Managerial Auditing Journal

Financial fraud detection and big data analytics – implications on auditors’ use of
fraud brainstorming session
Jiali Tang, Khondkar E. Karim,
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Jiali Tang, Khondkar E. Karim, (2018) "Financial fraud detection and big data analytics – implications
on auditors’ use of fraud brainstorming session", Managerial Auditing Journal, https://doi.org/10.1108/
MAJ-01-2018-1767
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Financial
Financial fraud detection and fraud detection
big data analytics – implications
on auditors’ use of fraud
brainstorming session
Jiali Tang
Department of Accounting, University of Hartford,
West Hartford, Connecticut, USA, and
Khondkar E. Karim
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Department of Accounting, Robert J. Manning School of Business,

University of Massachusetts Lowell, Lowell, Massachusetts, USA

Abstract
Purpose – This paper aims to discuss the application of Big Data analytics to the brainstorming session in
the current auditing standards.
Design/methodology/approach – The authors review the literature related to fraud, brainstorming
sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by
applying Big Data analytics at different steps.
Findings – The existing audit practice aimed at identifying the fraud risk factors needs enhancement, due
to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such
concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data
analytics into brainstorming can broaden the information size, strengthen the results from analytical
procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data
tools at every step of the brainstorming process, including initial data collection, data integration, fraud
indicator identification, group meetings, conclusions and documentation.
Originality/value – The proposed model can both address the current issues contained in brainstorming (e.g.
low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

Keywords Big data analytics, Brainstorming session, Fraud detection

Paper type Research paper

1. Introduction
Financial fraud remains one of the most discussed topics in accounting literature. According
to Cotton (2002), the financial scandals of Enron, WorldCom, Qwest, Global Crossing and
Tyco resulted in approximately $460bn loss. The detection of financial fraud, therefore, has
become a critical task for accounting practitioners.
In the fraud triangle put forward by Cressey (1973), three factors determine the likelihood
of fraud occurrence, including pressure, opportunity and rationalization. The core of these
factors lies in people’s belief and behavior. Due to the unpredictability and uncertainty in
fraudsters’ incentives and techniques, fraud detection requires the skillset that encompasses
both diligence and judgment. Managerial Auditing Journal
Although the current auditing standards intend to provide a comprehensive guideline © Emerald Publishing Limited
0268-6902
that governs the process in fraud examination, the actual implementation is conducted on a DOI 10.1108/MAJ-01-2018-1767
 MAJ case-by-case basis that heavily relies on auditor judgment thus leads to inconsistent success
rate. For example, AS 2401 contains explanations on understanding fraud, exercising
professional skepticism, responding to fraud risk, communicating with the management
and documenting auditors’ comments[1]. However, these standards might be interpreted
and executed differently by auditors, and they cannot effectively address the fraud risk
embedded in nonfinancial information (e.g. meeting content, management conversations,
tone at the top, language in annual reports, etc.) without competent personnel and
supplementary tools.
A recent trend is to apply data analytics to fraud detection. An example is the use of data
mining techniques aimed at finding patterns from journal entries to identify fraud
(Debreceny and Gray, 2010). While the study contributes greatly to the research on fraud
detection method by integrating data and technology, it still ignores the non-numerical
signals from parties who prepare the financial statements.
To cope with the flaws mentioned above and provide an addition to the fraud detection
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toolset, in this paper, we discuss the potential use of Big Data analytics in identifying fraud
risk, particularly in the brainstorming sessions required by current auditing standards. In
response to the accounting scandals in the early 2000s, an auditing statement aimed at
detecting fraud (SAS No.99) was established by AICPA. The standard specifically requires a
brainstorming session to be held by auditors to identify fraud-related risks. More recently,
the idea of brainstorming has been added to multiple sections of the auditing standards.
AICPA mentions in AU-C 240 that the engagement team is required to brainstorm and
discuss areas potentially subject to material misstatement, management’s fraudulent
reporting and asset misappropriation. AU-C 315 of AICPA considers such discussions as
auditors’ risk assessment activities. Similar requirement is also included in PCAOB
standards (AS 2110).
As the purpose of brainstorming is to gather intellectual input from different individuals
and inspire new ideas, such format seems to be an effective way of processing unstructured
data and capturing anomalies While we believe that brainstorming sessions can enhance the
overall fraud assessment, the execution can be quite challenging given the complexity in
conducing quality discussions and the limitations contained in the format of brainstorming
(e.g. production blocking)[2].
The integration of Big Data analytics provides one solution to improving the
performance of the brainstorming sessions. First, Big Data can enlarge the information base
used in brainstorming. By combining or aggregating different types of information through
Big Data tools, auditors can have access to a database that contains both the financial (e.g.
accounting record) and nonfinancial information (e.g. news on management, board meeting
minutes, contract details, etc.) of the client firm. Second, Big Data can enhance the
information content. When conducting analytical procedures, auditors can efficiently
compare data across time and industries to quickly identify anomalies. A larger sample data
(or the full population) will also increase the accuracy of the prediction models. Thus, Big
Data can generate reliable results that more precisely point to the fraud risks. Finally, Big
Data can facilitate the communications among the engagement team members, or even
between the predecessor and successor auditors. For example, during the brainstorming
sessions, auditors can use electronic devices to record their thoughts while reading other
members’ comments simultaneously. In addition, Big Data can also incorporate the industry
expertise of individual auditors by selectively displaying relevant information (e.g. news,
industry index, competitors) on the monitor to inspire new ideas. Overall, the application of
Big Data analytics in brainstorming sessions allows auditors to use unstructured data and
analyze fraud factors closely related to the fraud triangle. The larger information set and
 more reliable evidence help ensure quality discussions, and the computer-based setting can Financial
reduce production blocking and redundant process. fraud detection
To strengthen the practical contribution of our arguments, we propose a model that can
be adopted by the audit team engaged in the brainstorming sessions. We suggest a six-step
system that includes initial data collection, data integration, fraud indicator identification,
group meetings and discussions, drawing conclusions and documentation. We discuss the
possible application of Big Data analytics to each step of the process. The model offers an
innovative and potentially effective approach of conducting brainstorming and identifying
fraud risks.
The remainder of the paper is organized as follows. Section 2 presents the background of
fraud detection, Section 3 explains the brainstorming sessions in current auditing standards,
Section 4 discusses the application of Big Data analytics, and Section 5 concludes.
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2. Financial fraud and issues in its detection

Based on different behavior and consequences, Reurink (2016) classifies financial fraud into
three types: financial statement fraud, financial scams and fraudulent financial mis-selling.
Of the three categories, financial statement fraud is closely related to bookkeeping and day-
to-day transactions as it involves intentional falsification of financial records, inappropriate
application of accounting standards, earnings management and other activities that create
information asymmetry (Rezaee, 2005). Errors and misrepresentations in accounting record
often cause financial restatements. GAO (2002) reports that approximately 10 per cent of the
public firms underwent financial restatements during 1997 to 2002. For the other two
categories, financial scams refer to the deceptive schemes designed to unlawfully obtain
other parties’ assets, and financial mis-selling includes deliberately marketing financial
products to the unsuitable clients (Reurink, 2016).
Financial fraud has led to tremendous loss over the years. Karpoff et al. (2008) show that
financial misconduct is penalized by both the legal system and the market, and the latter
usually imposes a significantly stronger penalty. Of the 585 firms investigated in their
study, the loss incurred in the market due to reputation damage is 7.5 times higher than the
legal penalty. GAO (2002) finds that the announcement of restatements triggers a negative
market reaction of
10 per cent. Stockholders also respond unfavorably to corporate crimes,
especially for companies with similar record previously (Davidson et al., 1994). The
consequences of fraudulent activities are not limited to financial loss. Tian et al. (2016) show
that venture capitalists will face difficulty in obtaining future IPO business if they overlook
the fraud in prior cases.
The factors that determine fraud likelihood has been discussed extensively in the
literature. Theoretically, Cressey (1973) argues that an individual or organization needs to
possess the pressure, opportunity and rationalization (the fraud triangle) to conduct
fraudulent activities. Later research proposed a fourth factor, capability, and presented the
fraud diamond structure (Wolfe and Hermanson, 2004; Boyle et al., 2015). Power (2013)
states that fraud has been considered as a risk rather than an event and discusses the
current shift to a universal acknowledgement of fraud risk within organizations. Other
factors that have contributed to fraudulent activities include morality (Morales et al., 2014),
competition (Bolton et al., 2007), conflicts of interest (Mehran and Stulz, 2007), option-based
compensation (Burns and Kedia, 2006; Efendi et al., 2007), clawback adoptions and insider
sales (Fung et al., 2015), the knowledge and trust embedded in accounting positions
(Dellaportas, 2013) and lack of scrutiny on internal control systems (Baker et al., 2017).
Due to the uncertainty and variability in these factors, fraud detection has become a
challenging task that requires both talent and technology. While current auditing standards
 MAJ (AU 316) put a strong emphasize on exercising professional skepticism, they are perhaps
insufficient in addressing the amount of ambiguity and variations in different individuals
and organizations. For example, Davis and Pesch (2013) find that the auditors cannot treat
fraud detection uniformly without adjustment, and that the detection mechanisms are
influenced by organization types and individual social awareness. The incentive to
investigate fraud also depends how the reports are framed (Huerta et al., 2012). Trotman and
Wright (2012) show that auditors might evaluate external evidence related to fraud based on
whether the evidence clashes with business goals, resulting in potential subjectivity.
Further, internal auditors can experience stress from reporting to audit committees and alter
the level of fraud risk when reporting (Norman et al., 2010). Knapp and Knapp (2001) show
that auditors’ experience matters in adopting analytical procedures during fraud
examination.
To deal with the complexity in fraud detection and enhance effectiveness, different types
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of technologies are developed and implemented. Some of the highly discussed technology
applications include using data mining techniques to find patterns from financial records
(Debreceny and Gray, 2010; Grabski, 2010; Gray and Debreceny, 2014), using descriptive
data mining tools to identify internal fraud risks (Jans et al., 2010), using outlier techniques
to flag fraudulent insurance claims (Capelleveen et al., 2016), using computer algorithms to
detect abnormal stock price movement (Williams, 2013) and applying natural language
processing (NLP), queen genetic algorithm (QGA) and support vector machine (SVM) to
analyze annual reports (Chen et al., 2017).

3. Brainstorming sessions in current auditing standards

Statement on Auditing Standards No. 99 (SAS 99) is a set of standards established in 2002
after the occurrence of the accounting scandals. The statement aims at more effectively
preventing and detecting fraudulent activities. One of the highlights of SAS 99 is the
requirement of a brainstorming session held by the audit team members. Since then, the
standards have been updated and the idea of implementing a brainstorming session has
been shown in different parts of AICPA (AU-C 240 and AU-C 315) and PCAOB (AS 2110)
standards. According to AU-C 240, the content of the brainstorming sessions should include
the occurrence of potential material misstatement, the methods that management might use
to commit fraud and the possibility of asset misappropriation. In particular, the session
should address management behavior in the context of the fraud triangle (incentive,
opportunity and rationalization) and a high degree of professional skepticism must be
maintained[3]. Trompeter et al. (2013) place brainstorming in the framework of auditors’ role
in detecting fraud and argue that auditors should consider fraudulent act, concealment and
conversion methods during the brainstorming sessions. Based on a survey conducted on 22
auditors by Bellovary and Johnstone (2007), the brainstorming sessions can be summarized
as a four-step process, including client information review, fraud triangle analysis, fraud
likelihood assessment and audit response indication.
Prior studies have documented several benefits of conducting brainstorming. Osborn
(1957) argues that group interactions create stimulation and synergy. Carpenter (2007)
concludes that brainstorming auditors can generate new and quality ideas on fraud
detection and higher risk assessment relative to individual auditors. Brazel et al. (2010) state
that fraud risk factors (incentive, opportunity and rationalization) can more effectively
indicate fraud likelihood when the brainstorming is high-quality. Hoffman and Zimbelman
(2009) find that both strategic reasoning and group brainstorming facilitate the audit
procedure modifications.
 However, there is also evidence that group interactions cause process loss (Hill, 1982; Financial
Straus et al., 2011). Nunamaker et al. (1991) list several possible sources of process loss from fraud detection
brainstorming, and three sources are most discussed in the literature, including production
blocking, evaluation apprehension and free riding (Dennis and Valacich, 1993). Based on the
experiments conducted in Diehl and Stroebe (1987), production blocking appears to be the
main cause. Paulus et al. (1995) find that group brainstorming can diminish productivity
even when members are well-trained in group work.
A further line of research investigates face-to-face and electronic brainstorming. Overall,
the studies are in favor of brainstorming conducted electronically. Lynch et al. (2009) show
that computer-mediated brainstorming sessions enhance the effectiveness of fraud risk
assessment. Specifically, electronic brainstorming can mitigate concerns such as production
blocking, evaluation apprehension and free riding, and strengthen synergy during
interactions (Dennis and Valacich, 1993). Additionally, Smith et al. (2012) state that the
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superior performance of electronic brainstorming is mainly due to stronger task focus and
longer comments made by auditors. In particular, computed-based groups and group
support systems (GSS) have demonstrated strong performance in tasks related to idea-
generation (Valacich et al., 1994; Fjermestad and Hiltz, 1998).
Literature has also shown some benefits of face-to-face brainstorming. For example,
Cockrell and Stone (2011) find that face-to-face brainstorming encourages more in-depth
discussion that leads to better performance than electronic format. Brazel et al. (2004) show
that face-to-face audit workpaper review is associated with more accountability and higher
quality judgment from the preparers relative to the review in electronic mode. Overall, face-
to-face brainstorming seems to suffer more from process loss, particularly production
blocking, while electronic brainstorming might result in low-quality dialogue and judgment.

4. Application of big data analytics

Based on the above, financial fraud is the product of internal and external factors that
involve incentives, opportunities and rationalization. The detection mechanism is
complicated and tends to overlook clues from non-financial information. The auditing
standards introduce a critical mechanism in fraud assessment, the brainstorming sessions,
which allow auditors to draw on collective wisdom and generate ideas. In this section, we
discuss the integration of Big Data analytics into the brainstorming sessions.
The meaning of such integration is twofold. One is to propose detailed procedures that
can make use of the non-financial information in detecting fraud. The fraud triangle
emphasizes incentives, opportunity and rationalization, all of which involve a high degree of
uncertainty and cannot be identified simply through the numbers on the financial
statements. Big Data analytics can be useful in this dimension by incorporating
unstructured data and providing more reliable results. The other purpose is to address the
concerns in both face-to-fact and electronic brainstorming. As mentioned above, the
effectiveness of brainstorming can be compromised due to production blocking and lack of
in-depth discussions. The use of Big Data can, at least to some extent, mitigate these
problems and eventually enhance the effectiveness of fraud detection.
Big Data is usually defined as possessing the characteristics of four Vs, namely, Volume,
Velocity, Variety, Veracity (Zhang et al., 2015), and requires the support of large, complex
information systems (Vasarhelyi et al., 2015). Yoon et al. (2015) argue that Big Data provide
sufficient, reliable and relevant information that should be considered as complementary
audit evidence. Big data also increase audit quality due to its ability to enable population-
based audit compared to sample-based (Ramlukan, 2015). While the application of Big Data
in accounting and auditing practice has been introduced before (Tang and Karim, 2017), the
 MAJ focus on brainstorming sessions remains undiscussed and could generate useful
implications.
We propose three channels through which Big Data can be used in brainstorming. First,
Big Data can combine structured and unstructured data. A key part of the brainstorming
session is to review and analyze the client’s prior information in the framework of the fraud
triangle. Thus, the information should be presented in various formats that can effectively
indicate fraud risk factors. Prior studies support the use of nonfinancial measures (Brazel
et al., 2009) and text-mining method (Humphreys et al., 2011) in fraud assessment.
For example, the news related to management, board members, employees and company-
wide reputation can be selectively combined with the firms’ financial record to provide more
complete evidence. The observation that a CEO increases personal spending on luxurious
items when the company is incurring a loss and layoff should serve as a potential fraud risk.
Board meeting minutes can also be extracted and matched with the actual outcomes.
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Auditors can check whether the agenda is indeed carried out by gathering evidence from
different sources. In addition, Big Data can help auditors examine alumni relationships,
especially those between managers and audit committee members. Information derived
from social media can assist auditors in identifying appointments or new positions obtained
through personal network, which often leads to fraudulent activities. The messages from
anonymous whistleblowers can also be analyzed as an additional source of information.
Kaplan et al. (2012) show that non-anonymous whistleblowers are discouraged from
reporting when they witness no repercussions to the fraudsters. With the help of Big Data
technology, the brainstorming sessions can combine the whistleblowers’ information with
data in other formats, which retains the confidentiality and increase evidence completeness.
In short, Big Data provides a larger and more resourceful information base that enables
auditors to efficiently translate audit evidence into fraud risk factors. The integration of
unstructured data also enhances the veracity of audit evidence. The reason is that data
collected in such large quantity and from very different sources offer more complete
evidence compared to the traditional data and are subject to less management manipulation.
Second, Big Data can enhance the performance of analytical procedures, thus yielding
more insightful and reliable results for decision-making. In trend/pattern analysis, all
accounting data can be pooled and compared across years and industries. Perols and Lougee
(2011) find that earnings management in prior years is an indicator for committing fraud.
Accordingly, auditors can conduct regression analysis to find associations on the population
level to increase the model’s explanatory power. Big Data can also be useful in identifying
anomalies. Kedia and Philippon (2009) show that firms involve in earnings management
tend to invest aggressively to match the performance of the better firms in the industry.
Auditors can use computer algorithms to detect any sudden change in profits or revenue
that falls out of the normal range of the industry average. PCAOB (2007) states that the
performance of analytical procedures can be strengthened if the data analyzed are subject to
less manipulation. By gathering timely raw data from real-life sources, auditors are
presented with more reliable information thus improving the relevance of the ideas
generated during the brainstorming process. That is, Big Data analytics tools can
significantly contribute to the brainstorming sessions if they are designed to provide timely
and accurate feedback upon auditors’ request.
Finally, Big Data can facilitate the communications during brainstorming sessions.
Building on the idea of electronic brainstorming, Big Data can further incorporate
computerized programs to record fraud discussions, trace the chain of thoughts and
establish possible scenarios. Auditors can write down longer comments that include photos,
flowcharts and videos to express their ideas. Devices can be used to simultaneously record
 all auditors’ comments and present them on the same screen. The devices can be Financial
programmed to combine the unstructured data and group auditors’ comments into fraud detection
categories. Analysis, such as descriptive data mining, can then be performed within each
category to generate conclusions. Therefore, Big Data analytics not only eliminates the wait
time for speaking during discussions, a common issue in face-to-face brainstorming, but also
allows auditors to quickly use dialogue in idea generation.
In addition, Big Data can collect the comments from both predecessor and current
auditors and draw on prior fraud cases occurred in real life. This allows auditors to trace the
thought pattern of potential fraudsters and construct hypothetical fraud scenarios. Further,
Big Data tools can research on each auditor’s industry background and work experience.
Displaying information such as news reports and industry index can provide auditors with
a sense of familiarity and inspires logical and creative thinking.
To summarize, the above procedures require careful design and an efficient combination
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of Big Data, technology and accounting professionals. Such integration can help mitigate
concerns found in brainstorming (e.g. production blocking and low-quality discussions) and
use non-financial and unstructured data to better identify fraud factors within the
framework of the fraud triangle.
Following the advantages mentioned above, we propose a model that can effectively
incorporate Big Data analytics in the brainstorming sessions. The model contains six steps,
and the performance at each step can be enhanced by the suggested application of Big Data
tools, as shown in Figure 1.
Step 1 and Application 1: The brainstorming process should begin with initial data
collection that helps uncover potential fraud indicators, which later serve as the outline for
the group meetings. The system can automatically generate pre-saved data queries that
contain the company’s basic information, such as business operations, financial statements,
prior audit results, board composition and recent media coverage. Further data requests for
specific items can be submitted by members of the audit team based on their experience, and
the system will populate the results along with the original request. For example, an audit
team member might suspect the existence of a close relationship with the supplier could lead
to bribery or kickbacks. Therefore, the detailed purchase history with the supplier should be

Figure 1.
Proposed model for
the application of big
data analytics to the
brainstorming
session
 MAJ added to the query. If a firm’s revenue performance consistently beats external expectation, it
could be a signal of earnings overstatement and source documents that support the sales
transactions should be collected. In short, Big Data analytics can facilitate the data collection task
by searching and storing a large amount of data in different formats.
Step 2 and Application 2: data in various formats collected in Step 1 are processed. The
main objective of this step is to combine the structured and unstructured data so a unified
version of suitable evidence can be presented. For example, surveillance video can be
combined with inventory record to detect risk in inventory theft. The software can also
merge data of system logs with high-risk accounts such as cash to discover unauthorized
access and fraudulent activities.
Step 3 and Application 3: based on the data collected and organized in the previous steps,
some primary analytical tasks can be performed to identify potential risk factors. The
application of Big Data analytics in such analysis varies across companies and industries.
For banks and financial service companies, a close examination of the high default rate
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loans (e.g. type, loan amount, interest rate, location, account managers) can reveal the
business line with high fraud risk. In an environment where intellectual properties are
susceptible to theft, tests can be conducted to distinguish patterns, such as the number and
type of similar products released around the same time by competitors. This helps uncover
the department and product line that require stronger security checks and protection
systems. In the insurance industry, analysis can be carried out to identify irregularities in
billing activities. If a department consistently processes insurance claims right before the
policy lapses, it might be a signal of potential fraud committed by the insurers, which should
trigger further auditing work.
Step 4 and Application 4: the audit team can then coordinate group meetings, in which the
previously identified risk factors are used for idea stimulation and inspiration. The purpose
of incorporating the fraud indicators determined by Big Data tools is to provide helpful
guidelines and build relevant context so that new ideas are more likely to be generated. The
system-identified risk factors should not serve as a checklist that limits the scope of the audit
team’s discussion. Rather, they should provide additional evidence that could not be captured
by auditors’ intuition.
Even when the technology can establish an extremely secure environment, such as
blockchain, it is impossible to eliminate all risk factors, which makes manual monitoring
necessary. For example, while companies that adopt blockchain technology seem to contain
almost zero risk due to the difficulty in altering transaction records, auditors should still
consider the potential flaws in the system design. A dominating miner node could control
the blockchain to commit fraud, and identity theft can comprise the entire network (Xu,
2016). Hence, the existence of Big Data tools should serve to complement auditors’
professional judgment.
The effect of the group meetings can be further enhanced by the continued use of Big
Data tools throughout the discussion process. For example, similar fraud cases in the past
can be requested and presented during meetings for comparison. A Big Data software can
record each person’s thoughts, questions and notes simultaneously and display them on the
same screen for the team to share. Team members can follow-up with an analytical test
when new comments emerge to solidify the proposed ideas. Auditors can also use the
massive database to explore hidden clues and reconcile different opinions as they proceed
with the brainstorming. In short, the incorporation of Big Data encourages group members
to delve into new areas and offers instant data support.
Step 5 and Application 5: in this step, new ideas proposed during the group meetings are
evaluated and finalized. With the assistance of recording devices, each team member can
 view the ideas of others and leave comments. The system can categorize the auditors’ Financial
comments into different sessions according to the content and tones. Within each session, fraud detection
the software can collect additional data to perform analytical procedures. These procedures
can address questions include: are the concerns raised by auditors supported by real-life
cases? Is the fraud indicator particularly relevant to a specific industry? Can we observe a
pattern from similar fraud cases? Further, an anonymous voting on the mostly mentioned
ideas can be conducted. Based on the feedback provided by team members and data
analysis, the audit partner/leader can then proceed to draw the conclusions.
Step 6 and Application 6: the final step completes the entire process by documenting the
content and results of the brainstorming sessions. While the conclusions reached by the
audit team remain the most important product, it is also necessary to document the detailed
thought process of each team member. As Big Data tools can store and combine data in
various formats, even voice and video records of the group meetings can be properly saved.
These “work-in-progress” notes from the discussion will help form a useful source of
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database for future brainstorming sessions.

The implementation of the proposed model will require some necessary training, both on
auditors’ knowledge of current Big Data trends and their skills in using technology that is
compatible with handling audit tasks. A separate department can be established to design
the system, and workshops can be organized to teach members helpful techniques. The
training should be systematically conducted and offered to all auditors, particularly for the
audit partners who might lead the brainstorming discussions. Given the scale of resource
and revenue, Big 4 might be more suitable candidates to adopt the model at the current
stage. However, there is essentially no constraint on making the choice as long as the budget
allows, or the client’s business operations demand a Big-Data-supported audit.
We further discuss the potential benefits and extension of the model above. According to
PCAOB (2007), the effectiveness of brainstorming can be impaired when the session was not
properly held, or key members were absent. By incorporating Big Data tools, the
brainstorming session can be conducted remotely, which encourages more participation. As
the comments are recorded and processed in the system, the content of the meetings can be
shared via a network when a member fails to attend. This also allows continuous discussion
and communication among the engagement team, especially when new ideas or facts are
discovered after the planning stage.
However, the integration of Big Data in brainstorming sessions should be conducted
with caution to avoid interacting with factors that negatively affect auditors’ judgment.
Simon et al. (2018) find that the separation of likelihood and magnitude assessment of risk,
as recommended by the auditing standards, actually results in worse auditor’s judgment.
Rose et al. (2017) find that Big Data visualizations should be introduced after the traditional
audit evidence to yield positive results. These findings suggest that the design of a Big-
Data-supported brainstorming session should consider auditors’ cognition and behavior.
Finally, the proposed integration of Big Data tools can be extended to different steps of
the audit process. For example, initial data collection (Step 1), data integration (Step 2) and
fraud indicator identification (Step 3) can be used in the planning stage to establish the scope
of the audit and risk assessment procedures. Hammersley et al. (2010) find that
documentation specificity during planning stage affects the subsequent fraud risk
assessment; thus, documenting (Step 6) of the model can be helpful in this task. For
substantive testing, analytical procedures using Big Data technology described in Step 3 can
be carried out to detect material misstatement. The review stage can benefit from Step 3 and
Step 6 so auditors can have easy access to the previously discussed memos and performance
effective analytical procedures when assessing the conclusions.
 MAJ 5. Conclusions
This paper discusses the application of Big Data analytics to the brainstorming sessions
required in the current auditing standards. We review literatures on the causes and
consequences of financial fraud. We further comment on the existing problems in fraud
detection mechanisms, particularly the difficulty in analyzing unstructured and non-
financial data. The brainstorming sessions offer a potential solution by encouraging
discussions and new ideas. Given the complexity in organizing individual thoughts and
drawbacks in the format of brainstorming, whether it is face-to-face or electronic, we
propose the integration of Big Data analytics into the brainstorming sessions. The
obvious advantages of such integration include comprehensive information base, useful
results from analytical procedures and efficient communications. Our proposed model
outlines a six-step brainstorming process that can benefit from the application of Big
Data analytics.
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One limitation of the model proposed is its cost-effectiveness. The implementation of Big
Data tools during brainstorming sessions and other parts of the audit process can be costly,
particularly given the need to continuously update the system and collect various types of
data. Moreover, the choice of integrating Big Data tools partially depends on the audit fees
and whether the clients themselves use Big Data technology in the accounting practice. This
implies that larger audit firms with stronger revenue performance are perhaps more suitable
candidates to adopt the model. Another caveat is that the quantity of Big Data might create
unwanted burden for the engagement team. As stated in Appelbaum et al. (2017), if an audit
by exception (ABE) approach is adopted, Big Data can generate too many exceptions which
hinder the audit process. Brown-Liburd et al. (2015) identify information overload and
relevance as potential drawbacks of Big Data. This implies that adjustments to the overall
audit strategy need to be made if auditors decide to involve Big Data tools in the audit
process. Finally, the security of Big Data also deserves attention. Appelbaum (2016) argues
that the provenance of audit-related data should be safely managed so that the auditors can
retrace the events for verification purpose. The same might apply to using Big Data tools in
the brainstorming sessions, which is to emphasize the importance of verifying the source of
all data.
Future research can be conducted in areas that extend the arguments in this paper. For
example, theoretical framework with respect to the application of Big Data analytics should
be developed. Alles and Gray (2016) argue that Big Data can be interpreted incorrectly and
generate false positives without formal theories for guidance. Other potential questions
include: what are possible sources of the unstructured data considered in the brainstorming
sessions? What analytical procedures can be developed using Big Data tools? What are the
advantages and disadvantages of the existing brainstorming methods and future ones that
incorporate Big Data? We suggest future studies use survey results or case analysis to
address these questions.

Notes
1. Please refer to the official website of PCAOB, available at: https://pcaobus.org/Standards/
Auditing/Pages/AS2401.aspx
2. Production blocking refers to the situation within a group where only one member can speak at a
time. This consequently leads to wait time for other members (Diehl and Stroebe, 1987).
3. Please refer to the official website of AICPA, available at: www.aicpa.org/Research/Standards/
AuditAttest/DownloadableDocuments/AU-C-00240.pdf
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About the authors

Jiali Tang is an Assistant Professor of Accounting at the University of Hartford. Her research interest
includes earnings quality, corporate governance, audit quality and corporate social responsibility.
Jiali Tang is the corresponding author and can be contacted at: jtang@hartford.edu
Khondkar E. Karim has published over 80 articles in various refereed journals including
Accounting Organizations and Society (AOS), Behavioral Research in Accounting (BRIA), Journal of
Corporate Finance, Accounting Horizons, Journal of Accounting Auditing and Finance, Managerial
Auditing Journal, Review of Quantitative Finance and Accounting, Applied Financial Economics,
Advances in Accounting Behavioral Research, International Journal of Finance, Advances in
Accounting, Advances in International Accounting, Advances in Quantitative Analysis of Finance and
Accounting, Research in Finance, International Journal of Auditing and The Mid-Atlantic Journal of
Business. He is also the recipient of a 1999 ANBAR Citation of Excellence for AOS paper. He has
received the outstanding teaching award and outstanding research award in 1997 and 1999
respectively. He was recognized as one of the most productive accounting doctoral graduates in a
study published in Advances in Accounting. It identified the top ten doctoral graduates by the year
of their graduation and the number of publications within the top 40 selected accounting journals. He
has co-edited a monograph on environmental disclosure practices and financial performance and two
special issues of managerial finance on performance measurement and evaluation. He served on the
editorial board of Issues in Accounting Education. Currently, he serves as the Editor of Advances in
Accounting Behavioral Research.

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