The current generation is characterized by the rapid growth and upgrading of new technology, including universal digitization [1]. Artificial Intelligence (AI) stands at the forefront of this technological upgrading, representing the capacity of machines or software to create and exhibit intelligence [1]. Artificial intelligence is defined as a technology that makes machines smart, which organizations adopt to automate, augment, or replicate human intelligence and analytical decision-making functions. The implementation of AI shifts professionals from routine, monotonous tasks towards data-driven and analysis-based decision-making.

AI is a developing frontier of computational improvement used to address progressively complex decision-making problems [2]. Especially in Accounting and Auditing, AI incorporates advanced technologies such as administered or non-administered machine learning (ML), deep learning (DL), and natural language processing (NLP) [2]. The integration of AI has introduced massive changes to many traditional professions [3], and its application in the accounting and auditing field has become unavoidable because its work nature involves repetitive processes [4]. AI is now treated as a helpful business improvement tool in professions that need technical precision and accurate judgment, such as accounting [1]. AI research in accounting and auditing mainly focuses on financial reporting and audit assignments [5]. Recent advancements in the field of ML and deep learning have brought AI into the limelight of emerging technologies that impact accounting [3]. AI brings considerable promise and concern to the profession of accounting [1].

AI adoption also has several critical challenges. Different ethical issues related to the usage of AI in the fields of accounting and auditing have been identified in this study. The AI-generated content often shows biases towards a particular decision or judgment. These biases have severe social impacts. A variety of studies have raised concerns over these issues. AI brings a significant change in the updated skill sets and roles of accounting and auditing professionals.

This study helps us to understand how the accounting and auditing profession is gradually undergoing digital transformation and adopting automation. The findings of this study show that addressing ethical concerns and developing new skills for professionals are more important than technological investments.

The rest of the paper is arranged as follows: Section 2 describes the methodology to review the existing research works related to the applications of AI in the context of accounting and auditing. Section 3 performs bibliometric analysis of the considered studies. Section 4 portrays the employability and benefits of AI in the fields of accounting and auditing. Section 5 highlights the threats and challenges of introducing AI in the considered fields. The findings and potential recommendations are delineated in Section 6. Finally, the paper is concluded in Section 7.

Expert analysis has been conducted by the author and co-authors of this study, who are highly efficient in the relevant field due to their prior research experience. The analysis involved a systematic evaluation of AI implications in accounting and auditing based on predefined criteria such as practical application, theoretical consistency, and relevance to research objectives. Each criterion was assessed independently through discussions by the authors to reach a conclusion.

A powerful search strategy with appropriate query strings is important in the review study. Moreover, sorting out the most relevant studies is a very hard task. In this section, the search procedure, inclusion and exclusion criteria, selection strategy, and data acquisition process are described.

To conduct this study, we considered research works that were published between 2020 and 2024. We collected the bibliometric data of these research papers from the IEEE XPLORE and Scopus databases. After applying different inclusion and exclusion criteria to the collected works, we finally filtered 47 studies for further analysis.

Figure 2 shows the number of published studies by their publication years. From Figure 2, it can be stated that there is an upward trend in the number of publications in these years, which reflects the scholarly interest in this field. In 2020, 2021, and 2022, very few articles were published, indicating the initial recognition of AI in accounting and auditing. However, in 2023, a significant surge occurred, rising sharply from a single scholarly article to fifteen. By 2024, the number of published works further increased to seventeen, suggesting that artificial intelligence has become a mainstream research theme in the accounting and auditing field.

Figure 2. The number of published works over the years.

To visualize the key concepts of the 47 papers considered, we constructed a word cloud of the titles of these works. Figure 3 portrays the word cloud of the titles of these works. We utilized the matplotlib library of Python to plot the word cloud. The size of each word in the word cloud represents its frequency in the titles.

Figure 3. Word cloud of the titles of the considered works.

From Figure 3, it can be observed that the most frequently used words in the titles of the considered studies are terms like artificial, intelligence, accounting, auditing, reporting, financial, management, system, etc. These word terms reflect the primary topics of our research area. Moreover, these findings reveal that artificial intelligence in accounting and auditing acts as a transformative force in both theory and practice.

Furthermore, we have performed the network analysis of the index terms and keywords of the considered 47 studies. The semantic network analysis of the index terms of the considered studies helps to gain in-depth knowledge of the major research clusters and emerging themes in the field. In order to construct the semantic network graph of the index terms, we have used the VOSviewer tool [6]. Figure 4 illustrates the semantic network graph of the index terms of the considered works. The semantic network graph of Figure 4 represents the co-occurrence relationships among the keywords extracted from the selected works. The nodes of the graph represent the keywords, and the edges between the nodes represent their co-occurrences. The size of each node represents its degree in the graph. By observing the semantic network graph, it can be stated that nodes like artificial intelligence, AI, accounting, ChatGPT, financial reporting, auditing, and blockchain have higher degree values in the graph than the other keywords. These words represent the core concepts of the reviewed works.

From the semantic network graph of the index terms, the VOSviewer tool has extracted 23 clusters. Each cluster represents a particular area of research. We have considered the top six clusters with distinct thematic meanings based on their interpretability and cluster size. Moreover, we have named the clusters according to their semantic sense and the concepts they represent.

The details of Table 2 are discussed below.

Figure 4. Semantic Network Graph of the index terms.

Table 2. Illustrates the considered clusters from the semantic network of the index terms.

Cluster 1 (Cluster representing the cognitive aspect of accounting and auditing). This cluster’s core focus is accounting estimates, audit adjustments, auditor-client interaction, financial executive judgment, information technology, and internal control evolution. This cluster relates to the cognitive aspects of accounting and auditing, particularly how technology/AI helps in accounting and auditing judgments.

Cluster 2 (Cluster discussing technology-driven accounting). This cluster’s core focus is accounting 4.0, audit, cloud computing, fintech, industry 4.0, IoT, machine learning, and smart contracts. This cluster indicates how AI and technology-driven smart systems in accounting are significantly impacting reporting quality, investment-related decisions, and information systems.

Cluster 3 (Clusters representing innovation in accounting). This cluster’s core focus is accounting education, AI efficiency, assessment, AI tools, crowdsourcing, cost dynamics, decision support, and technology adoption. It emphasizes intellectual innovation in accounting, especially focusing on how AI is transforming teaching, evaluation, and skill development.

Cluster 4 (Clusters portraying organizational context). This cluster focuses on accountants, accounting systems, analysis, financial data, forecasting, institutional theory, integration, knowledge, technology, organization, and environment. This cluster examines how different organizations use technology in accounting for forecasting based on the analysis of accounting data.

Cluster 5 (Clusters signifying sociological and institutional changes). This cluster’s core focus is accounting change, banking, controllers’ digitalization, higher education, Islamic accounting, Islamic financial institutions, job readiness, management accounting, and role identity. This cluster identifies that accounting plays a major role in sociological and institutional changes through sector-specific themes.

Cluster 6 (Cluster portraying accounting profession and corresponding digital ethics). This cluster’s main focus is the accounting profession, artificial intelligence, business environment, Delphi study, digital accounting, digitization, ethics, firms’ accounting functions, and new technology. It relates to the crucial perspectives of the accounting profession on how AI and digital systems motivate ethical responsibilities and future directives.

Nowadays, AI plays an important role in creating opportunities in the fields of accounting and auditing. The prospects and benefits of AI in these domains are portrayed in Figure 5, and their detailed descriptions are discussed below.

Automation in repetitive tasks.AI automates repetitive and laborious tasks such as data entry, report writing, and reconciliation in accounting and auditing [1][7]. This fundamentally reduces the work pressure of accountants and helps them to perform a more critical and advisory role [1]. Tools like Robotic Process Automation can give more insights into the automation process that will help the company to be an expert in the concerned field [8].

Figure 5. Opportunities and benefits of AI.

Improved efficiency and reliability.AI algorithms are adept at reducing hard-to-find errors, improving efficiency in data analysis and financial reporting [8]. AI helps companies to reduce operational expenses by automating traditional accounting practices and ensuring adherence to legal requirements [7]. AI improves the quality, efficiency, and effectiveness of the audit process [9]. To minimize time and costs, large accounting firms use AI-based expert systems. These systems help to ensure the accuracy and consistency of complex work by allowing auditors to spend more time on important decisions [1]. Instead of being limited to small samples, AI performs analysis of the total population. As a result, the reliability and quality of the audit process are also improved. By applying deep learning-based approaches in the audit process, AI can also prevent low-quality judgments [2].

Enhanced data analysis and insights.AI allows the collection of big data more efficiently and provides real-time insights into the collected data. This helps in making decisions more effectively. It also enhances the reliability of data analysis [4]. AI helps to collect data from various unstructured sources and performs transaction matching and classification of different types of accounts [10]. These capabilities help an accountant perform different predictive consulting services [11]. Tools like Natural Language Processing (NLP) models can extract important information from unstructured data sources such as financial statements and annual reports [12].

Faster processing and reporting. AI helps to make rapid decisions and the circulation of information. Moreover, it enhances data processing efficiency for daily repetitive accounting tasks [5]. It performs real-time reporting and data analysis by providing instant access to financial performance-related information. It also enables the rapid detection of potential risks [7].

Detecting fraudulent acts. AI plays an important role in preventing fraud and enhancing the quality of accounting information [9]. AI continuously monitors transactions to scrutinize anomalies. It also helps to identify fraudulent acts and performs a reliable risk assessment during the audit process [13].

Support for auditors.AI helps auditors manage large datasets by identifying anomalies in transactions and analyzing risks more efficiently [9]. AI tools like machine learning can increase auditors’ analytical capabilities, which helps them recognize patterns [14]. It also allows auditors to focus on advisory roles and strategic decisions by reducing repetitive tasks [13].

Strategic decision-making and business management.AI empowers accounting professionals to make smarter choices and increase overall efficiency [5]. It helps managers in the thinking process and provides information to support strategic planning and decision-making [11]. AI tools help managers deepen financial insights. These tools also provide recommendations to maximize performance and achieve goals [7].

Benefits for Small and Medium Enterprises (SMEs).AI helps small and medium-sized enterprises (SMEs) to maintain competitiveness more efficiently [1]. It can convert the laborious bookkeeping process into efficient consulting services. It also has the ability to change the way an organisation operates. Moreover, it employs new potentialities whenever they are available [15].

Applications of AI in accounting and auditing. Nowadays, the accounting and auditing fields highly rely on the use of various AI and computer-based technologies. AI-based technologies can be used to assess and verify invoices for multiple payments. An organization has to manage different types of invoices. AI-based models can help professionals categorize these invoices into a variety of categories based on their billing methods, transaction types, etc. Machine learning and deep learning-based systems can also be used to predict the future budget of an organization using its historical data. Many organizations use AI-based models to check their employee expenses. These models identify whether the expenses are beyond the budgets and policies of those organizations. Any deviation from the policies of those companies triggers automated alerts from the AI-based models. Additionally, the revenue and growth of a company can also be measured by employing machine learning and deep learning-based techniques on the data of that company’s accounting department. AI-based models can be used to identify any irregular patterns and discrepancies in the accounting and auditing process. These tools are widely used for fraud detection. Many banks utilize AI-based technologies to identify potential credit card defaulters. Apart from AI-based technologies, at present, financial organizations are widely using blockchain-based technology in the fields of accounting and auditing. This technology ensures that when a record is validated, no intruder can make any changes to the existing data. Thus, this blockchain technology enhances the security of existing records [14].

Ethical concerns and accountability. The rapid advancements in AI have brought significant ethical concerns to the stakeholders [16].

Bias in algorithms and data. AI systems work based on big data and data patterns. They can retain biases and ethical attributes in the datasets. This raises questions about knowledge responsibility and how AI in accounting could reproduce existing biases [17]. It is important to discover and raise awareness about gender biases in Large Language Models (LLMs) to achieve ethical development and implementation of AI systems [18].

Lack of transparency and trustworthiness.There are concerns about the “black-box” nature of AI algorithms, where the reasoning behind AI-generated decisions is not always clear [4]. This lack of transparency leads to a lack of understanding regarding AI’s true motivations. It can also disrupt confidence among practitioners and regulatory bodies. Regulatory bodies are increasingly demanding transparent and responsible disclosure from AI developers [19]. People are hesitant to trust AI’s decisions because they are unaware of how advanced algorithms work and how they reach conclusions. The anticipated risk and difficulty of AI behavior also influence trustworthiness [20].

Gap in accountability and responsibility.A significant concern is the “responsibility gap.” The use of AI technology may lead to the infringement of ethical responsibility for the decisions of professionals, including accountants and taxpayers [20]. While humans have the sole responsibility to establish moral principles and norms for AI, the actual challenge is how they transfer their duties to AI [4].

Difficulties in professional judgement.Nowadays, AI systems are not able to feel human emotions. They cannot decide based on professional judgment or consider all consequences of their actions [4]. This raises questions about whether intelligent agents can do any work without professional disturbances. It also points to the need for humans to perform judgment [21].

Impact on workforce and professional roles. A long-held fear is that robots may take over various activities previously carried out by humans. AI systems are linked to unemployment, particularly for low- and middle-skilled workers who execute repetitive actions [4]. There are concerns about deskilling. In terms of deskilling, technology becomes the center of attention, and humans become submissive to it. Top management is concerned about the financial reporting risks created by automation. Additionally, some accounting executives are reluctant to adopt automation due to concerns about removing human judgment [22]. This reluctance can arise among professionals due to their misunderstanding about AI [4].

Shift in required skill sets. The growing adoption of AI requires a shift in the skill set of entry-level accountants. Accountants need to develop new skills to adapt to AI-driven environments [2]. To get the best from AI, training and continuous learning are required.

Asymmetric power relations. Automation of the system can create power imbalances between employees and companies, which creates serious threats for employees [23].

Data management issues. Auditors face challenges with data acquisition, data structure, security, and independence. Clients might be hesitant to share data due to confidentiality concerns. Furthermore, the available data may not be well organized. Auditors also need to recognize the limitations of using specific data [2].

Complexity of implementation and integration. Implementing AI in accounting and auditing is a challenging task. It involves organizational culture changes and work method adaptations [9]. Integrating humans and AI into the audit process requires a change in the mindset of future auditors [2]. The theoretical frameworks for implementing AI successfully in accounting and auditing are not detailed enough [20].

Accuracy limitations and errors. In some cases, AI can fall short in eliminating mathematical errors from the perspective of the users [10]. LLMs have raised concerns as they frequently generate inaccurate results and false statements [16].

Fraudulent and malicious usage. In the wrong hands, AI can be misused. Potential cyber criminals can spread disinformation and exhibit fraudulent behaviors by using AI [16]. Moreover, AI can lead to negative or unforeseen consequences, which are known as the runaway effect [1]. For cybercrime, vulnerabilities in financial transactions can be easy targets [8].

Security and privacy concerns. A major concern regarding these new technologies is the security and privacy of data [7]. Data leaks, data misuse, and privacy violations have led to unwillingness to adopt AI [4].

Limitations in decision-making. AI models struggle with tasks requiring financial statement creation, bookkeeping, or software use [12]. They may fail to detect anomalies and make decisions based on the situation at hand [21]. There is a tendency for some auditors to over-rely on technology, which can result in inefficient audit effort to address identified audit risks [2]. Some experts distrust AI because of its rigid nature, which makes them believe that AI could cause problems for unskilled users [24].

Regulatory and standardization challenges. The large volume of regulations and standards that accounting professionals must follow will be influenced by AI in the near future. It will pose challenges for legal provisions and liability [4]. The value of AI-powered information and big data is not yet formally recognized in financial statements. This creates a gap between book and market values. It also limits the usefulness of decision-making abilities derived from financial statements. In terms of AI, the current regulations of International Financial Reporting Standards (IFRS) are insufficient [25].

Concerns for Small and Medium Enterprises (SMEs).While AI can be effective for SMEs, there is a fear among accountants about emerging technology [1]. Many managers in SMEs may not have the educational background or abilities to understand the AI-derived information properly. Lack of proper understanding of AI leads to its exclusion from decision-making [26]. Furthermore, the costs and technological challenges can be significant barriers for SMEs to adopt AI [7].

This study has found significant impacts of AI and advanced technologies in the fields of accounting and auditing. The bibliometric analysis performed in this study has identified a variety of clusters within the index terms of the research papers. From the cluster analysis, it can be found that a large number of studies focus on the cognitive aspect of accounting and auditing. Moreover, several studies discuss the effects of technology and innovation in the field of accounting. Besides, the studies also focus on digital ethics related to the accounting profession.

Some findings, recommendations, and future research directives of this study are described below.

Implications for quality and efficiency. Artificial intelligence in the audit process has positively affected audit quality. Studies confirm that AI can have a significant and favorable impact on a company’s overall accounting systems. AI improves business forecasting, precise risk assessment, and accurate financial analysis, which ultimately leads to informed and calculated business decisions.

Financial reporting quality (QFR). This study found a significant relationship between the use of artificial intelligence applications and the quality of financial reporting. Different AI tools showed a positive relationship with financial reporting quality.

Error elimination and fraud detection. AI is found to be significant in eliminating accounting errors such as tax rate errors, cutoff period errors, errors of principle, hiding transaction errors, mathematical errors, and manipulation errors that reduce tax reporting quality in emerging markets [27].

Market perception. The use of automated technology by public firms is significantly increasing the decision-making efficiency of stakeholders. As a result, investors are more inclined towards automation than ever before.

AI adoption perspective. Accounting and auditing professionals adopt AI in order to improve the quality and efficiency of significant business decisions. Nowadays, controlling authorities prefer an auditor’s AI-supported valuation over human-supported valuation [21]. This study identifies several areas where existing research is limited, which may focus future research areas as follows.

Geographic and economic focus. The existing research has primarily been conducted on developed economies. Future research needs to increase the datasets to include additional geographic areas and emerging markets. Specifically, there is a weak concentration on the role of AI in emerging markets [10]. Among the considered studies, the majority of the works were from Middle Eastern and Islamic countries, followed by research works from Southeast and East Asian regions. Moreover, the considered studies also focused on the economies of other regions, like Europe, Africa, North America, and South America.

AI applications and interpretability. In terms of financial prediction and stock market forecasting, there is a significant gap in the interpretability and explainability of the decisions made by the AI models. The AI models can gain the trust of stakeholders by enhancing the transparency of their decision-making process. This will also lead to the widespread adoption of AI. The existing AI models can increase the trustworthiness and transparency of their decision-making process by incorporating different Explainable AI-based techniques.

Specialized accounting functions. Research shows a weak concentration regarding AI’s role in improving tax reporting quality by reducing accounting errors. A more complex AI model should be developed, which will be capable of distinguishing context, sarcasm, and subtle nuance in a large volume of text data to provide a more comprehensive understanding.

Adoption readiness of AI. Further studies are needed to examine knowledge about AI among different stakeholders and whether they are ready to adopt it or not.

This study recommends the following actionable steps for practitioners, managers, and the academic community.

Embracing AI adoption.Accounting and auditing professionals in private, corporate, and accounting firms should strategically embrace the applications of artificial intelligence, considering the benefits of economic value and the improvement of audit quality and accounting reporting quality in terms of accuracy and reliability [13].

Focus on training and skills development.Investments in educating employees are pivotal to maximizing the highest returns from technology [28]. A professional must be trained in AI tools and modern management accounting practices for identifying value-added and non-value-added operations in an organization [29].

Strategic research focus. Academia is motivated to focus research on themes such as investigating the use of AI to help businesses align their goals with environmental responsibilities and discovering more sustainable investment opportunities [19].

Methodological improvement. Future research on AI in accounting and auditing should involve larger data samples and a broader range of industries. Furthermore, research should focus on variables that clarify the AI/accounting/auditing system relationship as moderators and mediators.

Encouraging AI implementation. Auditing firms should be encouraged to apply artificial intelligence to accounting and auditing practices [13]. Corporations should continuously apply automated learning and neural network technology in the book-keeping process to improve the quality of financial reports [27].

This study reviews 47 articles on artificial intelligence in accounting and auditing. The bibliometric analysis has been conducted on the keywords of all the considered articles. The following analyses, such as trend analysis, word cloud analysis, VOSviewer analysis, and cluster analysis, were performed to gain an in-depth understanding of the literature. The considered article thoroughly scrutinizes the opportunities and threats of AI in accounting and auditing.

AI in accounting helps professionals perform repetitive tasks more smoothly. Professionals gain more time for deep data analysis and ensure efficiency. Decision-making tasks of accountants and auditors are more effective and fruitful with the help of AI. Tools of AI, such as robotic process automation, deep learning, natural language processing, machine learning, and neural networks, help professionals detect fraudulent activities more easily. AI helps accounting and auditing professionals in advisory roles to make various strategic decisions.

AI raises concerns about ethical issues such as biased data and algorithms. Professional judgment using AI lacks transparency and trustworthiness. There is a serious threat to the human workforce that AI will take over the human control environment. Our review suggests that professionals need to shift their skills from manual to AI adoption in their daily professional activities. AI in accounting and auditing should be conducted on larger datasets and include a broader range of industries, removing geographical barriers.