is defined by the PCAOB as meeting customer demand (2013).
3.2.1 Audit input
First, the input of the audit. Input refers to what audit firms employ to perform the audit
and achieve the desired result (PCAOB, 2013). From the different inputs for the audit, the
IAASB identifies ‘people’ as most influential on audit quality (2012). Ultimately, the skills and
the personal qualities of audit partners and staff determine the quality of the work
performed (FRC, 2008) as cited by Knechel et al. (2013, p. 388). So what qualities are
perceived as ‘good’ in the auditing literature?
According to Knechel et al., the financial statement audit consists of many
judgement calls that have to be made in the audit process, which in turn determine the
quality of the audit (2013, p. 390). To enable the auditor to make proper decisions, several
personal qualities should be present. One of the most important qualities is professional
scepticism (PCAOB, 2013). Specific examples of qualities of a professional sceptical auditor
are a questioning mind set and the ability to critically evaluate the obtained evidence
(ICAEW, 2013).
Moreover, knowledge, which determines expertise to a large extent (Ashton, 1991,
p. 220), of the industry and the auditee are identified by Knechel et al. as important
contributors to higher quality decision making and hence, higher quality audits (2013, p.
392). Knechel et al. argue that industry-‐specific knowledge could enhance judgement calls
that have to be made by the auditors (2013, p. 392). Hence, being able to analyse industries
more thoroughly has the potential to enhance the quality of the audit.
Furthermore, personnel should be independent and competent to perform high
quality audits (PCAOB, 2013). The independence of the auditor is determined by the
objectivity of the auditor (ICAEW, 2003). One of the threats to the objectivity of auditors is
when they provide other non-‐audit services to a client (Reynolds, Deis, & Francis, 2004, p.
31). Other qualities found in independent auditors are integrity and impartiality of the
auditor (Arens et al., 2014, p. 56). Moreover, personnel should possess the technical
capabilities to execute audit procedures (Khaddash et al., 2013, p. 210).
Another important input factor according to the PCAOB is tone at the top (2013).
Specifically for audit partners and firm managers, a positive relation has been found
between tone at the top and audit quality (PCAOB, 2013). When the top strives for
innovative and high quality audits, it is more likely that staff will do the same (PCAOB, 2013).
3.2.2 Audit Process
The next component of the framework is the ‘process’ of the audit, which refers to the four
phases described in section 2.2. Several important judgements in the audit process
according to auditing literature are: risk assessment, obtaining and evaluating evidence and
review of the work, as cited by Knechel et al. (2013, pp. 393-‐397). Judgements made by the
auditor are structured, semi-‐structured or unstructured, with structured judgements
requiring almost no judgement and unstructured decisions requiring a high level of
judgement (Arens et al., 2014, p. 190).
Knechel et al. identify two potential hazards that impair the auditor’s judgement.
The two hazards are anchoring and adjustment, and representativeness (Knechel et al.,
2013, p. 396). Anchoring and adjustments happen in the ordinary course of the audit. The
expectation is that adjustments to the anchor value are made in the ‘correct’ direction
(Kinney & Uecker, 1982, p. 56). For example, the auditor has an idea about certain book
values, the anchor, and during the audit the auditor finds evidence supporting or
contradicting this expectation, which underlies the adjustment. However, according to
Kinney and Uecker, it does happen that the ‘anchor’ is not sufficiently adjusted because of
sample outcomes (1982, p. 57).
Tversky and Kahneman originally established the definition of representativeness in
1974, as cited by Aston (1984, p. 80). The theory behind representativeness is that auditors
attach a higher probability to uncertain events that are more in line with expectation
(Ashton, 1984, p. 81). The expectation is based on certain resemblance between the
uncertain event and the population, i.e. the representativeness of the uncertain event of the
population (Ashton, 1984, p. 81). For example if we have item A and we want to assess the
probability that it comes from a population A or B, looking at the resemblances with the
population A or B is a logical step to take (Schroeder, Reinstein & Schwartz, 1996, p. 18).
Furthermore, Ashton argues that several factors will influence the likelihood that the
heuristic representativeness occurs (1984, p. 82). The first factor is the correspondence
between the sample and the parent population. When the auditor draws a sample in which
essential properties are more similar with the population, representative of the population,
the auditor will deem this scenario more likely (Ashton, 1984, p. 82). The opposite is true as
well. Therefore, the sample drawn is critical for the judgement of the auditor. In addition,
sample size has an influence on the auditor’s judgement, since in smaller samples extreme
values are more likely (Ashton, 1984, p. 81). However, larger samples, termed
protectiveness, do not guarantee that the above heuristics are prevented (Schroeder et al.,
1996, p. 19).
Another important process in the audit is obtaining and evaluating obtained audit
evidence (Mcknight & Wright, 2011, p. 194). As synthesized by Smith and Kida, audit
evidence has an influence in multiple ways on the auditor’s judgement (1991). Francis
argues that the financial statement audit is only as good as the evidence obtained (2011, p.
135). Arens et al. describe two measures of evidence quality, which are sufficiency and
appropriateness of audit evidence (2014, p. 196). Sufficiency has been explained in section
2.1, whereas appropriateness of evidence deserves further elaboration. According to Arens
et al., appropriateness consists of relevance and reliability of the evidence (2014, p. 196).
Relevance of the evidence refers to the intended use of the evidence in the audit.
For example, when the auditor is testing whether all sales are invoiced, tracking back from
the invoiced sales to shipping records is irrelevant. Relevant evidence would be to consider a
sample of sales shipped and track them back to invoiced sales to see or all are indeed
invoiced. The auditor should therefore obtain evidence that can be used to meet certain
audit objectives (Arens et al., 2014, p. 196).
Reliable evidence has several characteristics as described by Arens et al. (2014, p.
197). First of all, evidence directly obtained by the auditor is considered more reliable than
information obtained indirectly (Arens et al., 2014, p. 197). Second, when information is
obtained indirectly, the source should be qualified to do so. If so, evidence obtained from
outside the firm is regarded as higher quality evidence (Arens et al., 2014, p. 197). Third,
evidence that needs little judgement is regarded more reliable, hence higher quality of
evidence (Arens et al., 2014, p. 197). Finally, timeliness of audit evidence is an indicator of
quality. Arens et al. argue that evidence obtained for balance sheet accounts close to the
balance sheet date is more reliable than evidence obtained a considerable time before the
balance sheet date (2014, p. 197). Timeliness for the income statement is slightly different,
Arens et al. emphasize that evidence should be obtained from the whole year, rather than
only at the end (2014, p. 197).
The last component of the process part of the quality framework is review and
control of the work that has been done, since it is positively related to audit quality
according to Knechel et al. (2013, p. 396). To achieve a high quality review, the PCAOB
argues that the technical competence of the reviewer should be of a considerable level
(2013). Furthermore, reviewers should have enough time to properly review the work that
has been done, which makes a proper planning of the audit essential (PCAOB, 2013).
3.2.3 Audit output
Output is the last component of this quality framework, which will be considered most
important with respect to audit quality by users of financial statements and regulators of the
audit profession (IAASB, 2012). Most indicators are only quantitative and difficult to
transform to criteria. For example, the amount of false positives and false negatives (errors
that arise with going concern opinions) are empirically testable, but hard to define in
qualitative terms. In the end, it depends on how effectively the audit process is executed,
which is determined by the people performing the audit. However, the IAASB found an
important qualitative aspect of ‘output’, which is transparency of the audit performed
(2012). They argue that when the audit is more transparent, it could be considered of higher
quality by different stakeholders (2012). Further, the time-‐lag between the year-‐end and the
issuance of the audit report decreases the relevance for decision making, which decreases
the perceived usefulness of the financial statement audit (Chan & Vasarhelyi, 2011, p. 152).
Therefore, decreasing the time-‐lag would increase the quality, since customer demands are
better satisfied.
4 Examination of the possible value of big data analytics In this section I will assess if and how big data analytics could be valuable to the auditors in
different phases of the financial statement audit. In this section I will examine whether big
data analytics can realize more efficient financial statement audits without impairing
effectiveness, and whether higher quality audits can be achieved. Furthermore, challenges
and obstacles for big data analytics will be discussed.
4.1 Big Data Analytics and Efficiency As described in section 3, big data analytics could be valuable to auditors when it makes the
audit more efficient. This can be achieved by reducing labour hours in quantitative and
qualitative sense. Regardless of specific phases of the audit, it is quite straightforward that
the amount of qualitative resources used is unlikely to decrease when big data analytics are
employed in the financial statement audit. Cao et al. argue that currently there already is a
shortage of people with sufficient knowledge of the auditing process and data analytics
(2015, p. 427), a shortage which is unlikely to decrease with the emphasis on data analytics
in multiple industries at the moment. Data analytics is a complex field of study and requires
sufficiently educated employers (specialists often referred to as data scientists). Hence the
resources used in qualitative terms will increase in order to actually enable audit firms to
use big data analytics in the financial statement audit.
Moreover, the auditing profession is just one of the many professions who use or
intend to use forms of big data analysis. This phenomenon is demonstrated by the search
results on several job applications websites, where big data analysts are in high demand by a
variety of firms. Moreover, since auditors essentially fulfil a public service as explained in
section 2.1, wage-‐growth potential will be significantly lower than with firms such as Google
and Microsoft, thus making audit firms (even) less interesting to potential employees. The
above implies that the competition for data scientists is fierce, consequently increasing the
cost of hiring these data scientist, increasing the cost of the audit. In my opinion,
outsourcing the big data analytics part of the audit could be a way to prevent high cost.
However, hardly any research has been conducted in this area, which makes it difficult to
assess the possibilities and consequences of outsourcing parts of the external audit in terms
of efficiency and quality.
However, the amount of resources used could be reduced if big data analytics has
the potential to replace existing labour-‐intensive parts of the financial statement audit. In
phases two and three one of the main activities is gathering sufficient appropriate evidence
to test assertions made by the auditee’s management. For example, testing the value of the
inventory from a retailer is generally done by obtaining evidence by physical examination1,
which is a labour-‐intensive job (Arens et al., 2014, p. 205). Brown-‐Liburd and Vasarhelyi
argue that using the information of radio frequency identification (RFDI) chips for validating
inventory could make the process more labour efficient (2015, p. 2). The authors, however,
neglect to explain how it could be realized. A possible explanation could be that more
efficient inventory validation is achieved because the auditor can rely on the generated
numbers by RFDI. As argued below in the evidence section, RFDI is a sensor technique
automatically generating information, requiring less verification. It does require that the
auditee uses and stores RFDI data, which is not self-‐evident for every business. Also, RFDI
technology is not a recent development, as it has been around for quite some time.
Nevertheless, there do not seem to be many applications already by financial statement
auditors. Maybe the technology does not satisfy the required assurance level, but pilot
studies should be able to identify the reasons.
Furthermore, assessing the internal control of the auditee is an important task in the
second phase of financial statement audit. Currently, the internal control is validated by
techniques such as reperformance, observations and examination of documents (Arens et
al., 2014, p. 331). These techniques are labour-‐intensive and often have to be performed in
combination with one another in order to suffice in terms of sufficient evidence (Arens et al.,
2014, p. 204). A technique such as process mining, described in section 2.3, has the ability to
replace the above activities, and greatly improve the effectiveness and efficiency of the
control procedures since it requires less personnel (Jans, Alles & Vasarhelyi, 2014, p. 1752).
The authors argue that because of the wide application of (ERP) systems, electronic
verification of the internal processes is more easily done, because the ERP system
automatically records procedures (not) undertaken by personnel (2014, p. 1754). The
strength of this technique lies in the fact that automatically generated data is involved in the
analysis (Jans et al., 2014, p. 1753). Since automatically generated information is hard to
alter, auditors can rely more easily on this data without having to verify every source.
However, process mining is not a widely applied technique in the financial
statement audit to validate the internal control (Stewart, 2015, p. 112). A possible
explanation could be that using a technique such as process mining would require superior
knowledge and experience of the business process of the auditee (Jans et al., 2014, p. 1756).
1 For example by counting the inventory of a retailer, however differences exist across industries audited
Only experienced auditors would be capable of using the technique effectively, which means
more qualitative resources are needed for an audit, which increases the cost of performing
an audit. Considering the low margins on financial statement audits, this would be
undesirable. Procedures such as observation/reperformance can be more easily performed
by inexpensive staff members (inexperienced personnel) (Arens et al., 2014, p. 331).
Also, complete consensus about the auditor’s responsibility with respect to internal
control does not exist. In the United States for example, auditors have the responsibility to
assess the internal control for firms satisfying criteria of the SOX, but for smaller firms and in
Europe there is no such regulation. Therefore, the importance of innovative techniques such
as process mining could be undervalued by external auditors, since the current techniques
suffice and there is not enough pressure from the environment (stakeholders, regulators
etc.) for auditors to consider expensive new techniques.
A better internal control assessment however, is important to the control risk
component of the audit risk model, outlined in section 2.1. Techniques which execute better
and more efficient internal control activities would decrease the control risk, thus
decreasing the amount of evidence that has be accumulated and thus less work that has to
be done. However, risk assessments are rather subjective, which means that reductions in
the risk are not translated 1 to 1 in less evidence, making a solid argument impossible.
4.2 Big Data Analytics and Audit quality
Aside from different phases in the audit, the input of people is an important determinant for
the audit quality. In section 4.1 I argued that more specialists are needed in order to enable
audit firms to incorporate big data analytics in the financial statement audit. Ultimately, a
higher ratio of specialists to staff in the audit engagement team would increase the quality
of the audit (He, 2015, p. 1686). Big data analytics, however, have almost no influence on
the personal qualities of the auditors identified in section 3. Rather, auditors will have to
possess more qualities to use big data. Professional scepticism, one of the most important
qualities according to auditing standards, is hardly influenced by big data analytics.
Moreover, big data analytics demands more professional scepticism from the auditors. For
example, the auditors make several risk assessments during the audit, such as the possibility
of material misstatements, due to error or fraud. As described in section 2.2 Benford’s Law is
an example of a tool that identifies suspicious transactions/accounts. One of the most
important consequences of big data analytics, such as Benford’s Law, is the ability to analyse
the whole population. When analysing millions of transactions, the amount of ‘positives’ will
be substantial (Stewart, 2015, p. 111). Positives in this case are the amount of suspicious
transactions found by the tool. Stewart argues that determining which ‘positives’ should be
further investigated depends on the judgement of the auditor, requiring a certain degree of
professional scepticism and knowledge about the auditee and the industry (2015, p. 111).
Wrong decisions could lead to many ‘false positives’, which could lead to lower quality
financial statement audits.
Industry and auditee specific knowledge could be improved using big data analytics
(Zhang, Yang, Appelbaum; Cao et al.; Yoon et al., 2015), however the general way auditors
become industry specialist is by spending considerable time in a certain industry. The
aforementioned authors apparently think that big data analytics could be some kind of
shortcut, but do not explain how this would work. The same holds true in becoming more
knowledgeable of the auditee, a longer tenure is positively related with auditee specific
knowledge. This fact is often a reason why first time audits are more expensive than
subsequent audits by the same auditor (Simunic, 1980). The knowledge of the industry and
auditee are important determinants with respect to the inherent risk component in the
audit risk model of section 2.1. This type of risk assessment improves with big data analytics
according to Yoon et al., but they do not give a solid argument to support their claim.
A possible positive influence of implementing big data analytics would be on the
‘tone at the top’. When management of the firm incorporates an innovative technique such
as big data analytics into the audit, it is more likely that the rest of the personnel will follow
the good example. Hence, employees will be more motivated to strive for high quality, since
the top of the firm does the same. However, strong empirical prove for this relationship is
absent. Nevertheless, it is imaginable that when your manager puts in more effort, you will
be more willing to do the same than when the manager is rather reluctant in striving for
improvement.
In the third section of this thesis, several characteristics of high quality evidence
were discussed. Francis argues that high quality evidence is of significant influence on the
quality of audits (2011, p. 135). First of all there’s the issue of reliability of audit evidence.
The source of the information is identified as critical in the in the assessment of reliability of
evidence (Brown-‐Liburd & Vasarhelyi, 2015, p. 6). Brown-‐Liburd and Vasarhelyi argue that
because big data is partly generated automatically, the chance of alteration is relatively
smaller than manually generated data (2015, p. 6). This inability to alter data is enhances the
reliability of big data as audit evidence. However, since so many different sources produce
information which is accumulated and called big data, verifying every source would be
cumbersome and even impossible, making big data evidence less reliable. This problem
characterizes big data, and is the fourth V veracity.
Another characteristic of high quality audit evidence is objectivity. Big data will
demand sufficient judgement in what to conclude from the available information and what
is correct or incorrect, as demonstrated by the example of fraud risk assessment above.
Because the whole population is obtained, numerous positives will be present. As described
earlier, the only way to cope with this problem is for the auditor to make a subjective
judgement what to take away from the results. In favour of big data as audit evidence is the
velocity of data, which will provide the most up-‐to-‐date information and evidence. For
instance, when evidence has to be found about accounts receivables, big data could provide
the most current information about a specific creditor.
Moreover, high quality evidence is determined by the sufficiency of aggregated
evidence, as described in section 3. Traditionally the questions that had to be answered with
respect to sufficiency were: adequate sample size and which items to select from the
population (Arens et al., 2014, p. 199). Strongly in favour of big data analytics is the fact that
the whole population is tested, which means no need for sampling and related difficulties.
However, the curse of too much knowledge could in the end impair audit quality because of
the limited amount of information humans, and thus auditors, can process (Knechel et al.,
2013, p. 387).
Another argument in favour of big data, related to the fact the whole population is
tested, is the reduction of two other decision heuristics in the audit process described in
section 3. Both the heuristics adjustment and anchoring, and representativeness occur
because of sample outcomes. When the whole population is tested, decisions are no longer
made on the basis of sample outcomes, thus reducing the chance that auditors are
subjected to decision heuristics in the audit process.
Besides the need for more judgement in evaluating audit evidence obtained with big
data analytics, another drawback can be derived between the difference of current use of
statistical tools and the intended use with big data statistical methods. In the financial
statement audit linear regression models are used often used for analytical review, which
are based on a causal relationship derived from experience and knowledge (Debrencey &
Gray, 2014). An example of such a review method is the relationship between revenues and
cost of goods sold, which is assumed to be fairly stable. Large fluctuations would therefore
be suspicious. In this case we speak of directed data mining (Alles & Gray, 2015, p. 15). The
idea behind big data analytics is undirected data mining, which means that no such causal
relation is established (Chent et al, 2012, p. 1168). This means that decision will be made
based on correlations in data sets (Alles & Gray, 2015, p. 19). According to Cukier and
Mayen-‐Schoenberger, the possible consequence is that auditors will abandon the practice of
finding out why certain relations exist, and simply assume that certain events occur
simultaneously as cited by Alles and Gray (2015, p. 23). For auditors to base their opinion on
correlation instead of causation strokes with their natural conservative attitude (Alles &
Gray, 2015, p. 27). Also, in my opinion it is a lot harder for the auditor defend his/her
opinion based on correlations and no common sense, making it not that appealing to use.
Above, big data analytics were discussed with respect to the audit process and input
and how it could increase the quality of the audit. The last part of the analysis of the
possible value of big data analytics considers the output. The main criterion is the
transparency of the audit process, which is important to various stakeholders to assess the
quality of the financial statement audit. As mentioned several times, big data and analytics
are rather complex subjects. Experts like data scientist already have trouble identifying
opportunities and limits of big data, which makes it unlikely that novices will have a good
understanding of these analytics. Therefore, big data analytics would decrease transparency
rather than increase transparency of the financial statement audit process.
However, big data analytics does nurture more technology-‐driven audits and forces
auditors to rethink their traditional approach (Chan & Vasarhelyi, 2011, p. 153). According to
Chan and Vasarhelyi, more automation in the audit could be the consequence of
incorporating more technology. By automating certain parts of the audit, auditors could
satisfy the demand for more timely or even more audits during a year, hence more real-‐time
assurance (Chan & Vasarhelyi, 2011, p. 153). This would increase the relevance of the
financial statement audit to users of financial statements.
Francis argues that from the most important determinant of audit quality when
looking at output, is the amount of wrong audit reports issued (2011, p. 127). Audit reports
are considered wrong when a clean opinion is given when a qualified opinion should have
been issued and vice versa (Knechel et al., 2013, p. 397). However, it is hard to transform
this determinant for audit quality into a criterion, which was possible with other
determinant as demonstrated in section 3. Nevertheless, an important argument can be
derived from this quality determinant against the use of big data analytics. From several
studies it can be concluded that the very few audits actually go to trial because of wrong
decisions of the auditor, making it questionable whether improvements are actually needed
(Knechel et al., 2013, p. 398). An explanation for the low amount of trials could be the
buyout of the auditee by audit firms when something does go wrong (Allles & Gray, 2015, p.
15). This makes it particularly hard for outsiders, to assess this potential need for higher
quality audits from the perspective of auditors with respect to output.
4.3 Potential challenges and hurdles
Several general difficulties in using big data analytics in the financial statement audit exist.
Below, several challenges and hurdles are explained, but the list is by no means exhaustive.
First of all, much of the literature discusses the benefits of big data under the assumption
that analytical tools exist to analyse big data. The main issue here is that no such technique
is available to analyse big data in a way that is valuable to the auditors. The large variety of
data-‐generators makes it impossible to transform all data into an analysable format (Brown-‐
Liburd, Issa & Lombardi, 2015, p. 458). Techniques that were discussed in section 2.3 have
the potential to analyse small parts of big data, but more research is needed. For instance
more research in the area of ‘Mapreduce’, which is able to analyse several formats at the
same time (Varian, 2014, p. 4). However, a solution could be a partial implementation of big
data analytics.
Moreover, the highly regulated environment of the audit profession makes it rather
difficult to implement new techniques and tools. For example, I argued in section 4.2 (on
reliability and appropriateness of audit evidence) that one possible implementation of big
data was that it could serve as audit evidence. However, the existing standards and rules
limit the possibilities of new types of evidence. Currently, the new Data Audit Standard is
being implemented (Titera, 2013, p. 327), but its precise consequences remains as of yet
unclear.
Another challenge lies in the education of auditors. At this moment, I have had two
years of education in ‘accounting (auditing) and control’, but education in statistical
techniques does not go further than standard linear regression. Furthermore, in audit
courses, only the traditional techniques and audit approaches are discussed, without even
mentioning the current research and progress in the area of new techniques. This inevitably
leads to gaps in the knowledge of future auditors when it comes to such new techniques.
Also, as described in section 4.1, using analytics properly would require more experienced
personnel, demanding a change in the current educational curriculum.
Also, several privacy concerns arise with the use of big data. There exist several
examples of concerns over privacy, such as whether or not corporate emails are property of
the firm or if they should fall under the protection of privacy laws. Big data will go far
beyond the boundaries of corporate email, especially when we consider data from social
media and other Internet applications. However, an example found the newspaper ‘NRC
Handelsblad’ demonstrates the pace at which opinions can change. Chris Hensen describes
an example of a car insurance company (ANWB) that intends to use a tracking device in the
car, which will measure average speed, breaking time etc. of the driver (2016, p. 10). This big
data will than be analysed and car drivers who drive properly and safely are granted a
reduction of insurance fees (Hensen, p. 10). The interesting thing about this example is that
over half a year ago, another Dutch insurance company (Achmea) had an almost identical
idea, unleashing a wave of criticism about privacy concerns. The announcement of ANWB,
however, does not seem to suffer the same fate. It must be noted, however, that the
industries are quite different, which might account the ways in which the media and the
public reacted.
Related to the privacy concern is the possible reluctance of auditees to cooperate
with the audit firms. In general, auditors have to ask for certain information and definitely
do not receive all the information they need up front. On an auditing career day, several
senior auditors emphasized that firms certainly will try to hide certain events, either by
burying information or by omitting information when it’s not specifically asked for. When we
consider the massive amount of information auditors are asking for with respect to big data,
it seems highly unlikely that auditees are willing to provide such information.
5 Discussion In this section the arguments of chapter 4 will be evaluated and at the end of this section a
conclusion will be presented. In chapter 3, three criteria were described and explained,
namely efficiency/effectiveness, cost reduction and audit quality, which are critical in
assessing the value of any tool, such as big data analytics, for the financial statement
auditors.
The first two criteria, efficiency and effectiveness, and cost reduction, are to some
degree interrelated because cost reduction would be the logical consequence of more
efficient financial statement audits. In chapter 4, the analysis whether big data analytics
could increase efficiency in the financial statement audit focused on the required amount of
labour hours. Big data would surely demand more specialists in the audit engagement team,
so in qualitative terms efficiency is unlikely to decrease. In quantitative terms efficiency
improvement is still ambiguous, since big data analytics for now seems to be more of an
additional tool for the auditors rather than an essential tool, at least for the time being.
From the above it becomes clear that cost reduction is not as obvious as one might
think. Several authors who wrote in the Accounting Horizons Big Data forum edition are
extremely positive with respect to efficiency and cost reduction that big data analytics would
bring to financial statement audits. However, they neglect to involve competition of firms
such as Google and Microsoft for sufficiently educated statisticians (data scientists). To
compete with such firms, compensation for data scientists will be high and cost reduction
rather unlikely.
Quality of the financial statement audits is a controversial topic. Since DeAngelo
defined audit quality in 1981, numerous papers have been written about this very topic. The
quality framework described in chapter 3 led to interesting results in chapter 4. First of all,
big data analytics have only a minor influence on the ‘input’ for audits. The only possible
positive influence of big data analytics is found with ‘tone at the top’, but tone at the top is
surely not the most important factor described in chapter 3 with respect to input. Once
again, authors who published in the Big Data forum are very positive, stating that big data
analytics will improve the auditor’s knowledge and will lead to more industry specialists.
Surprisingly, these statements are not backed up with solid argumentation and evidence,
which makes it a rather weak case.
However, in the ‘process’ the second component of the quality framework of
chapter 3, big data analytics does have a certain potential. Big data analytics could serve as
evidence, satisfying the important criteria of high quality evidence as outlined in chapter 3.
Furthermore, techniques such as process mining are likely to lead to an objective evaluation
of the internal control of the auditee and with the growing amount of firms with ERP
systems application of process mining is becoming more likely.
However, an important activity performed at various stages of the audit is the
several risk assessments made by the auditor. In section 2.1 the fraud risk assessment, going
concern assessment and the components of the audit risk model have been explained. The
auditing literature is mainly focused around the big data analytics and fraud risk assessment,
and a strong case is made for the positive results of big data in the fraud risk assessment.
However, detecting fraud is not widely accepted as the job of the auditor in the financial
statement audit. Moreover, several authors (Yoon et al., 2015) have the habit to refer to
‘improved risk assessments’ without specifically referring to a type of risk assessment. In my
opinion they refer to the fraud risk assessment, but the relevance to financial statement
audits is considerably less than for example AAR, IR and CR.
The last component described in the audit quality framework was output.
Transparency has always been the main issue with respect to the financial statement audit,
a reason for the growing criticism that audit firms did not document their steps thoroughly
enough and regulatory bodies had difficulties with monitoring audit firms. Big data analytics
will surely fuel this debate, since big data analytics are complex, require more judgement
and thus leave less space for objective evaluation. The literature around the output
component led to an interesting insight, which is the low amount of trials with respect to
wrong audit opinions, which becomes even less meaningful compared to the amount of
audits performed each year. The Enron case described in the introduction is an extreme
example.
Taking all this into account, I’m of the opinion that big data analytics in the financial
statement audit has been overhyped, demonstrated for instance by Accounting Horizon’s
Big Data forum. Big data analytics does have potential in several areas, but the value for the
auditors in the current state of financial statement audit is negligible. However, which is also
one of the limitations in this thesis, big data analytics is a very recent development. Much
research is currently being undertaken, which could alter literature review performed in this
thesis. Another limitation, because of the novelty of the subject, is the fact that I had to rely
on some unpublished work, because the most recent developments around big data
analytics are not yet published in scientific journals. Furthermore, this thesis is built around
a theoretical framework of how financial statement audits ought to be performed, which
isn’t the same as the real-‐world practice of auditing. Hence, a suggestion for future research
would be to perform case studies with big data analytics. Also, more research should be
conducted in defining big data in a way that’s suitable to auditing. Currently, transaction
data is the most important data for auditors, essentially making anything beyond transaction
big data. When a better definition of big data is established, future research is likely to be
more valuable and better directed, so that the auditing profession might benefit from it.
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