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Artificial intelligence and the
changing landscape of accounting:
a viewpoint
John Kommunuri
Department of Accounting, Auckland University of Technology,
Auckland, New Zealand
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Received 4 July 2021
Revised 31 January 2022
26 April 2022
Accepted 27 April 2022
Abstract
Purpose – The study aims to explore the changing landscape of accounting and the role of emerging
technologies in the accounting environment. The author presents viewpoints on the influence of artificial
intelligence (AI), machine learning (ML) and other subsets in accounting, emphasising the increasing need for
and significance of these applications. The viewpoints could provide researchers and practitioners with a
meaningful overview of knowledge and research agenda.
Findings – The role of emerging technologies in accounting and various opportunities and challenges in
implementation are discussed. In addition, possible future research directions are identified.
Research limitations/implications – The paper does not contain empirical findings.
Originality/value – This paper expresses the author’s viewpoints regarding the impact of AI and ML on
the changing accounting environment.
Keywords Artificial intelligence, Machine learning, Robotic process automation, Accounting
Paper type Viewpoint
Introduction
The paper presents the author’s viewpoints in response to the call for papers for the special
issue on “Accounting in Transition: Influence of Technology”. Digitalisation is a new
opportunity, and recent technological advancements are driving a transformative change in
accounting and finance. With the emergence of artificial intelligence (AI) and its subsets,
namely, machine learning (ML), robotic process automation (RPA), artificial neural
networks (ANN) and deep learning (DL), the accounting industry is undergoing an immense
and extensive transformation revolutionising the delivery of accounting, finance, audit and
advisory services. Contemporary accounting is immensely data-driven, automated and
depends more on intelligence systems than information systems. These new intelligence
systems have created longitudinal shifts in the development of accounting by creating an
enormous demand for change in the roles and tasks of human and AI-based actors, firmly
establishing that AI is a promised way forward for accounting.
AI is an overarching term where technology can think, learn, perform tasks with acumen
and mimic human intelligence. It is the science and engineering of making intelligent
machines and embracing human-like intelligence in machines. It demonstrates the ability of
a system to correctly interpret the given data, learn from such data and use those learnings
to achieve specific goals. It is similar to cognitive technology with an intelligence level
appropriate for performing cognitive tasks in accounting and related areas. In accounting,
the idea of AI is not new though it has only recently started to gain momentum in the
accounting literature and is growing in accounting practice. The growing attention on AI is
due to the technological maturity achieved through the use of computational calculations,
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algorithms and the ability to analyse huge quantities of information and data in real time
(Sestino and de Mauro, 2022).
In New Zealand (NZ), only a handful of local academic conversations on the use of AI in
accounting have been published in peer-reviewed journals. Few articles are published on the
topic in business and professional accounting magazines. A recent journal article published
in Pacific Accounting Review has highlighted the impact of emerging technologies on the
accounting curriculum and the accounting profession (Wang, 2021). A special issue of the
Pacific Accounting Review dedicated to forensic accounting teaching and research notes that
forensic accountants have an opportunity to integrate big data analytics to help advance
their discipline and offer predictive fraud detection services (Dunstan and Gepp, 2018; Botes
and Saadeh, 2018). Reid (2019) finds that AI is already playing a vital role in boosting
efficiency and productivity within the NZ business community. In terms of accounting
applications, Reid (2019) notes that Xero has AI-supported features within its cloud-based
accounting software to boost efficiency and reduce repetition and proposes using
timesaving AI-driven technology that may change the billing model away from time-based
charging.
An article featured in the CPA Australia magazine (INTHEBLACK) in February 2020
highlights technologies such as AI and RPA that “promise to catapult the accounting
profession into a new realm that will be both challenging and exciting” (Rollins, 2020, p. 35).
It will allow rapid data processing, automate well defined and repetitive tasks such as bank
reconciliations and supplier invoice processing and challenge people to take on higher-value
work. On the other hand, an article published in early 2019 by the NZ CIO magazine reports
that while RPA has consistently been promoted on the grounds of exceptional
improvements, it achieved only moderate improvement in cost-saving, service speed and
accuracy (Oshri and Moore, 2019).
Transitioning from the former system of bookkeeping and reporting to the use of
accounting software for recording and reconciliation of transactions through to their
reporting, the recent trend is a great transition to the use of advanced digital technologies of
AI. With these technologies, the accounting functions are further enhanced and moved
beyond financial reporting and compliance to the detecting of misstatements and
irregularities using algorithms and data structures and using code languages such as
Python, R, statistical analysis system, improving accounting estimates (Ding et al., 2020),
developing AI-powered regulation models such as RegTechs, making AI-based liquidity
forecasts for liquidity steering, supporting fraud detection systems, supporting bond and
debenture emission in online auctions based on blockchain technology, predicting litigation
risks, analysing management restatement disclosures to correct intentional misstatements
or correct unintentional errors. Damerji and Salimi (2021) reason that AI provides a bridge
for information systems to be transformed into intelligence systems with improved
automation and optimisation of information systems. They say that information systems
help with capturing, storing, analysing and evaluating the available data to communicate
the best output as a piece of information. Whereas, AI’s intelligence systems are cable of
learning, reasoning, adapting, detecting, predicting and performing tasks similar to humans.
Hence, AI systems are considered more intelligent than information systems.
The multiple functions embedded in AI will undoubtedly benefit accountants in tasks
such as recognising, learning and reasoning through computers. These functions help them
process large data sets or documents containing purchase orders and sometimes replace
human employees in routine tasks such as collecting data and recording transactions.
Automating tedious and mundane tasks that accountants have to deal with daily can be
possible through AI and its subsets. Furthermore, as accounting is becoming a strategic
task, AI will enable accountants to move up the value chain and play a more strategic role.
Therefore, accountants have excellent opportunities to imbibe more value-added and
creative tasks into their daily routine.
However, the use of AI-based technologies is currently in its infancy, and little attention
is paid to investigating the role, relevance and impact of these technologies in accounting
(Mancini et al., 2021). Though there are numerous academic and professional publications on
the use of intelligent technologies in various areas such as commercial and marketing
activities (Lombardi et al., 2020), strategic processes and business models (Schiavone et al.,
2021), yet a little attention is paid to the role and relevance of these technologies in the
accounting environment. Hence, the research investigating the use of AI in accounting and
how accountants could tap into a higher potential to enhance performance is still at an early
stage. Besides, there is little knowledge of the extent of training and skill development
needed to execute various accounting functions effectively and how these technologies can
be optimally utilised in the public accounting context. Compared to other sectors, the
financial sector is one of the industries where AI has been more intensively applied (Biallas
and O’Neill, 2020). Due to its significant application in accounting, more research on AI is
warranted on the extent of the use of its techniques in accounting domains and the
ramifications for the future of the accounting profession. In addition, there are calls for a
greater understanding of ML principles for accounting graduates (AACSB, 2014), which
express the need for a better understanding of the role of accounting academics in the future
of AI.
The paper aims to fill the gap in our knowledge by evaluating the transformation of
accounting and how AI technologies have become crucial in the accounting profession. The
paper revisits the uses of AI-based applications, i.e. ML, RPA and ANN, in accounting,
emphasising the need for and significance of these applications. Motivated by the need to
thoroughly understand the context surrounding the adoption of AI in accounting, the paper
reflects on the core role that AI-based technology currently plays, identifying the emerging
themes surrounding the rise and implementation of AI, and presents the shifting dynamics
of how AI and its subsets are useful in accounting. Positioned on these themes, the paper
presents several viewpoints on using AI and ML in the coming years.
The paper makes four main contributions: First, the paper adds knowledge to the fastgrowing literature on the influence of AI-based technology on the accounting profession in
view of the changing roles and tasks. Second, identifying the influence of AI-based
technology on professional accounting occupations and related tasks and skills makes a
practical contribution to making the future of AI-based accounting more tangible for
management, accountants and accounting academics. Third, it highlights the challenges
and risks associated with the digitalisation of accounting processes. Finally, the paper
provides suggestions to inquire into various avenues and challenges concerning the
increased use of AI in accounting.
Accounting transformation
The reports of some professional accounting bodies express that businesses are in the early
stages of introducing and implementing AI technologies into their practices. Though the
literature surrounding technology is extensive, the literature on the role of AI and its subsets
in accounting and finance is still at the exploratory stage. The primary goal in designing
and developing AI technology was undeniably to tap into a higher potential and make it a
man-plus-machine strategy. In accounting, integrated systems, cloud solutions, blockchain
technology and AI-related technology have changed and will change the workflow and
processes of accounting in the future. Large accounting firms report significantly higher
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expertise in using AI than their non-Big4 counterparts. As there has been progress in
the evolution of technology for applying AI in the accounting industry, these firms are
launching numerous projects costing billions of dollars in investment in AI. For instance, the
PWC plans to spend $12bn and hire more than 100,000 new staff in the area of AI by 2026
and offer new products that feature AI and ML, followed by KPMG to shell out $5bn over
five years and Ernst&Young around $1.5bn (Maurer, 2021).
The current literature mostly maintains a theme of “man-versus-machine”. Over the past
decade or so, there have been numerous concerns that AI could replace human tasks.
However, the human part of critical analysis and judgement contributes to AI models’
knowledge, output and recommendations. In that sense, AI’s computational power does not
displace human performance, but rather it complements it. We cannot define machine
cognition outside of the relationship to the human brain and behaviour. As the volume of
information increases, the demand for processing skills and processing capacity increases.
AI and human intelligence are inextricably linked. The complementarity between
accountants and machines provides guidance on how accountants can or should adapt to
survive and thrive in the age of AI. AI performs high volume, repetitive and computerised
tasks without getting tired and enhances accountants’ service capabilities. Accountants can
re-engineer every stage of the record-to-report process by redesigning the activities around a
portfolio of AI-based technologies.
AI enables full automation of various time-consuming tasks such as payment transaction
testing and extracting supporting data for corroborative evidence in substantive testing for
auditors. It helps scan keywords and patterns in complex electronic documents to extract
sales, contracts, expenses and other relevant decision-making information. AI tools can also
spot any unusual amounts recorded in transactions and cause accounting routine tasks to be
much lower in the coming years. For example, digitalised invoices can be arranged for
customers using optical character recognition (OCR) technology. Account checks and
reconciliations can be done using smart bots, ensuring that the data is accessed only with
password permission and the actions are tracked using an audit trail. Leitner-Hanetseder
et al. (2021) suggest that the AI-based technology will perform intercompany reconciliation
work, assets and liabilities valuation and related reports preparation in the near future.
ML, a significant subset of AI, is extensively applicable in a wide range of disciplines,
including engineering, computer science, marketing, bioinformatics, finance and accounting.
ML uses statistical tools to learn from data and then applies algorithms to solve professionrelated problems. ML is classified into supervised, semi-supervised, unsupervised and
reinforcement learning, and the algorithms can be categorised into classifiers, regression
and clustering. ML algorithms will help categorise and interpret the digitalised data without
human help and post them to the respective account. Though ML applications are new in
accounting, their use in accounting and finance-related functions is growing fast and
becoming essential tools in accounting.
With the increasing volumes of Web-based data, ML improves the efficiency and
accuracy of data processing models without human input. It uses algorithms to process and
uncover patterns inherent in large volumes of data to develop predictive models through
supervised and unsupervised learning. As a result, it generates more accurate loss estimates
than managers’ actual estimates, enhancing estimates’ reliability and consistency. The
application of ML on audit engagements is the most promising advanced technologies under
consideration, as it helps to evaluate complex accounting estimates for auditors (KPMG,
2016). Using ML is the next level of intelligence for analysing numbers already being used
in other areas such as statistical and mathematical modelling. ML algorithms developed
in statistics and computer science have proven powerful for predictive tasks
(Mullainathan and Spiess, 2017) and does not require functional forms and mathematical
formalisation to establish predictions (Bertomeu, 2020). Some prior studies used ML to
analyse financial statements, measure information content and predict errors and
irregularities. ML’s empirical methods can scrutinise through accounting data sets with
several variables to detect and interpret patterns present in ongoing misstatements. For
example, Bertomeu et al.’s (2021) analysis identified misstatements, found differences
between misstatements and irregularities, compared algorithms, examined one-year and
two-year ahead predictions and interpreted groups at greater risk of misstatements.
An important area of accounting research is the development of effective methods for
accounting fraud detection on a timely basis, as it offers significant value to investors,
regulators and auditors. Research on fraudulent accounting mechanisms has been
conducted largely in the accounting and finance areas. However, a traditional model
(Dechow et al., 2011) uses the independent variables that account for fraud in parametric
models to detect a concurrent event. The model exhibits a good in-sample fit and confirms
the theoretical conjecture. The vast empirical literature on detecting fraud using
“discretionary accounting accruals” has certainly made good progress in detecting
accounting fraud. However, these models neglect a number of explanatory variables that
might bring additional predictive power and hence fail to extract useful predictive
information from the available financial data. Using ML methods, incorporating a large
number of explanatory variables into a model can detect accounting fraud more effectively
as better data, improved algorithms and accuracy of computer vision will increase the
accuracy rate. Cecchini et al.’s (2010) method uses ML based on support vector machines
with a financial kernel (SVM-FK) by mapping raw financial data into a list of predefined
ratios. This model outperforms Dechow et al.’s (2011) model, but it uses only one year for
validation with the matched fraudulent and non-fraudulent firm years in one testing period.
Hence, its implementation procedures are subject to look-ahead bias. Ensemble learning, a
primary ML paradigm, has been used in prior studies for fraud detection to overcome these
issues and biases. It combines the predictions of more than a year of a set of base estimators
to improve the generalisation ability and robustness (Bao et al., 2020). Applying ML tools to
accounting research in optimising prediction enables accounting researchers to draw new
theoretical insights from a better understanding of complex data.
Another enabler of the digital revolution is the Predictive Analytics Integrator. Financial
managers have to identify the critical business areas where predictive and ML techniques
can be used to automate the business processes, improve efficiency and reduce costs. For
example, in the cash management area, the manager has to categorise cashflows by liquidity
items such as cash flows from operations and cash flows from investments. ML capabilities
and comparing the actual and predicted results will help the manager gain predictive
insights into liquidity items. Then, the actual liquidity items will be replaced by the
predictive items. Accountants enable financial markets to take account of the companies’
impact on society and the environment. AI-based data-gathering technologies such as
“cognitive” search engines help accountants tag data sets that can be shared with other
organisations. Technology enables accountants to gather data and calculate a monetary
value for “externalities”. Milana and Ashta (2021) state that to scrape environmental, social,
and governance information from company reports, regulatory databases and other data
sets, data mining and ML technologies can be used for reliable results.
The second important subset of AI is RPA. An RPA robot is used to automate and speed
up the most onerous tasks such as validating data, accessing the database, creating relevant
documents and uploading the repository. Audit data standards will be replaced by RPA,
which requires little programming. Still, programming languages such as Python and R will
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greatly help deploy RPA-based audit-related tasks and help reduce the processing time from
days to minutes. RPA will decrease the time and cost of data processing and improve
process accuracy, consistency, traceability and decision quality. It offers unique capabilities
and advantages over previous technologies, leaving the functions of capturing, segmenting
and analysing data to RPA, reducing processing costs and enhancing value-added tasks.
Greater benefits will be realised if RPA is utilised within a much bigger networked
technological solution attempting to optimise a service value chain instead of just serving as
a local solution to an isolated case of inefficiency (Oshri and Moore, 2019).
The third useful subset of AI used in accounting is ANN, a powerful qualitative method
for analysing complex information. Power learning in ANN is much closer to human
performance, improving the effectiveness of information systems. For example, accounting
data have complex relationships between its various components, and at times, it makes
them difficult to analyse and requires more outstanding human expertise. Nevertheless, the
computational techniques in ANNs have tremendous potential to solve problems, make
market forecasts and analyse financial statements. In addition, ANNs perform analytical
review procedures, and risk assessment uses algorithms in classification tasks.
Going forward
AI is still an evolving technology, but its usability and application in accounting are wideranging. Many AI-enabled applications are already in use to perform repetitive tasks with
greater consistency and a clearer audit trail. Some examples include processing supplier
invoices, handling bank reconciliations, counting inventories, inspecting fixed assets and
reading contracts or other documents to generate meaningful insights. The impression of
this new technology, so far, is that it will shake up the accounting profession by providing
an excellent opportunity for accountants to upskill in order to take on higher-value work.
Potential applications of AI in accounting are wide-ranging. For example, it offers the
opportunity to undertake predictive fraud detection services by using advanced data
analytics techniques to gain insights based on the behaviour of the data (Dunstan and Gepp,
2018; Botes and Saadeh, 2018). AI-enabled tools such as RPA and advanced data analytics
can enable auditing of the entire transaction dataset, significantly improve audit efficiency
and effectiveness and support continuous monitoring (Wang, 2021). It is worth noting that
accountants are already playing the role of business enhancers rather than information
providers, and hence embracing AI is very much needed. Identifying the potential niche of
AI technology and assessing the impact on accounting practice in the near future is needed.
Rather than adopting a wait-and-see approach, accounting firms should embrace AI
technologies to build on their existing strengths and develop strategies to implement
AI-enabled environments in the workplace. The accounting and finance sector is well placed
to reap the benefits that AI offers. Hence, there are calls to understand ML principles by
accounting graduates better.
Bakarich and O’Brien’s (2021) survey responses of public accounting professionals
indicate that both ML and RPA are currently not being used extensively by accountants, but
substantial changes are on the horizon. There will be an increased effort to push AI in the
public accounting profession as we advance. The accounting profession recognises the
importance of technological advances for success, particularly for enhancing
communication and engagement with clients and improving efficiency and quality (CPA
Australia, 2019, 2021). However, some accountants do not feel comfortable with the
changing technical knowledge and skills and might push back. But it is inevitable for
the profession to move forward in the direction of AI. New thinking and adaptations are
essential to realign policies and the profession with digitalisation. Because of the data-driven
nature of business and accounting, there is a greater need for training in statistics, data
analytics, programming language well-suited for data analytics (e.g. R and Python) and ML
skills for accounting graduates. Through the adoption of AI, tertiary institutions and
accounting graduates will have an opportunity to acquire AI knowledge which will give a
competitive advantage in the coming periods.
To help accounting graduates gain knowledge and skills in data analysis, data mining
and data reporting should be included in the curriculum. The Association to Advance
Collegiate Schools of Business adopted Accounting Standard 7 in 2014 to include ’the
development of skills and knowledge related to data creation, data sharing, data analytics,
data mining, and data reporting to the curriculum. This ensures that accounting students
are well prepared for their career development. Therefore, integrating the application of AI,
ML and RPA within the accounting curriculum is essential to prepare the next generation of
accountants. In addition, relevant professional training and reforms in the university
curriculum can help accounting students and accountants strengthen soft skills and
creativity and better prepare for the incoming future. Jackson et al.’s. (2022) recent study on
the changing role of accountants with technological trends, the associated changes in skill
demands and how well-prepared early career accountants (ECAs) are for new technology
suggests stronger perceptions of ECAs’ preparedness for new technologies compared to
their managers. It is worth noting some recent efforts made by professional accounting
bodies in this direction. For example, the Chartered Accountants Australia and New Zealand
(CAANZ, 2017) and CPA Australia have issued new accreditation guidelines for accounting
programs that require integrating technology in the curriculum effective from 1 January
2022. In addition, the American Institute of Certified Public Accountants (AICPA, 2021) has
recently published a new “Guide to Audit Data Analytics” intended to encourage auditors to
voluntarily make more use of technology-based audit data analytics, which has the potential
to enhance traditional audit procedures and offer a new way of visualising and analysing
results. The rapid changes in AI are opening new avenues for research on its use in
accounting and auditing.
There is enormous potential for future research on the interface of digitalisation in
accounting. Future research should assess and, if needed, revise the best practice guidelines
for AI in accounting. Future research could explore how automation is changing the role of
accountants, the unique functions that accounting professionals play in their organisation’s
digital transformation and the skills and competencies that accountants should develop to
work alongside their digital colleagues. The underlying AI algorithms are still under active
research, and hence accounting researchers will find it rewarding to develop approaches
tailored to accounting settings. The current AI frameworks are designed for modelling static
data. Although ML provides great benefits in utilising static data sets, only a few
libraries are designed for specific measurements such as time series. AI-based integrated
systems that can access actual data in real-time and develop suitable solutions, suggestions,
forecasts and trends for future planning are to be designed. Future research could propose
new requirements in accounting and pay attention to changes and consider new tasks and
the skills needed in the wake of fast-changing business scenarios.
What are the challenges?
Certainly, having a robust AI architecture that integrates all, or at least most, accounting
functions will be a strategic differentiator. Software robots can easily take over routine tasks
at the workplace but will only slowly take over non-routine tasks and novel situations
requiring judgement and critical thinking. While these new technologies offer greater
efficiency, transparency and higher quality, they create new challenges for accountants as
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their skill set, processes, competitive advantage and market structures shift. With the
increase in opportunities as AI capabilities mature, the journey of the profession to AI
maturity is not an easy one. Despite the rapidly expanding experience with AI tools,
accountants face significant obstacles in successfully adopting and implementing AI
technologies. Petkov (2020) expresses that though there are significant developments and
many attempts to implement technology in accounting, the implementation is rather limited
or not fully undertaken because of the management’s unwillingness to make this cultural
shift toward this technology. These software robots will be dealing with overly complex
tasks such as liquidity planning and fraud detection. Fitting ML tools into time-series and
panel data analysis can often become challenging. Though ML and its subset deep learning
are extensively useful, they are like “black boxes”, which are at times difficult for
accountants to understand and interpret. Machines can talk and detect images, but they
often do not understand what humans say. Commerford et al. (2022) suggest that when
“algorithm aversion” auditors receive evidence from an AI system instead of a human
specialist, they are more concerned that the firm’s AI system might lack the necessary
knowledge and expertise. As a result, they recommend smaller proposed adjustments
which- could have economically significant implications. It may heavily limit regulators,
accounting and auditing firms’ reliance on these techniques due to limited technical
expertise in their use.
Conclusion
The viewpoints signal an important opportunity for university providers, accounting firms
and professional associations to identify recent technological advancements and better
develop technology-related skills among current and aspiring accountants. Aligning with
predictions that accounting’s desired skill set will change by 2025 (World Economic Forum,
2018), businesses, accounting firms and accountants alike should try to grapple with the
changes that the intelligence technologies bring. New technologies should be embraced to
maximise the opportunities they accrue in the years ahead. Given certain constraints in
information processing, AI coupled with human intelligence can deliver greater efficiency,
better transparency, higher quality and better outcomes in accounting. Serious thinking
about the downsides and risks that the increasing application of AI poses is needed.
Machines may make confident predictions based on details but sometimes may not make
sense to humans. Despite many advantages of using technology, automation and AI-based
processes could not perform accountants’ most valuable functions, such as using
professional judgement and analysing financial information. Hence the need for human
accountants and auditors will not go away, at least in the near future.
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org:https://www.hbsp.harvard.edu/product/R1801H-PDF-ENG
Corresponding author
John Kommunuri can be contacted at: john.kommunuri@aut.ac.nz
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