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Big Data, Data Analytics and Artificial Intelligence in Accounting: An
Discipline of Accounting and Finance
Newcastle Business School
University of Newcastle
Sydney, NSW 2000, Australia
Tel: +61 (2) 8262 6406
Sajal Kumar Dey
Discipline of Accounting and Finance
Newcastle Business School
University of Newcastle
Sydney, NSW 2000, Australia
Tel: +61 (04) 5067 4662
School of Business
Faculty of Business and Law
University of Wollongong
NSW 2522, Australia
Tel: +61 (04) 1418 3974
Bose, S., Dey, S. K. & Bhattacharjee, S. (2022). “Big Data, Data Analytics and
Artificial Intelligence in Accounting: An Overview” in S. Akter and S. F.
Wamba (Eds.) Handbook of Big Data Methods (pp.1-34). Edward Elgar
Publishing, United Kingdom. Forthcoming.
1 Corresponding author: Sudipta Bose, Discipline of Accounting and Finance, Newcastle Business
School, University of Newcastle, Sydney, NSW 2000, Australia. Tel: +61 (2) 8262 6406;
Big Data, Data Analytics, and Artificial Intelligence in Accounting: An
This chapter provides a brief overview of the growing role played by big data, data analytics,
and artificial intelligence (AI) in the accounting profession. The term “Big Data” along with
other trending topics such as “Data Analytics” and “Artificial Intelligence (AI)” have become
buzzwords in the accounting profession in recent years. The skills of accounting professionals
have evolved as technology has made rapid advances from using pencil and paper to
typewriters and calculators, and eventually spreadsheets and accounting software. Data
analytics in accounting is a relatively new skill set that is growing significantly in all areas of
the accounting profession. Accounting professionals who can discern patterns and trends in
big data and translate them into compelling strategic narratives will find themselves at the
centre of the twenty-first-century business world. Therefore, accounting professionals can
capitalise on numerous opportunities in this rapidly evolving, disruptive, but ultimately
advantageous environment by embracing big data, data analytics, and artificial intelligence
(AI) to stay ahead of the competition.
Keywords: Artificial Intelligence (AI); Big data, Blockchain; Data analytics; Machine
The business environment is increasingly competitive, and most organizations are
looking for an “edge”. For many companies, that “edge” is the implementation of
new technology, enabling the mining of vast amounts of data (Big Data) using
leading-edge analytical tools.
–– Institute of Management Accountants [IMA] (2019)
Robotisation is rapidly influencing the way how humans interact, how work gets done,
and identifying tasks that can be automated (Chartered Institute of Management Accountants
[CIMA], 2022). With the advent of big data, data analytics, and artificial intelligence (AI),
accounting and finance professionals can take advantage of several opportunities in this
rapidly evolving, disruptive, but advantageous environment (Chartered Institute of
Management Accountants [CIMA], 2022). The total amount of data created, captured,
reproduced, and consumed worldwide is projected to grow rapidly, reaching 64.2 zettabytes
or 64.2 trillion gigabytes in 2020 (See, 2021). Additionally, global data generation is expected
to exceed 180 zettabytes over the next five years, reaching a peak in 2025 (See, 2021). This
data can be captured and analysed to provide valuable insights that can support future
business growth. Consequently, businesses need to employ tools that can help them in
converting data into usable information, which requires the use of data analytics tools. Data
analytics is the process of identifying patterns and trends in past raw data (often referred to as
big data) to predict future events that assist in strategic decision-making while artificial
intelligence (AI) entails data processing, making assumptions, and attempting to make
predictions that are beyond human capabilities.
Although the term “Big Data” is relatively new to the business world, it is already
routinely used for practically every facet of human activity (Vasarhelyi, Kogan, & Tuttle,
2015). The fundamental reason for big data’s popularity is that recent advances in information
technology, especially the Internet, have made available an exponentially growing amount of
information (Vasarhelyi et al., 2015). More specifically, big data is widely recognised as the
next frontier for innovation, competition, and efficiency (Manyika et al., 2011). In line with
this notion, the McKinsey Global Institute (2012) conducted a survey and found that 51% of
the global business leaders believe that big data and data analytics are top priorities in their
current business function. Furthermore, the Chartered Global Management Accountant
[CGMA] (2013) surveyed more than 2000 Chief Financial Officers [CFOs] and finance
professionals around the world and concluded that big data would revolutionise the way
businesses operate over the next decade.
Accounting professionals have an important role to play in big data and data analytics
since accounting deals with the recording, information processing, measurement, analysis,
and reporting of financial information (Liu & Vasarhelyi, 2014). Accounting practitioners
around the world have emphasised the value of big data in the accounting and finance fields.
For example, the Association of Chartered Certified Accountants [ACCA] and the Institute of
Management Accountants [IMA] (2013a) contend that big data, cloud, mobile, and social
platforms are changing the landscape for accounting and finance professionals, and they must
adapt to the challenges posed by cybercrime, digital service delivery, and artificial
intelligence. Similarly, the Chartered Global Management Accountant [CGMA] (2013)
stresses the significance of big data, arguing that it raises significant challenges for the future
role of accounting and finance. Accountants, who specialise in providing financial accounts to
report on past performance, may be sidelined if they do not embrace this change and
appreciate the new technologies (Chartered Institute of Management Accountants [CIMA],
2022). Alternatively, they might grasp the opportunity to become big data champions as a
source of evidence to support decision-making and help reinvent the way businesses are done
(Chartered Global Management Accountant [CGMA], 2013).
With rapid advances being made in technology, accounting skills have shifted from
using pencil and paper to typewriters, calculators, and eventually spreadsheets and accounting
software (Poddar, 2021). Data analytics in accounting is a relatively new skill set that is
growing significantly in all areas of accounting. The value of the benefits of data analytics in
accounting and finance has grown over time, and has in fact transformed the task processes,
particularly those that provide inferences, predictions, estimations, and assurance to decision-
makers and information users (Austin, Carpenter, Christ, & Nielson, 2021). Consequently,
accounting scholars, researchers, and practitioners worldwide view data analytics as a
valuable tool for gaining new insights into businesses financials, identifying areas for process
improvement, and reducing risk.
Nowadays, accounting professionals globally provide significant support to their
senior executives by analysing and interpreting the large volume of accounting data, most of
which comes from non-traditional accounting systems (Siegel, 2013; Haverson, 2014;
Davenport & Harris, 2017). Web server logs, Internet, and mobile phone clickstream
recordings, social media, and a large number of machine-generated and sensor-detected
systems are implemented to extract financial and non-financial information that is used to
analyse and interpret the data to make important business decisions (Bertolucci, 2013; Löffler
& Tschiesner, 2013). Added to this, several governments around the world have taken the
initiative to improve third-party access to both internal government data and data collected
from citizens and businesses as a key measure of administrative procedures, which will create
more prospects for data analytics (Casselman, 2015; Ofﬁce of Management and Budget,
2015). Furthermore, the use of artificial intelligence (AI) technologies (e.g., computer visions,
natural language processing, speech recognition and machine learning) enables companies to
improve their efficiency and obtain valuable insights about their customers and employees to
develop more competitive customer and personnel strategies (Ernst & Young, 2020).
Accounting professionals are playing a more strategic role in the field of artificial intelligence
(AI) technology. Since the advent of big data, data analytics, and artificial intelligence (AI),
accounting professionals have demonstrated their ability in delivering greater value to
businesses by generating higher revenues and streamlining processes.
The remainder of the chapter is organised as follows. Section 2 discusses big data in
accounting. Section 3 describes the data analytics in accounting including the role of data
analytics in financial accounting, auditing, and management accounting, followed by Section
4 which provides an overview of artificial intelligence (AI) in accounting. The final section
(Section 5) concludes the chapter.
2. Big Data in Accounting
The term “Big Data” is defined as a huge dimension of unstructured and structured data
derived from multiple sources (Ernst & Young, 2014). Structured data refers to the highly
organised information stored in the relational databases of spreadsheets. In contrast,
unstructured data refers to data from sources that are not highly organised (e.g., photos,
videos, blogs, presentations, social media posts, satellite imagery, open-ended survey
responses, and website content) that generally produce 85% of the world’s information today
(Mills et al., 2012). The other data type is semi-structured data that contains both structured
(highly organised) and unstructured (not highly organised) elements (e.g., emails, zipped files
etc.). The term “Big Data” has become a buzzword in the accounting profession in recent
years like other trending topics such as blockchain, artificial intelligence (AI), and machine
learning (Boomer, 2018). Although there is no internationally accepted definition of big data,
Gartner (2012) defines big data as “high-volume, high-velocity, and/or high-variety
information assets that demand cost-effective, innovative forms of information processing
that enable enhanced insight, decision making, and process optimization”. Big data is thus
characterised by these “three Vs”, namely volume, velocity, and variety (Association of
Chartered Certified Accountants [ACCA] and Institute of Management Accountants [IMA],
2013b). Volume indicates the massive size of datasets (e.g., Facebook, Google, Yahoo, blogs,
census records, etc.), velocity indicates the speed at that the data is generated (e.g., stock
prices data that are generated very rapidly), while variety is the collection of data from
different sources (both structured and unstructured) (Cao, Chychyla, & Stewart, 2015). Figure
1 shows the “3V” model of big data.
Figure 1: The “3V” model of big data [Source: Merritt-Holmes (2016)]
There are two other sources of big data, and these are veracity and value. Veracity
indicates the accuracy and reliability of the data, while value focuses on the costs and benefits
of data collection (Zhang, Yang, & Appelbaum, 2015; Merritt-Holmes, 2016; Janvrin &
Watson, 2017). Furthermore, according to Zhang et al. (2015), big data includes four different
elements, namely high volume, high velocity, high variety, and high veracity. The application
of continuous auditing now largely depends on automated real-time data analysis due to the
large volume and high velocity of data (Vasarhelyi, Alles, & Williams, 2010). However, high
volume and high velocity create gaps between modern audit analytics and the demand for big
data analytics. The huge variety and high veracity also generate new challenges because the
current method of auditing has only limited competencies to deal with big data.
Although big data encompasses massive veracity, huge volume, and computing
supremacy for the collection and processing of information, it cannot be stored, processed,
and analysed using old-fashioned methods (Gartner, 2012). Big data cannot be used by
businesses without a systematic analysis being undertaken. However, after efficiently
scrutinising, cleaning, transforming, and properly interpreting the big data, it generates
valuable insights (Cao et al., 2015). Furthermore, efficient analysis and appropriate
interpretation of this data will speed up the revenue generation process, understand
customers’ expectations and provide essential information to interested and relevant users
(Trkman, McCormack, De Oliveira, & Ladeira, 2010; Davenport & Harris, 2017). When it
comes to accounting, the purpose of big data is to collect, organise, and use data from a range
of sources to gain new business insights in real-time. For example, accounting and financial
analysts can access real-time data from anywhere with a network connection instead of
relying on monthly financial reports.2
Although big data has several implications for business, particularly in terms of
accounting software, making financial decisions, analysis of customer consumption patterns,
and banking (Davenport & Harris, 2017), the importance of big data varies from organisation
to organisation. For example, big data from the Big 4 accounting firms is not considered big
for a small accounting and tax consultancy firm (Vasarhelyi et al., 2015). Likewise, the big
data originating from the Big 4 firms is unlikely to be considered that big compared to NASA
(Vasarhelyi et al., 2015). Whether a given amount of data is big or not depends on whether
that data exceeds the capabilities of information systems that work with that data. Thus,
storage and processing are considered to be the two measures of big data competencies
(Vasarhelyi et al., 2015).
2 https://online.maryville.edu/blog/data-analytics-in-accounting/ (accessed on 8 March 2022).
Despite the fact that big data has become an emerging buzzword for accounting
professionals in recent years, the impetus of accounting has always been to provide useful
information to interested users (Capriotti, 2014). Although the main goal of accounting
professionals is to provide information from a large volume of business records for decision-
makers to consider, accounting information originates from diverse sources such as paper-
based systems, legacy-based systems, and highly technical business systems (Janvrin &
Watson, 2017). Through the implementation of various software and analytical tools,
accounting professionals identify, record, summarise, analyse, and report financial
information for their internal and external users. Both internal and external auditors
implement a variety of automated techniques (e.g., generalised auditing software) to review
accounting information to ensure that managers prepare financial statements in accordance
with relevant accounting standards and applicable laws.
In this way, big data has changed the practice of measuring business transactions and
ensuring their relevance. Additionally, big data offers companies the ability to capture
transactions before their formal accounting entry, identify inventory movements before their
actual receipt or delivery, identify customer calls before actual service activities are
performed, and many more forms of identifying economic activities (Vasarhelyi et al., 2015).
The measurement system for accounting transactions has been drastically changed due to new
technology-driven changes and the advent of Enterprise Resource Planning [ERP] systems
that speed up the process of capturing data and improving the data processing systems
(Romero, Gal, Mock, & Vasarhelyi, 2012). Although no changes are visible in accounting
practices and standards such as International Accounting Standards (IAS) and International
Financial Reporting Standards (IFRS), big data is the main cause of a paradigm shift that
makes it possible to identify and address business functions earlier (Vasarhelyi et al., 2015).
Table 1 provides a list of several opportunities and challenges in implementing big data for
the accounting and finance profession as identified by the Association of Chartered Certified
Accountants [ACCA] and the Institute of Management Accountants [IMA] (2013b).
Table 1: Opportunities and challenges of big data for the accounting and finance
Area Opportunity Challenge
Helping companies value their data assets
through the development of robust
Increasing the value of data through
stewardship and quality control.
Big data can quickly ‘decay’ in value as new
data becomes available.
The value of data varies according to its use.
Uncertainty about future developments in
regulation, global governance, and privacy
rights and what they might mean for data
Use of big data
Using Big data to offer more specialized
decision-making support in real time.
Working in partnership with other
departments to calculate the points at
which big data can most usefully be
shared with internal and external
Self-service and automation could erode the
need for standard internal reporting.
Cultural barriers might obstruct data sharing
between silos and across organizational
Use of Big data
Expanding the data resources used in risk
forecasting to see the bigger picture.
Identifying risks in real-time for fraud
detection and forensic accounting.
Using predictive analytics to test the risk
of longer-term investment opportunities
in new markets and products.
Ensuring that correlation is not confused with
causation when using diverse data sources
and big data analytics to identify risks.
Predictive analytic techniques will mean
changes to budgeting and return on
Finding ways to factor failure-based learning
from rapid experimentation techniques into
processes, budgets and capital allocation.
Source: Association of Chartered Certified Accountants [ACCA] and Institute of Management
Accountants [IMA] (2013b)
3. Data Analytics in Accounting
Data analytics can be defined as “processes by which insights are extracted from
operational, financial, and other forms of electronic data internal or external to the
organization” (KPMG, 2016). There are a variety of ways in which these insights can be
derived, including historical, real-time, or predictive. They can be risk-focused (e.g., control
effectiveness, fraud, waste, abuse, non-compliance with policy/regulatory) or performance-
focused (e.g., increased sales, decreased costs, improved profitability, etc.) and frequently
provide “how?” and “why?” answers to the initial “what?” queries commonly found in the
information retrieved from the data (KPMG, 2016). Three distinct characteristics of data
analytics are the data itself; the analytics applied to it, and the presentation of results in a way
that enables commercial value to be generated (Gantz & Reinsel, 2012). More generally, data
analytics encompasses not only the collecting and managing of data but also visualising and
presenting of data using tools, infrastructure, and methods to gain insights from big data
(Mikalef, Pateli, Batenburg, & Wetering, 2015). Therefore, data analytics is a systematic
process of investigating structured and unstructured data through various techniques such as
statistical and quantitative analysis, as well as explanatory and extrapolative models to
generate useful information for accounting decision-makers.
Accounting has evolved in tandem with changing technology, from the use of pencil
and paper to typewriters, calculators, spreadsheets, and accounting software (Poddar, 2021).
Most accountants have the ability to analyse data as they are experienced in recording and
analysing transactions. As well, they are well trained to document the accounting information,
are familiar with financial statements, and have sufficient experience in various aspects of
business decisions, making them experienced trusted advisors to businesses (Haverson, 2014).
Therefore, technical skills, analytical thinking, and problem-solving competence have long
been a part of the accounting profession. However, accounting data analytics is a relatively
new skill set that is spreading rapidly in the accounting profession.
The importance of data analytics has increased over time as accounting professionals
implement the data analytics tools to infer, predict, and assure the business data to make a
useful decision. For example, corporate executives and senior-level management are adopting
data analytics to identify and infer operational inefficiencies (Dai, Byrnes, Liu, & Vasarhelyi,
2019). In addition, data analytics is an important area of investment for public accounting
firms, particularly in consulting, tax advisors and auditing services (Earley, 2015). Tax
professionals use data analytics to detect tax fraud and forecast future tax liabilities
(DaBruzzo, Dannenfelser, & DeRocco, 2013). In addition, data analytics is now widely
implemented by auditors to identify uncertain, ambiguous, or possibly fraudulent transactions
(Vasarhelyi, 2013; Verver & Grimm, 2013; Brown-Liburd, Issa, & Lombardi, 2015), and to
understand and assess the safety and control of a client’s huge volume of datasets (Ernst &
Young, 2014; Vasarhelyi et al., 2015).
Apart from the demand for data analytics by accounting professionals, accounting
scholars are considering data analytics in their accounting curricula. The Association to
Advance Collegiate Schools of Business [AACSB]’s demand for data analytics as a
component of an accounting degree demonstrates the relevance of data analytics given that
graduates must possess abilities in creating, distributing, assessing, and interpreting data
(Schneider, Dai, Janvrin, Ajayi, & Raschke, 2015). Additionally, the Pathways Commission
on Accounting Higher Education (Behn et al., 2012), funded by the American Accounting
Association (AAA) and the American Institute of Certified Public Accountants (AICPA),
inspires the AACSB to introduce a new accounting program accreditation standards. These
include accounting students’ learning experiences including “data creation, data sharing, data
analytics, data mining, data reporting, and storage within and across organizations”
(Association to Advance Collegiate Schools of Business International [AACSB], 2013).
Furthermore, businesses implement data analytics tools in their day-to-day functions
to increase their profits and reduce costs/overheads in various ways outside of accounting. For
example, customer analytics is used in marketing to find and understand consumer purchasing
habits and other behavioural patterns so that market trends can be predicted and forecast new
opportunities. Algorithmic trading is used to speed up the current stock price monitoring
systems. Although unstructured data is not used in business for analysis, the integration of
data analytics accelerates the process of using unstructured data to boost the timeliness of
3 https://online.maryville.edu/blog/data-analytics-in-accounting/ (accessed on 8 March 2022).
3.1 Types of Data Analytics
There are four types of data analytics: descriptive analytics, diagnostic analytics,
predictive analytics, and prescriptive analytics. They are summarised in the table below.
Table 2: Types of data analytics
analytics Explanations Examples
Provides insight based on past information.
What is happening?
Used in standard report generation and in basic
spreadsheet functions such as counts, sums,
averages and percent changes and in vertical
ntal analyses of financial statements.
Examines the cause of past results. Why
did it happen?
Used in variance analyses and interactive
dashboards to examine the causes of past
Assists in understanding the future and
provides foresight by identifying patterns
in historical data. What will happen?
When and why?
Can be used to predict an accounts receivable
balance and collection period for each customer
and to develop models with indicators that
Assists in identifying the best option to
choose to achieve the desired outcome
through optimization techniques and
machine learning. What should we do?
Used in identifying actions to reduce the
collection period of accounts receivable and to
optimise the use of payable discounts.
Source: Tschakert, Kokina, Kozlowski, and Vasarhelyi (2016)
Data analytics is often misinterpreted as just descriptive analysis (“what is”).
However, what is really valuable is predictive (“what is going to happen”) and prescriptive
analysis (“what should we do?”) rather than descriptive analysis (Tschakert et al., 2016).
Companies and industries rely heavily on data analytics to take competitive advantage of
technological innovations. Thus, regulators, external capital providers, and capital market
participants consider the availability of data and their efficient analysis (Tschakert et al.,
Descriptive analytics focuses on what happened in the past. The term “Past” refers to
any point in time when an event occurred, which could be a month ago or only a minute ago.
Today, 90% of companies employ descriptive analytics, the most fundamental type of
analytics. This sort of analytics examines both real-time and historical data to provide insights
into the future rather than establishing a cause-and-effect relationship between events
(Tschakert et al., 2016). Google Analytics is a prominent example of descriptive analytics in
action; it provides a concise overview of website activity, such as the number of visits in a
certain period or the source of the visitors. Other business applications of descriptive analytics
include sales revenue results coupled with purchase, cost per customer, customer credit risk,
inventory measurement and accessibility, Key Performance Indicators (KPIs) dashboard, and
monthly revenue report (Tschakert et al., 2016).
Diagnostic analytics aims to further explore the cause of an event. The diagnostic
analysis delves into descriptive analytics data to ascertain the underlying reasons of outcomes.
For instance, if the descriptive analytics indicates that sales decreased by 20% in July, it is
needed to determine why, and the logical next step is to apply the diagnostic analytics. This
form of analytics is used by businesses because it connects more data and identifies patterns
of activity. For example, a freight company can employ diagnostic analytics to determine the
cause of sluggish shipments in a particular region.
The knowledge gathered from descriptive analytics is applied to predictive analytics
(Appelbaum, Kogan, Vasarhelyi, & Yan, 2017) and seeks to answer the question, “what is
likely to happen?” This type of analytics uses past data to make predictions about future
events. Therefore, forecasting is the core of predictive analytics. Advanced technology and
manpower are required to forecast this analysis, which is based on statistical modelling. Sales
forecasting, risk assessment, and customer segmentation to determine customer profitability
are examples of commercial applications for predictive analytics (Appelbaum et al., 2017).
Prescriptive analytics is at the forefront of data analytics, incorporating insights from
previous analyses to determine the best course of action to take in response to a current
problem or decision. The prescriptive model uses information about what happened, why it
happened, and a variety of “what-might-happen” evaluations to assist the users in selecting
the best course of action to take to avoid a future problem. Prescriptive analytics simplifies
the implementation and management of sophisticated tools and technologies such as
algorithms, business rules and machine learning. Artificial intelligence (AI) is an excellent
example of predictive analytics. Artificial intelligence (AI) systems absorb a significant
amount of data in order to learn and make intelligent decisions, and well-designed artificial
intelligence (AI) systems are able to communicate and even react to their decisions.
Prescriptive analytics and artificial intelligence (AI) are currently being used by most large
data-driven organisations (e.g., Apple, Facebook, Netflix, and others) to improve decision-
3.2 Importance of Data Analytics in Accounting
Data analytics is undoubtedly one of the most transformative technological
breakthroughs that has impacted the accounting profession over the past few decades
(Schmidt, Riley, & Church, 2020). By integrating accounting data analytics, companies can
make efficient business decisions and meet external capital providers coupled with capital
market participants’ expectations. The importance of data analytics in boosting a company’s
performance is well recognised (Wixom, Yen, & Relich, 2013). Accounting data analytics
supports companies to confirm that the business is operating efficiently; for example,
healthcare organisations can use accounting data analytics to reduce costs (i.e., less waste and
fraud) while improving the quality of care (i.e., safety and efficacy of treatment) (Srinivasan
& Arunasalam, 2013). In addition, data analytics helps accountants to track the performance
of the organisations and take necessary actions when they detect any deviations. This data-
analytical evaluation is important for the long-term feasibility and existence of a company.
Accounting data analytics opens up new potential for accountants to offer additional
value-added services to their clients, such as auditors can provide more precise
recommendations with less margin of error by continuously monitoring of larger datasets, tax
accountants leverage data science to quickly examine difficult tax questions that are likely to
be introduced to improve users’ experience. This can help in attracting new customers and
increasing the percentage of customers who remain loyal to the company over time. The
output of data analytics often includes sensitive information that raises concerns about
confidentiality or privacy (Schneider et al., 2015). As a result, data misuse can exacerbate
potential risks for businesses from various sources that are both internal and external.
Accounting executives are well known about these risks, and they are also well trained in how
to deal with these risks. Accounting data analytics can support in identifying the areas of these
business risks that are being challenged by a company and introduce predictive data analytics
to make an efficient business decision regarding specific business risks.
Accounting data analytics can be used to uncover the behavioural patterns of
customers. These patterns can help companies to develop analytical models that are likely to
be used in the future to discover investment opportunities and increase a company’s profit
margins (Poddar, 2021). Therefore, accounting data analytics can enhance a company’s profit
margin coupled with maximising the wealth of its owners. To maintain the highest level of
financial viability, every organisation should regularly evaluate cash flow and optimisation
opportunities. Many people were unable to generate cash during the COVID-19 emergency
due to forced business closures, lockdowns, stay-at-home directives, and widespread fear of
virus transmission (Clayton & McKervey, 2020). While these events are difficult to predict, a
clear cash flow picture can help mitigate the suffering associated with such disruptions.
Businesses can use data analytics to better understand and manage their sales, inventories,
receivables, and client segmentation, which is especially critical during the recovery (Clayton
& McKervey, 2020). It can be stated here that data analytics provides detailed insights into
the sources and uses of cash and makes it possible to assess the health of both ends of the
3.3 Emerging Accounting Technologies
Several data analytics in accounting might support a firm’s auditing and accounting
processes. Explanations of those approaches are documented below.
3.3.1 Deep Learning
Deep learning is an emerging artificial intelligence (AI) technique for analysing large
amounts of data to uncover complex and abstract patterns hidden within the raw data (Sun &
Vasarhelyi, 2018). Deep learning shows the deeper structure of events and situations in
several layers of neural networks by combining the information with more advanced methods
(Poddar, 2021). For example, existing data is likely to be used to generate an automated
algorithm for specific audit judgement, such as lease categorisation, bad debt calculation, etc.
(Poddar, 2021). Several companies across the world are outsourcing deep learning projects in
their research centres, for instance, IBM, Watson and others. Renowned accounting firms
have invested a significant amount of money in deep learning and artificial intelligence (AI).
Deep learning assists in decision-making throughout the audit process, including planning,
internal control review, substantive testing, and completion (Sun & Vasarhelyi, 2018).
Companies now understand the importance of deep learning in accounting data analytics so
that they can use it in their accounting and auditing processes.
3.3.2 Blockchain Technology
Blockchain stands for a decentralised information and accounting system that allows
for the control and validation of payment transactions while avoiding currency duplication or
digital multiplication (Abad-Segura, Infante-Moro, González-Zamar, & López-Meneses,
2021). Using block chain technology, accounting data can be securely stored, instantly shared,
and validated by anybody with an interest in this issue (Dai & Vasarhelyi, 2017). Blockchain
can serve as an alternative ledger system for accounting records (Coyne & McMickle, 2017)
and can help advance accounting information from a double-entry system to a triple-entry
system (Abad-Segura et al., 2021). This blockchain is likely to be used to store programs that
run only when predetermined conditions are met, and these programs are known as Smart
Contracts (Poddar, 2021). These smart contracts have several benefits. For example, if an
outlier reaches 100% of the median value of transactions, the auditor and the company agree
that it is time to evaluate the data using the human eye (Poddar, 2021). Therefore, blockchain
is likely to be introduced to identify such outliers and direct them to the auditors.
3.3.3 Predictive Analytics
Predictive analytics is an advanced analytical tool that can be used to identify real-time
insights and predict future events by analysing historical data (Poddar, 2021). Predictive
analytics allows organisations to foresee the future, predict outcomes, uncover opportunities,
uncover hidden threats, and take quick action to run their business and make insightful future
investment decisions. Thus, this predictive analytics has a great potential to support
3.4 Tools of Accounting Data Analytics
A company can use numerous tools to identify its financial performance and position
from several angles. Organisations can process their data using the following accounting data
3.4.1 Microsoft Excel
Microsoft Excel is a spreadsheet application used for Windows, macOS, Android and
iOS and is widely used by businesses worldwide. It has a wide range of features such as
calculation of data, summarising numbers, pivot tables, graphing tools, etc. Microsoft Excel
can perform statistical analyses like regression modelling. Microsoft Excel is one of the most
significant and robust data analytics tools in the market, and it enhances the efficiency and
effectiveness of user expectations.
3.4.2 Business Intelligence Tools
Accounting professionals might benefit from business intelligence tools that help them
to identify sustainable and predictive insights from a particular dataset. By using a variety of
business intelligence tools, a company can clean the data, model data and create easy-to-
understand visualisations (Poddar, 2021). This visualisation provides detailed understandings
and helps identify areas that require further development. Such tools generate some shared
features that can be easily accessible and understandable to other members of the group.
There are various business intelligence tools such as Datapine, Tableau, Power BI, SAS
Business Intelligence, Oracle Business Intelligence, Zoho Analytics, Good data, etc.
3.4.3 Proprietary Tools
A proprietary tool is a tool that is devised by a company for its own use. A company
internally develops and utilises this tool to produce and sell products and goods/services to its
users and customers. Large companies usually introduce proprietary tools such as Interactive
Data Extraction and Analysis (IDEA). The IDEA is a software application that allows
accountants, auditors, and finance professionals to interact with data files.
3.4.4 Machine Learning Tools
Machine learning is a data analysis technique where a software model is trained using data.
It is a field of artificial intelligence based on the premise that systems can learn from the
training data, identify patterns, and make judgments with minimal human interaction. Several
companies across the world use the most advanced and sophisticated tools in their data
analytics in accounting procedures such as “R” and “Python”. These programming languages
are mostly employed by companies to perform highly customised and advanced statistical
analyses. Python is one of the fastest-growing programming languages available today.
Python was originally developed as an object-oriented programming language for use in
software and web development but later extended for use in data research. Python can
perform a wide range of research on its own and integrate with third-party machine learning
and data visualisation software. On the other hand, R is a popular statistical programming
language that statisticians use for statistical analysis, big data analytics, and machine learning.
Facebook, Uber, Google, and Twitter use R for behavioural analysis, advertising
effectiveness, data visualisation and economic forecasting. Both of these programming
languages are used to generate various algorithms that perform regression analysis, detect
data clusters and perform other programming tasks.
3.5 Data Analytics in Auditing
Due to the massive and rapid technological advances, companies are continuously
developing technologies to improve their business strategy and day-to-day operations. Among
these technologies, data analytics has gained popularity across a wide range of organisations,
from corporate and government to scientific and academic disciplines, including accounting
and auditing (Dagilienė & Klovienė, 2019). According to the Institute of Chartered
Accountants of England and Wales [ICAEW], it is vital for the audit profession to keep up
with these changes and be proactive in examining how new technological trends may affect
auditing methods (Joshi & Marthandan, 2018). Therefore, accounting professionals around
the world need to adapt to these technological disruptions. Audit data analytics is believed to
have a greater depth of capabilities and broader concept than standard analytical methods
because it involves powerful software tools and statistically demanding methodologies (Joshi
& Marthandan, 2018).
Auditors need to adapt data analytical skills and big data technologies to efficiently
execute their operational functions. Both internal and external auditors use data analytics to
perform audit functions such as continuous monitoring, continuous auditing, and full-set
analysis when sample audits fail to produce a good quality of results (Vasarhelyi et al., 2010;
Protiviti, 2020). Data analytics helps auditors to extract useful insights from large volumes of
datasets in real-time, allowing them to make evidence-based decisions. In addition, data
analytics may assist auditors in the following areas –
a) Providing audit evidence by analysing the general ledger systems of corporations in-depth
(Malaescu & Sutton, 2015).
b) Detecting fraud and improving other aspects of forensic accounting (Joshi & Marthandan,
c) Assisting in the detection of anomalies and trends, as well as the comparison of industry
data in risk assessment (Wang & Cuthbertson, 2015).
d) By incorporating external data, auditors can provide services and resolve issues for clients
that are beyond their current capabilities (Earley, 2015).
The American Institute of Certified Public Accountants [AICPA] and Rutgers
Business School announced a research initiative in December 2015 that focused on the
advanced use of data analytics in auditing (Tschakert et al., 2016). The initiative’s aim was to
develop a better understanding of how data analytics can be integrated into the audit process
improve the quality of auditing work (Tschakert et al., 2016). Potential advances in data
analytics include higher-quality audit evidence, minimising repetitive tasks, and better
correlations of audit tasks with risks and assertions (Tschakert et al., 2016). Both the AICPA
Assurance Services Executive Committee and the Auditing Standards Board Task Force are
working on a revised audit data analytics guideline that will be more acceptable and workable
than the existing analytical procedure guideline. The new guideline will adopt most of the
existing audit analytical procedure guidelines but will also introduce separate guidance on
how audit data analytics can be integrated into the overall audit process (Tschakert et al.,
2016). Another project is also underway to develop voluntary audit data standards that are
likely to support the extraction of data and expedite the utilisation of audit data analytics, and
a mechanism to illustrate where audit data analytics might be implemented in a typical audit
program (Tschakert et al., 2016).
3.6 Data Analytics in Financial Accounting
Implementing data analytics can help a company to increase its profit margins and
gain a competitive advantage. For example, according to a recent survey of accounting
professionals conducted by software vendor Sage (2018) of 3,000 accounting professionals
globally, 56% of accountants believe their practice revenue has risen over the last 12 months
due to the adoption of automation. Companies with a limited application of data analytics in
their business operations may be forced out of business in the long run. Data analytics is the
most important area where technological transformation can happen fast enough for an
organisation and its senior-level executive to adapt. Thus, the change management concept
may be considered to take advantage of data analytics.
The measurement of accounting information has increasingly lost its informational
value due to the significant decline of market value explanation provided by accounting
variables (Lev & Zarowin, 1999). However, this decline in information value is particularly
evident for emerging knowledge-intensive businesses with a higher intangible intensity,
which claims a steadily expanding share of a country’s economy (Srivastava, 2014). The real-
time processed data has been appreciated by the economy which is measured through
quarterly or annual financial statements (The Economist, 2002; Vasarhelyi & Greenstein,
2003). It is considered to be the most useful information for the external capital providers and
capital market participants to understand and interpret the information so that effective
decisions can be made (Krahel & Titera, 2015). Current technologies of accounting recording
systems are providing more absolute measurements in terms of intangibles, inventory
valuations (e.g., LIFO, FIFO) and estimation of depreciation compared to traditional
accounting and reporting systems (Lev & Zarowin, 1999; Lev, 2000). For example, most
students feel unable to learn several old-fashioned inventory accounting methods, while
recently, firms are integrating radio-frequency identification (RFID) or barcodes to measure
and report the actual inventory (Vasarhelyi et al., 2015).
Another set of real-time financial statements reveals more accurate and real-time
business-related information compared to traditional accounting and reporting systems that
help the internal and external user to make efficient and convincing investment decisions
(Gal, 2008). This technology-enabled reporting provides more relevant and descriptive
disclosure, supporting the analysis and provisioning of management, auditor, and stakeholder
dashboards. Thus, the value of the traditional accounting methods that report information has
lost its appeal to information users such as external capital providers and several capital
market participants. Businesses have added a large amount of data to their traditional data
repositories, resulting in massive databases in their ERP systems, of which only a small
portion is relevant for financial reporting. Although these “structured data stores” in ERPs are
large, they could be overwhelmed by increasing the amounts of less structured data.
3.7 Data Analytics in Management Accounting
Data analytics in management accounting can be defined as how information
technology tools are implemented to analyse and interpret a company’s managerial activities
(Spraakman, Sanchez-Rodriguez, & Tuck-Riggs, 2020). The traditional role of management
accountants has focused on participating in management decision-making, designing,
planning, and performance management systems, budgetary control, product profitability, and
assisting senior management in formulating and implementing organisational strategies
(Association of Chartered Certified Accountants [ACCA], 2020). However, as big data and
data analytics become more prevalent, their current practices, working data and tools,
interactions with management and other departments, and requests for new areas of
competence are all likely to expand (Nielsen, 2018; Tiron-Tudor, Deliu, Farcane, & Dontu,
2021), which include channel profitability, predictive accounting, and business analytics
(Appelbaum et al., 2017).
Data analytics is viewed as a critical skill for any accounting professional. Currently,
management accountants are uniquely prepared to assess an organisation's data requirements
because they have a comprehensive understanding of it and its existing information systems.
This helps them to delve into both financial and non-financial data to drive better decision-
making (Association of Chartered Certified Accountants [ACCA], 2020; Tiron-Tudor et al.,
2021). Therefore, management accountants must comprehend data analytics and be able to
convey their findings to upper management, as well as have the business understanding and
commercial acumen to assess data analytics results and provide significant commercial
analysis and supply suggestions. Management accountants also use data analytics to add value
by improving productivity, profitability, and cash flow, as well as managing customers,
innovation, and intellectual property, which focuses on new perspectives and ensures business
organisations’ long-term viability.
From the academic perspective, although researchers have demanded that prior
research on data analytics in management accounting lacks sufficient empirical evidence
(Cokins, 2014; Dinan, 2015; Lin, 2016), several conceptual papers have explored the
implication of data analytics in management accounting (Schneider et al., 2015; Appelbaum
et al., 2017). Vasarhelyi et al. (2015) illustrate how accounting evolved from paper-based
aggregate information records through charts of accounts/general ledgers and then to big data
or data analytics. Consistent with this notion, Pickard and Cokins (2015) argue that
accountants are in the best position of a business, allowing them to own and drive a huge
number of data analytics to make effective business decisions. They also report that data
analytics are likely to be incorporated by the accountants so that they can analyse data ranging
from general financial ratios to more sophisticated data analytics techniques such as
clustering, regressions, and factor analysis. Similarly, Schläfke, Silvi, and Möller (2013)
document that accountants have prior knowledge of financial reporting and can therefore
implement various contemporary data analytics tools to prepare several financial and non-
financial reports. Moreover, Marr (2016) reports that 45 companies throughout the world are
using big data in management accounting such as Walmart, Netﬂix, Amazon, and Airbnb, etc.
Nielsen (2018) argued that management accounting research should capitalise on the
opportunity provided by the data analytics movement to develop theories and ideas for fact-
based judgments with high external validity. Similarly, Arnaboldi, Busco, and Cuganesan
(2017) look into the interaction between technology-enabled networks and the accounting
function to spur more research and debate on the topic.
4. Artificial Intelligence (AI) in Accounting
Artificial intelligence (AI) is the simulation of human intelligence in machines. It
allows machines to think, learn and solve problems in the same way that human brains do.
The use of artificial intelligence enables machines to perform the necessary tasks by
mimicking the behaviour of human intelligence. Several companies worldwide have
implemented artificial intelligence (AI) in their accounting functions and analysis in order to
obtain the benefits of artificial intelligence (AI). For example, according to a recent survey of
3,000 accounting professionals globally conducted by software vendor Sage (2018), 66% of
accountants believe they will invest in artificial intelligence (AI) to automate repetitive and
time-consuming tasks, while 55% assert they will use artificial intelligence (AI) to improve
their business operations.
Artificial intelligence (AI) was first introduced into accounting more than 30 years ago
(Abdolmohammadi, 1987; Brown, 1989). Specifically, artificial intelligence (AI) was
employed in financial accounting and auditing in the late 1980s and early 1990s (Barniv,
Agarwal, & Leach, 1997; Etheridge & Sriram, 1997). After this period, significant advances
were made in other areas of accounting and finance. Companies throughout the world are
reaping enormous benefits by integrating artificial intelligence (AI) into accounting tasks,
which can be classified as internal or external. For internal purposes, artificial intelligence
(AI) is used in accounting functions to produce more accurate and acceptable financial
statements. Artificial intelligence (AI) can offer information faster than humans due to its
competency and consistency in analysing and interpreting accounting data (Petkov, 2020). As
a result, the accounting functions performed by artificial intelligence (AI) can provide quick
and accurate output. This instant output improves the timeliness of accounting information
and helps users in making informed decisions.
Artificial intelligence (AI) that has been well-trained to attain accuracy, i.e., that has
been programmed to follow accounting rules, would produce more accurate and consistent
accounting information. Consistent with this notion, incorporating artificial intelligence (AI)
in accounting functions can eliminate accounting errors and human errors when preparing
financial statements. Furthermore, several companies throughout the world have adopted
artificial intelligence (AI) with predefined “trained principles”, and these companies are
benefiting from improved financial reporting comparability. Accounting firms are currently
integrating artificial intelligence (AI) into auditing functions to ensure compliance and reduce
managers’ intentional errors. This would limit the ability of managers to use certain
formulants’ financial functions. Despite the fact that just a few accounting firms have
included artificial intelligence (AI) in their auditing functions, the majority of them use
artificial intelligence (AI) to manage audit risk (Zhao, Yen, & Chang, 2004).
Furthermore, the most notable benefit of incorporating artificial intelligence (AI) into
a company’s accounting function is the minimisation of future costs. In the long-term,
reliance on artificial intelligence (AI) will reduce having to depend on human operations and
improve the efficiency and accuracy of a company's financial reporting. Primarily, there are
certain fixed costs associated with the design, development, and implementation of artificial
intelligence (AI) in a company's accounting function, as well as some indirect costs associated
with monitoring and confirming artificial intelligence (AI)’s performance. Furthermore,
another important cost of artificial intelligence (AI) is its reliance on the entire system
because if the system is hacked/attacked and no human backup assistance is available, it will
become a liability rather than an advantage to the company (Petkov, 2020). For this reason,
proper maintenance of the artificial intelligence (AI) system is an important function of a
company before implementing artificial intelligence (AI). Moreover, Petkov (2020) identifies
the following potential accounting functions (as shown in Table 3) that can be delegated to
artificial intelligence (AI).
Table 3: Potential accounting functions to delegate to an artificial intelligence (AI)
Human Functions Artificial Intelligence (AI) Functions
Cash Manual Input of Cash Receipts and Payments [Use of Journal
Entries (hereafter, J/E)].
Bank Reconciliation performed by individuals reconciling
outstanding checks, deposits, errors, interest, etc.
To scan cash payments/receipts into general ledger (G/L) similarly to how it is done in a
Bank Deposit/Withdrawal (regardless of their nature).
To train AI to perform this reconciliation by analysing reconciling inputs and generating
bank reconciliation report for revie
J/E prepared based on contractual obligation (be it oral or verbal,
followed by invoice).
J/E for collection based on receipt of payment.
J/E for allowance for doubtful accounts, based on estimations and
These tasks could be delegated to AI. Specifically, the receipt of cash payments via wire
transfers or checks at the point of scanning could result in J/E in the system (similar to Bank
Inventory J/E for purchases and sales.
J/E based for lower of cost or market (LCM) value, obsolete
inventory, etc. (based on historical data).
Delegate to AI capable of identifying movement of inventory (ins and outs) and prepare
Delegate the estimation of LCM to AI by providing inputs—costs (would come directly
from G/L and market, from standard created tool sheet capturing market values of inventory
from third parties.
Prepaid J/E to record initial asset.
J/E to record period end expense based on use.
Delegate to AI by training it to scan bank statements and identify such transactions.
Humans could continue to be involved to determine duration. Make periodic timely
Investments J/E for initial recording.
J/E adjustments based on cost or equity method chosen.
AI to scan bank statement and identify such purchases, record J/Es.
To train AI to analyse F/S of invested companies and seek the activity—such as NI and
Dividends and prepare J/Es automatically.
Property, plant, and
J/E to record PPE purchases; or disposals if any.
J/E for depreciation expense, already done by AI.
AI to scan bank statements and identify transaction related to PPE purchases and disposals.
Intangibles J/E to record intangible purchases; or disposals if any.
J/E for amortization expense, already done by AI.
for goodwill impairment.
AI to scan bank statements and identify transactions related to intangible purchases and
Train AI to perform impairment testing by providing key inputs from other departments.
J/E prepared based on contractual obligation (be it oral or verbal,
followed by receipt invoice from vendor).
J/E for payment to vendor.
These tasks could be delegated to AI. Specifically, the payment of cash payments via wire
transfers or checks at the point of scanning could result in J/E in the system (similar to Bank
J/E prepared based on assumptions and historical data.
Train AI to analyse such data and make on demand J/Es based on this data.
Unearned Revenue J/E to record initial liability.
J/E to recognize revenue based on use.
Delegate to AI by training to analyse budgets and tie the budgets to actual revenue order
and its performance.
Notes payable (N/P) J/E to record assumption and repayment of N/P.
J/E for interest payment
To teach AI to scan bank statements and identify such transactions.
J/E for interest payment should be based on the contract and therefore could be delegated.
Refer to A/R and Inventory
Refer to A/R and Inventory
Refer to A/P and Inve
Refer to A/P and Inventory
Source: Adopted from Petkov (2020)
This chapter aims to provide an overview of the growing role played by big data, data
analytics, and artificial intelligence in the accounting profession. Big data, data analytics, and
artificial intelligence (AI) have become emerging buzzwords for accounting professionals in
recent years. It is becoming increasingly important for accounting professionals to understand
the possibilities that big data and data analytics offer to their clients and the industry.
Accounting is data-driven, and subsequently, big data can assist accounting professionals in
delivering greater value to their clients. Accounting professionals’ job responsibilities are
rapidly changing due to the development of big data, data analytics, and artificial intelligence
(AI). It is argued that these emerging technologies pose a threat to accounting professionals.
Experts argued that anyone who does not embrace this change and appreciate the new
technologies would be left behind (Chartered Institute of Management Accountants [CIMA],
2022). However, accounting professionals have long-standing reputations in the market for
their outstanding technical and problem-solving skills as well as their ability to make data-
In fact, a recent survey of accounting professionals conducted by software vendor
Sage (2018) reports that 83% of clients now have higher expectations from their accountants
than they did five years ago. Sage (2018) also reports that 39% of accountants describe
themselves as early adopters of technology after surveying among 3000 accountants globally.
Individuals who are able to discern patterns in data and translate them into compelling
strategic narratives will find themselves at the centre of the twenty-first-century business
world (Association of Chartered Certified Accountants [ACCA] and Institute of Management
Accountants [IMA], 2013b). Therefore, in a rapidly changing business environment,
accounting professionals can seize numerous opportunities by leveraging big data, data
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