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Sallem, N. R. M., Hussain, N. H. C., Muhmad, S. N., Adnan, N. S., & Halmi, S. A. H. (2024). Artificial Intelligence (AI) Revolution in Accounting and

Auditing Field: A Bibliometric Analysis. Advances in Social Sciences Research Journal, 11(9.2). 64-78.

URL: http://dx.doi.org/10.14738/assrj.119.2.17402

important insights and publication trends into artificial intelligence in the area of

accounting and auditing.

Keywords: Artificial Intelligence, Accounting, Auditing, Bibliometric Analysis,

Digitalization.

INTRODUCTION

The integration of artificial intelligence (AI) in the fields of accounting and auditing is

transforming traditional practices, offering unprecedented opportunities for efficiency,

accuracy, and strategic decision-making. As AI technologies continue to evolve, their

applications within these domains are expanding, ranging from automating routine tasks to

providing advanced analytical capabilities that support complex financial assessments.

Traditionally, accounting and auditing functions relied heavily on manual processes and human

expertise. However, with the advancement of information technology (IT), there has been a

significant shift in how these functions are performed. Recognizing the importance of IT,

organizations, accountants, auditors, professional bodies, academics, and regulators have

increasingly focused on enhancing accounting and auditing processes through technology [1].

Technological, regulatory, and economic changes will persist in challenging the profession’s

traditional methods and mindsets, which is beneficial. Accountants and auditors must quickly

adapt to changes in user demand and the development of new and emerging metrics of

organizational performance beyond traditional financial statements [2].

Using AI including machine learning, deep learning, big data analytics, data mining, and cloud

computing in accounting and auditing practices has leveraged the possibility of processing

massive volumes of financial data, facilitating the identification of patterns, trends, and

anomalies [3]. The adoption of AI in accounting and auditing has the potential to revolutionize

these professions. For instance, AI-driven tools can enhance the accuracy of financial reporting,

improve fraud detection mechanisms, and streamline audit processes. [4] concludes that

digitization in accounting facilitates communication among economic actors, ensuring business

continuity during crises, and real-time information access. The decision to digitize is influenced

by internal factors like organizational culture and external factors like telecommunications

infrastructure. It shifts accountants' roles from record-keepers to strategic partners, enhancing

overall economic performance.

However, despite these benefits, the implementation of AI also raises ethical and regulatory

concerns, particularly regarding data privacy, job displacement, and algorithmic bias. Managers

should establish clear procedures for employees to follow, ensuring guidelines are well

understood. Without this, time and resources could be wasted, affecting business value. It is

crucial for maintaining internal stability, as unclear leadership might cause staff to lose trust,

leave, or resist new technologies, undermining AI's value [5]. In the short run, automation

seems beneficial, but long-term negative consequences emerge. Automation is a priority across

industries, with AI leading it. Operational excellence and skill development are crucial, as

lacking skills hinder progress. Sustainability is central to all initiatives [6].

This study employs bibliometric analysis to map the landscape of AI research within accounting

and auditing. By identifying key trends, influential authors, and prominent research themes, it

highlights the most cited works, prolific contributors, and collaborative networks. The findings

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Advances in Social Sciences Research Journal (ASSRJ) Vol. 11, Issue 9.2, September-2024

Services for Science and Education – United Kingdom

from this analysis elucidate the current state of research and suggest potential directions for

future studies. These insights will enhance our understanding of how AI is reshaping these

fields and inform practitioners, researchers, and policymakers about the current and future

implications of AI technologies.

LITERATURE REVIEW

Bibliometric Analysis

This technique originated in the early 20th century and has seen significant advancements over

the decades. Bibliometric analysis is a powerful tool for assessing and understanding academic

research. It provides valuable insights into publication trends, research impact, and

collaboration patterns. Bibliometric analysis is a methodological approach used to

quantitatively analyze academic literature and research output. This method can reveal trends

in specific fields, identify influential works and authors, and map the development of research

areas over time [7 -9]. Bibliometric analysis is gaining popularity in various disciplines

including accounting and auditing fields. It has become a valuable tool for evaluating the

development and influence of research within these disciplines.

[10] conducted a bibliometric and coding analysis to investigate the relationship between

technology and disruption in accounting literature, with a specific focus on blockchain's impact

on business processes and digital interactions. Similarly, [11] examined the scalability and

business applications of blockchain within accounting and auditing, highlighting its potential to

enhance transparency and efficiency. A study by [12] utilized bibliometric and content analysis

to analyze the scholarly discussion on blockchain technology in accounting and auditing. They

employed tools like the Bibliometrix R-package and VOSviewer for network visualization of

keywords and bibliographic coupling. This study also identified research gaps, such as the need

for comprehensive studies on blockchain's regulatory and governance aspects in accounting

and the exploration of risks and challenges associated with new technologies in auditing

(MDPI).

Bibliometric analysis offers a comprehensive and quantitative approach to understanding and

evaluating the academic literature. Its helps researchers gain a deeper understanding of their

fields, track the evolution of research topics, and assess the impact of their work. As scholarly

databases and bibliometric tools continue to evolve, the use of bibliometric analysis in

academic research is likely to expand, providing even greater insights into the complexities of

scientific communication.

Artificial Intelligence (AI) in Accounting

The integration of artificial intelligence (AI) technologies has significantly transformed

traditional accounting practices, introducing a range of efficiencies and capabilities that were

previously unattainable. The adoption of AI in accounting has led to a significant reduction in

manual tasks, allowing accountants to focus on more strategic activities and has increased

productivity and accuracy in accounting practices [13-14].

AI has revolutionized accounting through the automation of repetitive and time-consuming

tasks. Technologies such as robotic process automation (RPA) can handle functions like data

entry, invoice processing, and payroll, which frees up accountants to focus on more strategic

activities. For example, AI-powered systems can automatically categorize transactions,