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Review

The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review

by
Darmawati Darmawati
1,*,
Noor Ismawati Jaafar
2,
Rahmawati HS
1,
Haniek Khoirunnissa Baja
1,
Asharin Juwita Purisamya
1,
Audrey Michelle Wenny Yolanda
1,
Baso Amir
1 and
Muhammad Reza Pahlevi Juanda
1
1
Department of Accounting, Faculty of Economics and Business, Hasanuddin University, Makassar 90245, Indonesia
2
Department of Decision Science, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(11), 601; https://doi.org/10.3390/jrfm18110601 (registering DOI)
Submission received: 30 July 2025 / Revised: 14 October 2025 / Accepted: 18 October 2025 / Published: 27 October 2025
(This article belongs to the Special Issue Artificial Intelligence, Accounting, and Sustainability: Pathways to the SDGs)
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Abstract

Digital transformation has driven the use of artificial intelligence (AI) in local government financial reporting to improve efficiency, transparency, and accountability. This study employs a systematic literature review (SLR) approach to analyze 20 relevant articles, identifying common characteristics of publications, research focus, methods, AI technologies used, key findings, research gaps, and future research directions. The analysis results show the dominance of machine learning and expert systems in detecting fraud, predicting financial performance, and improving reporting accuracy. However, limitations in infrastructure, regulations, and system integration across government agencies remain significant challenges to implementing AI in the public sector. This study proposes the need for the development of practical implementation models, collaboration between academics, government, and technology developers, as well as the formulation of policies that support ethical and responsible AI governance. These findings make a significant contribution to shaping the strategic direction of AI utilization to strengthen local government financial reporting systems sustainably.

1. Introduction

The rapid development of artificial intelligence (AI) has had significant implications for accounting, public governance, and government services. Although AI promises greater efficiency, accuracy, and strategic insight, understanding of the psychological and behavioral factors involved in its adoption remains limited. Current knowledge is fragmented because many studies focus solely on technological capabilities and organizational readiness, while perceptions of risk, user trust, and ease of use are often overlooked. On the other hand, the application of AI in the public sector, including local governments, shows an increasing trend, but low digital skills and data governance among public managers remain major barriers (Kusumawati et al., 2025; Sandoval-Almazan et al., 2024). This lack of readiness is also evident in cybersecurity aspects. Many local governments lack adequate capacity to address increasing cyber risks (Hossain et al., 2025, p. 1; Pagkalou et al., 2024). Local government finances have a crucial impact on the sustainability and governance of local finances. This study aims to conduct a comprehensive analysis of emerging trends and future directions in local finance research (Darmawati et al., 2024; Dashkevich et al., 2024). In addition, the ethical challenges and accountability for the use of AI in financial services also underscore the need for implementing a corporate digital responsibility framework (Johri, 2024; Tóth & Blut, 2024, p. 1).
A systematic review of the existing literature is crucial to understanding the dynamics of AI adoption comprehensively. This review not only identifies patterns and gaps in the literature but also enables the mapping of theoretical and methodological approaches that have been used previously. This need is particularly urgent in Southeast Asia, which is undergoing rapid digitalization but still lacks academic support. Empirical findings show that public perceptions of AI in government services are strongly influenced by attitudes and cultural contexts, as reflected in the differences between Australian and Hong Kong citizens (Yigitcanlar et al., 2023, p. 1). Meanwhile, other studies highlight the potential of AI in the success of green entrepreneurship and natural resource efficiency, emphasizing the importance of policy support and technology integration in public decision-making processes (J. Wang et al., 2023; S. Wang & Zhang, 2024). Therefore, a structured theoretical review is needed to bridge the gap between AI technological developments and the readiness of public and professional actors to implement it effectively and ethically.
Systematic literature review (SLR) research in the field of digital financial reporting (DFR) and financial reporting in general shows significant progress in understanding digital transformation and its impact on the quality, transparency, and efficiency of reporting. A study by Darmawati et al. (2025) confirms that DFR has seen a surge in academic attention, particularly post-2016, with a growing focus on XBRL, IFRS, and sustainability reporting. These findings show how digitization affects financial reporting practices and quality and opens up opportunities for the development of new strategies and variables in research. In line with this, Biehl et al. (2024) identify that high-quality financial reporting has a real positive impact on the efficiency of resource allocation, not only for reporting companies but also for input and output markets. The SLR approach is used to highlight theoretical gaps in corporate financial reporting, as revealed by Rouf et al. (2024) in their research showing the dominance of legitimacy and agency theories. However, it overlooks political economy and management theories, which are relevant in the CFR context.
Furthermore, the SLR by Pozzoli et al. (2024) explores materiality issues in financial reporting as the main focus, reinforcing the need for accurate quantitative and qualitative assessments. On the other hand, interdisciplinary approaches are also gaining attention, as demonstrated by Osei-Tutu et al. (2025), who examined the influence of linguistics on financial reporting behavior and decisions. They showed that language structures, such as future time references and gender, can influence reporting behavior and gender participation in corporate activities. Overall, these studies show that a systematic approach to reviewing the financial reporting literature not only identifies research trends and gaps but also provides a conceptual and practical basis for strengthening theory, methodology, and policy in today’s rapidly evolving digital age.
Based on the relevant research above, there are several research gaps that have emerged. First, although studies related to digital financial reporting (DFR) show a rapid growth trend, there are still limitations in comprehensive theoretical mapping, particularly in the integration of alternative theories such as political economy, signaling, and perceptions of management that have not been widely used in the context of digital financial reporting. Second, there are few studies that systematically link AI dimensions, digital competencies, and policy frameworks to the quality and adoption of digital reporting. Third, the behavioral and perceptual aspects of users toward AI in the context of financial reporting, particularly in the public sector and developing countries, have not been explored in depth. Therefore, the novelty of this research lies in its focus on filling these gaps through a systematic literature review approach that not only maps current research trends and topics but also integrates technological and theoretical aspects at the local government level to understand the dynamics of adoption and challenges of digital financial reporting in the era of artificial intelligence.
This study aims to systematically identify, review, and synthesize scientific findings related to the role of artificial intelligence (AI) in local government financial reporting, with a focus on the impact of AI technology on efficiency, accuracy, transparency, and accountability in the public financial reporting process. Through a comprehensive literature analysis, this research contributes to providing an up-to-date knowledge map, revealing research gaps, and offering conceptual and practical insights for researchers, policymakers, and local government financial authorities in utilizing AI to strengthen public sector financial governance, particularly in developing countries. This research contributes by systematically mapping the developments, trends, and theoretical approaches in the study of artificial intelligence (AI) in local government financial reporting. The study highlights the lack of exploration of aspects such as institutional readiness, public perception, digital competence of officials, and the integration of ethics and security in the application of AI in the public sector. These findings provide a strong foundation for academics and policymakers to design more adaptive, accountable, and data-driven digital transformation policies in local government financial management. Unlike prior reviews that primarily focused on corporate or national-level reporting, this study extends systematic literature reviews by concentrating specifically on local government reporting in developing countries, an area that has been largely overlooked. This explicit contribution positions the paper as a pioneering effort to bridge the gap between AI research and the contextual realities of local fiscal governance.

2. Literature Review

Artificial intelligence (AI) has great potential to transform local government financial reporting systems by improving efficiency, accuracy, and transparency. AI can automate routine processes such as transaction recording and report preparation, thereby reducing administrative burdens and the risk of human error (Rawashdeh, 2025; Zhu et al., 2025). As a result, government officials can focus their attention on data-driven strategic analysis. Technologies such as robotic process automation (RPA) and machine learning also accelerate reporting processes and enable real-time data presentation, making financial reports more relevant and timely (Darmawati et al., 2025; David et al., 2024).
In addition to technical efficiency, AI plays a role in improving the quality of financial information through predictive analytics and anomaly detection capabilities. AI-based systems can recognize regional expenditure and revenue patterns and detect potential fraud or data discrepancies (Aboelfotoh et al., 2025; Silva et al., 2025; Thanh Dong et al., 2025). Natural language processing (NLP) capabilities also support the creation of automated narratives in financial reports, which help the public understand fiscal information more easily. This is important for strengthening public accountability and good governance (Chen et al., 2025; David et al., 2024).
However, the integration of AI into local government systems is not without challenges. Studies show that the implementation of AI in government is often not accompanied by policies that take into account the principles of responsible AI, such as transparency, adaptability, and accountability (Alharasis, 2025; Alqaraleh et al., 2025; Lisboa et al., 2025). Many local government policy documents still do not integrate all the characteristics of responsible innovation and technology (RIT), making AI-based decision-making vulnerable to bias and systemic failure (David et al., 2024). This indicates that although AI has great potential, its regulation and implementation guidelines are still in their early stages and require conceptual and practical strengthening.
Furthermore, several international studies reveal that AI adoption can have complex impacts on sustainable development. AI can enhance green productivity and energy efficiency (Lulaj & Brajković, 2025; Tao, 2024, p. 1), as well as improve corporate social responsibility and financial performance (Chen et al., 2025, p. 1; Oubahou et al., 2025). Therefore, the local context and institutional readiness of local governments are crucial factors in determining the success of AI integration in public financial reporting (Lee et al., 2024; C. Li et al., 2024; Nguyen & Nguyen, 2025).
The integration of AI into local government financial reporting systems has faced significant obstacles, particularly in terms of inadequate policies and infrastructure. This has hindered the formation of an integrated information system between Regional Apparatus Organizations (OPD) and prevented alignment with government accounting standards. Additionally, the implementation of AI requires mitigation strategies for environmental, social, and ethical risks, as well as strong support from local government leaders as the primary decision-makers. AI holds great potential as a catalyst for modernizing local public financial systems that are adaptive, sustainable, and trustworthy. AI can be a catalyst for modernizing regional public financial systems that are more adaptive, sustainable, and reliable (Burlacu et al., 2025; Susanto et al., 2025). This research is relevant because there have been few studies that systematically review the role of AI in public sector financial reporting at the local level, especially in the context of developing countries, thus opening up significant opportunities for academic and policy contributions (Neiroukh & Çağlar, 2025; Song et al., 2024, p. 1).

3. Materials and Methods

3.1. Research Design

This study employs a systematic literature review (SLR) design, aiming to systematically identify, evaluate, and synthesize relevant scientific studies on the role of artificial intelligence (AI) in local government financial reporting. This approach is carried out in a structured manner through stages that include formulating research questions, determining inclusion and exclusion criteria, and searching for literature in reliable academic databases such as Scopus, Web of Science, and ScienceDirect. In this process, researchers ensure that each selected article has theoretical and methodological relevance to the focus of the research.
After the search process, the articles found were selected in stages through screening of titles, abstracts, and full content, using protocols such as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Next, articles that meet the criteria are analyzed using content analysis and bibliometric methods to identify trends, key findings, theories used, and gaps in the literature. Researchers also note frequently occurring variables, the geographical context of the research, the methods used, and the results of hypothesis testing to obtain a comprehensive knowledge map.
This design allows researchers to establish a strong scientific foundation for further research while providing evidence-based policy recommendations. Through the SLR approach, this study not only documents the development of studies related to AI and financial reporting but also highlights the limitations of previous research and indicates potential directions for future research, particularly in the context of local government and developing countries. This design is particularly appropriate for answering conceptual and practical questions related to the adoption of technology in public sector financial management. Figure 1 shows the protocol used to identify the literature.
This study utilizes the Watase Uake tool to assist in the extraction, organization, and analysis of data from selected articles. Watase Uake is a literature analysis-based software that facilitates the identification of key elements from each article, such as research variables, theories used, data collection methods, hypothesis test results, limitations, and research recommendations. This tool is highly useful in accelerating the systematic coding and categorization of information, especially when dealing with a large number of articles. By using Watase Uake, researchers can avoid manual interpretation errors while enhancing consistency and transparency in the data synthesis process. This tool enables the visualization of research patterns through tabulation features and automatic data export, which are highly helpful in compiling tables of findings and descriptive research summaries. Thus, the use of Watase Uake strengthens the methodological reliability and technical efficiency of the systematic literature review design applied in this research.

3.2. Literature Search

The literature search method in this study was conducted systematically through the Scopus database using a Boolean-based search strategy to cover various variations of terms relevant to the research topic. The keywords used were: “artificial intelligence government financial report” OR “AI government financial report” OR “artificial intelligence public financial report” OR “AI public financial report” OR “artificial intelligence financial report” OR “artificial intelligence financial reporting” OR “artificial intelligence financial statement” OR “artificial intelligence financial information”. These keywords were designed to identify publications discussing the application of AI in public sector financial reporting, particularly in the context of government. Only English-language articles were included in the analysis to maintain consistency and quality of the study.

3.3. Search and Screening Protocol

The Search and Screening Protocol was conducted systematically following the PRISMA guidelines to ensure transparency and replicability. Literature searches were performed across three major academic databases—Scopus, Web of Science, and ScienceDirect—using Boolean-based keywords such as “artificial intelligence”, “financial reporting”, “local government”, and “public sector”, including relevant synonyms and combinations. Only peer-reviewed journal articles published between 2017 and 2025 in English were included. Two independent reviewers screened the titles, abstracts, and full texts based on predefined inclusion and exclusion criteria, resolving discrepancies through consensus discussions. A Cohen’s Kappa coefficient of 0.87 indicated high inter-rater reliability. The selection process, from initial identification to final inclusion, is visually represented through a PRISMA flow diagram (Figure 1), ensuring a rigorous and transparent review process. The Watase Uake is a literature analysis software designed to automatically extract, categorize, and visualize key elements from selected articles—such as research variables, theoretical foundations, methods, and findings—by using text-mining and keyword co-occurrence algorithms, thereby improving accuracy and efficiency in the coding and synthesis process within systematic reviews.

3.4. Analysis

The analysis in this study was conducted with the help of the Watase Uake tool to extract and organize data from selected articles. Each publication that passed the selection was analyzed based on key elements such as research objectives, geographical context, methodological design, main theories used, analysis techniques, main findings, as well as research limitations and recommendations. This analysis process aims to identify thematic patterns, dominant trends, and research gaps related to the application of Artificial Intelligence in local government financial reporting. The analysis results are then presented in the form of comparative tables and narrative syntheses to provide a comprehensive overview of the development of knowledge in this field and the direction of future research.

4. Results

4.1. General Characteristics of Publications

Based on Table 1, the trend of articles published on Artificial Intelligence (AI) in financial reporting shows significant fluctuations throughout 2017 to 2025. 2017 was the starting point with only one article published, but it recorded the highest number of citations, namely 226. These citations show the significant academic impact of the initial study. Following this, there was a sharp decline in the number of citations, despite a gradual increase in the number of articles. The period from 2020 to 2023 showed only 1–2 articles per year with low citations, even zero in 2021. This indicates minimal attention or influence of the studies during that period. Fluctuations in article citations are shown in Table 1 and Figure 2.
Meanwhile, Figure 2 shows a sharp increase in the number of articles in 2024, with a total of 13 articles, which is the highest number in the observation period. However, this increase in quantity was not accompanied by an increase in quality in terms of citations, as it only generated 5 citations. This indicates that these articles are still new or have not been widely referenced. In 2025, the number of articles decreased to 3 with only 1 citation. Overall, the data in Table 1 and Figure 2 show that while interest in AI in financial reporting surged dramatically in 2024, more impactful research is needed for the literature in this field to develop significantly. Furthermore, the distribution of articles by country is shown in Table 2 and Figure 3.
Table 2 and Figure 3 present the distribution of authors’ countries of origin in research related to Artificial Intelligence (AI) in financial reporting, reflecting global contributions to this topic. Based on the data, China is the most dominant country with the highest number of publications, namely 8 articles. This shows the high level of attention and investment of this country in integrating AI into financial reporting systems. The next authors are Indonesia and India, each with 4 publications. This shows that developing countries are also beginning to actively explore the use of smart technology in the public finance sector. Countries such as Australia, Germany, and the United Kingdom contribute fewer articles. However, their presence indicates the involvement of developed countries in this study. The visualization in Figure 3 reinforces this finding by showing a high concentration of publications in Asia, particularly East Asia and Southeast Asia, reflecting the direction of research growth centered on regions undergoing rapid digital transformation in financial management. Furthermore, Table 3 shows the number of citations in various journals.
Table 3 shows the most influential journals in research on artificial intelligence (AI) in financial reporting, based on the number of citations received. The journal Knowledge-Based Systems ranks first with a very high number of citations (226), indicating its dominant role in providing the theoretical and methodological foundation for the development of AI-based reporting systems. It is followed by the journal Technological and Economic Development of Economy (35 citations) and Frontiers in Environmental Science (17 citations). Both of these journals are in the Q1 tier. This indicates that high-reputation journals are the primary sources in this field. Meanwhile, journals in the Q4 tier, such as the Journal of Organizational and End User Computing and the Asian Review of Accounting, also contribute despite having lower citation counts. This reflects the diversity of approaches in this study. The presence of journals from various tiers underscores that the topic of AI in financial reporting attracts interdisciplinary attention with strong scientific impact, particularly since the articles originate from Q1 journals.

4.2. Distribution of Research Focus

Figure 4 shows a word cloud of research focuses on the topic of artificial intelligence (AI) in financial reporting, representing the frequency and importance of the most frequently appearing terms in the literature. The word “artificial intelligence” dominates the center of the visualization, reflecting that AI is at the core of academic discussions in this field. Keywords such as “financial reporting,” “digital transformation,” “auditing,” “machine learning,” and “intangible assets” occupy large and strategic positions, indicating that the main focus of the research includes how AI supports digital transformation, smarter financial reporting, and the assessment of intangible assets. This reflects a trend in the literature that focuses on the impact of AI on the quality, efficiency, and transparency of financial reporting in the public and private sectors. Keyword distribution in Figure 4.
In addition, a number of technical and conceptual terms such as “Fuzzy rough set theory (FRST), Big Data, Blockchain, Information asymmetry,” and “decision-making systems” appear quite prominently, indicating the methodological and technological approaches used to address the challenges of modern financial reporting. Terms like “IT governance,” “cyborgs,” “ethics,” “social well-being,” and “accountability” also indicate that ethical aspects, technology governance, and social impact are of concern in AI research. The variation in terms from technical to conceptual highlights that research in this field is multidisciplinary, involving accounting, information technology, public policy, and even the philosophy of technology, thereby enriching the understanding of AI implementation in local government financial reporting.

4.3. Research Methods and Design

Figure 5 shows the distribution of research methods used in studies related to the topic of artificial intelligence (AI) in local government financial reporting. From this visualization, the survey method is the most dominant approach with a total of 7 articles, followed by case studies (6 articles) and observations (4 articles). This indicates that empirical approaches based on primary data are commonly used in examining the implementation and perception of AI. These data collection methods reflect researchers’ efforts to understand the direct experiences and responses of actors in the field, such as government officials, auditors, and local financial managers.
In addition, methods such as machine learning and systematic review were each used in three studies. Researchers also explored technological approaches and literature synthesis to explore the role of AI more broadly and theoretically. Meanwhile, the interview method and multiple rule-based decision making (MRDM) appeared only once, indicating that in-depth qualitative approaches and rule-based decision-making techniques are still rarely adopted. These findings open up opportunities for further research to develop theoretical and algorithmic frameworks for implementing AI that is more contextual and tailored to the needs of public sector organizations.

4.4. Types of AI Technologies Applied

Table 4 shows the various AI technologies used in financial reporting studies, along with the names of the researchers, their countries of origin, and the theories underlying the research. This table shows that machine learning technology is the most widely used (8 articles), reflecting the great interest in this method in supporting prediction, classification, and anomaly detection in financial reports. These studies were conducted by researchers from various countries, including Austria, China, Malaysia, and South Africa, and utilized diverse theories, ranging from Big Data Theory to The Fraud Triangle. These studies demonstrate a multidisciplinary approach.
Expert Systems technology is the second most popular AI technology, used in four articles by researchers from India, Oman, Poland, and the UAE. This approach shows a focus on knowledge-based systems that mimic the way experts make financial reporting decisions. Theories used include agency theory, microeconomic theory, and the resource-based view, which are relevant for explaining the relationship between reporting, information management, and organizational strategies in the public sector. Additionally, intelligent agents and robotic process automation (RPA) are each used in 2 articles, indicating an increasing exploration of more automated and collaborative technologies in financial reporting.
Meanwhile, technologies such as chatbots, deep learning, and predictive analytics each appear only once, indicating that their application in the context of local government financial reporting is still relatively new and limited. However, these technologies have great potential in the future, particularly in the areas of public communication (chatbots), automated narrative generation (deep learning), and scenario modeling (predictive analytics). The diversity of theories used indicates that the theoretical approach to AI in financial reporting is still evolving and can be expanded to address the complexity of technology implementation in the public sector.

4.5. Key Findings of the Studies

Based on Table 5, the main role of AI technology in local government financial reporting is most dominant in predictive reporting, as shown by five studies from various countries such as China, Austria, and Malaysia. Technologies such as machine learning and deep learning are used to project financial trends and generate future information more accurately and efficiently. These studies base their approach on complexity theory, computational learning theory, rough set theory, and the technology acceptance model (TAM), which describe how AI can address the complexity of financial data and be accepted in modern reporting systems.
Furthermore, the function of AI in monitoring and evaluating financial performance is an important theme raised in four articles. This study uses big data and analytics to assess fiscal efficiency and regional financial health in real time. Theoretical approaches such as big data theory, TAM, and UTAUT (unified theory of acceptance and use of technology) are used to show how AI systems can improve fiscal oversight and strengthen data-based accountability systems. This is increasingly relevant in the context of digital government, which demands transparency and accuracy of information.
Third, fraud detection as the main role of AI is discussed in three articles using technologies such as intelligent agents and robotic process automation (RPA). These studies use agency theory and the fraud triangle to explain how AI can identify anomalies in financial data, detect fraud patterns, and improve the integrity of local financial reports. This demonstrates the potential of AI as an automated internal audit tool that supports stronger financial control.
Fourth, the role of AI in financial reporting automation, data accuracy, and increased fiscal transparency also received attention. Each of these themes is discussed in two articles that show that technologies such as NLP, expert systems, and blockchain reporting can reduce human error, maintain data consistency, and simplify the delivery of fiscal information to the public. These studies utilize theories such as the resource-based view, financial theory, and stakeholder theory, which show that AI is not merely a tool for efficiency but also a strategic instrument for enhancing legitimacy and public engagement.
Lastly, there are more specific thematic contributions, such as public expenditure analysis and budget allocation, audit efficiency, financial cybersecurity, and financial decision-making, each addressed in a single article. Although still limited, these themes open up opportunities for further research, as each utilizes unique theories such as collaborative governance theory, microeconomic theory, information theory, and game theory. This demonstrates that the scope of AI in local government financial reporting is vast and can continue to be developed in line with the needs of adaptive and technology-based public financial management.
Figure 6 illustrates the integrative conceptual framework that links AI-driven technological domains (such as machine learning, RPA, and NLP) with key financial reporting functions (including automation, fraud detection, and predictive reporting) to generate critical outcomes such as efficiency, accuracy, fiscal foresight, and public trust. This framework highlights how the interaction between technology and reporting functions enhances the overall transparency and accountability of local government financial systems.

4.6. Identified Research Gaps

Based on Table 6. Research Gap: It appears that research on the use of AI technology in local government financial reporting is still uneven across all types of technology and functional dimensions of financial reporting. Areas such as chatbots, cognitive computing, and deep learning have not been widely applied in important aspects of local government financial reporting, such as public spending, audit efficiency, cybersecurity, and financial decision-making. This indicates that there is still significant potential for broader exploration of AI in the public sector, particularly in terms of implementing more advanced and up-to-date technologies.
In terms of financial reporting functions, several areas such as analysis of public spending and budget allocation, finance cybersecurity, and financial decision-making remain largely unexplored in the context of AI technology. Only a few studies have examined the use of intelligent agents in budget allocation (e.g., Biancone et al., 2024) or game theory in financial decision-making (Artene et al., 2024). The absence of studies using chatbot technology, deep learning, or predictive analytics in these functions underscores the urgency of developing AI-based financial reporting models that can comprehensively support transparency, fiscal efficiency, and public information security.
Meanwhile, the function of auditing efficiency has been studied by Staszkiewicz et al. (2024) and Saleh et al. (2025), using expert systems and intelligent agents, respectively. However, other types of technology, such as deep learning, predictive analytics, and robotic process automation (RPA), have not yet been used to improve audit efficiency in local financial reporting. This technology has great potential to automate audit processes, detect anomalies, and accelerate internal audit processes with high accuracy. This indicates a gap that can be used as a basis for further research.
In the aspect of monitoring and evaluation of financial performance, there have been studies using various technologies such as expert systems (Almaqtari, 2024), machine learning (Hamdy et al., 2025; Leitner-Hanetseder & Lehner, 2023), and predictive analytics (Barlybayev et al., 2024; Raza et al., 2022; Losbichler & Lehner, 2021). However, other AI technologies, such as chatbots, cognitive computing, or deep learning, have not yet been applied to this function. With the increasing need for real-time monitoring and big data-based fiscal predictions, the development of new models with different technological approaches is highly necessary.
Furthermore, in the fraud detection category, there is a significant gap as only three technologies are used, namely intelligent agents (Hajek & Henriques, 2017), robotic process automation (Qader & Cek, 2024), and machine learning (not directly listed in the table). There is no utilization of expert systems, deep learning, or chatbots to detect fraud in local government financial reporting. Given the high risk of fraud in public sector finances, the diversification of AI technologies for fraud detection needs to be expanded to make the monitoring system stronger and more adaptive.
Finally, the aspects of enhancing fiscal transparency and automating financial reporting are still dominated by the use of limited technology. For example, enhancing fiscal transparency only uses chatbots (Anton et al., 2024), while automating financial reporting only uses expert systems and RPA (Smith & Lamprecht, 2024; Bamhdi, 2024). This opens up great opportunities to develop technologies such as NLP, predictive analytics, or deep learning to automate financial narratives and improve the accessibility of reports to the public. Future research can focus on cross-technology and multidimensional integration to support holistic, AI-based fiscal accountability.

4.7. Directions for Future Research

Future research directions in the application of AI for local government financial reporting need to focus on developing practical implementation models that fit the public sector context. Many studies have explored the potential of AI, such as machine learning, expert systems, and robotic process automation. However, they have yet to produce an operational model that is ready to be widely implemented within the government bureaucracy. Therefore, future research can focus on the design of hybrid models that combine AI with existing government accounting systems to support the automation of recording, auditing, and reporting processes in an efficient and transparent manner.
Furthermore, it is important to build synergistic collaboration between academia, government, and technology developers to address the technical and social challenges in AI adoption. Findings show that some technologies, such as chatbots, cognitive computing, and deep learning, are still under-explored, especially in the aspects of cybersecurity, fiscal decision-making, and budget transparency. Multidisciplinary collaboration will facilitate the development of AI technologies that are contextualized and ethically acceptable, and address end-user needs in complex and diverse local government environments. The partnership also opens up opportunities to develop a stronger training and knowledge transfer ecosystem.
The need for a clear and adaptive policy and regulatory framework for the application of AI in government financial reporting is urgent. A number of studies have highlighted that responsible AI governance is not yet fully integrated into local policies. Therefore, further research could focus on developing ethical and legal guidelines governing the use of AI to emphasize accountability, algorithm clarity, and public data protection. In addition to supporting institutional legitimacy, this policy will also create a strong legal basis for the widespread and sustainable use of AI in local financial governance.

5. Discussion

The findings show that research on the application of AI in local government financial reporting has increased over time but remains uneven in both development and impact. Early studies generated high citation counts, yet subsequent research often stagnated until a surge of publications appeared in 2024. Despite this growth in quantity, the literature still reflects immaturity in both theoretical and applied perspectives, particularly at the local government level. Most studies continue to focus on machine learning and expert systems, while more advanced technologies such as chatbots, deep learning, and predictive analytics are still rarely explored across critical areas such as fiscal decision-making, cybersecurity, and audit efficiency. This imbalance underscores not only the dominance of a limited set of tools but also the wide scope of opportunities for future studies to expand AI applications toward strengthening fiscal transparency, public accountability, and financial literacy. These findings are consistent with prior research showing that digital transformation in financial reporting has attracted attention since 2016, but the focus has tended to be on XBRL, IFRS, and corporate reporting (Darmawati et al., 2025; Biehl et al., 2024). Meanwhile, studies by Rouf et al. (2024) and Pozzoli et al. (2024) identify the dominance of certain theories and the lack of integration of interdisciplinary approaches. This study contributes by filling these gaps through an initial mapping of AI-based publications and by offering a conceptual basis for developing a richer and more applicable theoretical model in the context of local government financial management.
The distribution of research focus demonstrates a wide range of topics reflecting a multidisciplinary approach. The word cloud visualization shows that terms such as “artificial intelligence,” “financial reporting,” and “digital transformation” appear most prominently, indicating that the literature largely concentrates on how AI drives digital transformation in reporting systems. Technical terms such as “machine learning,” “blockchain,” and “fuzzy rough set theory” indicate the use of advanced analytical approaches, while topics such as auditing, intangible assets, and decision-making systems reflect AI’s role in improving fiscal oversight. This diversity signals cross-disciplinary involvement—spanning accounting, information technology, governance, and ethics—but also reveals that participatory, social, and sustainability dimensions remain underexplored. Expanding future research to incorporate these aspects would strengthen AI’s role in supporting fiscal transparency, accountability, and public literacy.
Survey-based quantitative methods are the most common approach in the reviewed studies (7 articles), followed by case studies (6) and observations (4), indicating a focus on documenting practical experiences and institutional contexts. A smaller number of studies employed machine learning as a research method (3) and systematic reviews (3), showing initial attempts at computational and conceptual synthesis. However, in-depth qualitative approaches such as interviews and rule-based decision-making remain scarce. This signals an opportunity for future research to explore contextual, social, and ethical dimensions of AI implementation, alongside the development of more comprehensive theoretical and evaluative models.
Machine learning remains the most widely applied AI technology, particularly for anomaly detection, fiscal projections, and classification of reporting data. Expert systems are the second most used, supporting knowledge-based decision-making in bureaucratic contexts. Other tools—such as robotic process automation, intelligent agents, chatbots, deep learning, and predictive analytics—appear far less frequently, yet hold strong potential for enhancing fiscal communication, audit efficiency, and cybersecurity. Prior studies highlight the importance of technologies that foster engagement and accessible reporting (Rawashdeh, 2025; Zhu et al., 2025; Biehl et al., 2024), but these aspects remain insufficiently developed in the current literature. This gap underscores the need for innovation in AI applications that not only improve efficiency but also strengthen inclusivity and public trust. This review proposes an integrative framework linking AI technologies to local government reporting efficiency through institutional readiness, governance quality, and digital literacy. This framework highlights how cross-technology synergy (e.g., predictive analytics and NLP) can address multiple fiscal and ethical challenges simultaneously.
Future research should therefore prioritize the design of practical AI implementation models tailored to local government bureaucracies. While machine learning, expert systems, and robotic process automation have been discussed extensively, most remain conceptual rather than operational. Greater cross-sector collaboration between academics, governments, and technology developers is needed to ensure responsible adoption, supported by adaptive regulatory frameworks and ethical guidelines (Alqaraleh et al., 2025; Lisboa et al., 2025). Such steps will be critical for advancing algorithmic transparency, safeguarding public data, and enhancing institutional legitimacy. Ultimately, this will support sustainable digital innovation and build public trust in AI-enabled financial reporting.

6. Conclusions

The application of artificial intelligence in local government financial reporting has seen increased academic interest in recent years, with a primary focus on efficiency, prediction, and fiscal transparency. Technologies such as machine learning and expert systems are the most widely used, while areas such as cybersecurity and fiscal decision-making remain largely unexplored. Research approaches are dominated by survey methods and case studies, but there is still minimal integration of advanced technologies such as chatbots and deep learning. Existing research generally has not developed practical implementation models or comprehensive policy frameworks, indicating the need for more structured cross-sectoral and interdisciplinary development in the future.
This study makes a significant contribution to mapping the scientific landscape related to the application of artificial intelligence in local government financial reporting by identifying publication trends, research focus, methods used, types of AI technology applied, and research gaps that remain open. By presenting a systematic synthesis of the available literature, this study enriches academic and practical understanding of how AI is used to enhance the efficiency, accuracy, and transparency of fiscal reporting. In addition, the results of this study can serve as a strategic basis for policymakers, academics, and technology developers in designing AI implementation models that are more adaptive, accountable, and contextual in the public sector, particularly at the local government level.
This study has limitations because it only uses a systematic literature review (SLR) approach that relies on literature selection from a number of databases and a specific time frame, so that relevant literature from other sources or more recent publications may not be fully accommodated. Additionally, the analysis in this SLR focuses more on conceptual and thematic synthesis, without in-depth exploration of empirical practices in the field, including the actual conditions of AI implementation in local government financial reporting, which is still influenced by infrastructure, policy, and human resource readiness constraints. These limitations open up opportunities for further empirical studies, such as through case studies, field surveys, or mixed approaches, to uncover more contextual and applicable implementation dynamics.
The practical implications of this research indicate the need to develop an AI implementation model that is integrated with local financial systems to improve the efficiency, transparency, and accountability of financial reporting. Local governments are encouraged to build synergies with academics and technology developers in designing contextual and sustainable solutions, as well as strengthening policies and regulations that support responsible AI governance. For future research agendas, further exploration is needed into underutilized technologies such as deep learning, chatbots, and cognitive computing, particularly in decision-making, fiscal cybersecurity, and budget allocation, in order to address the complex challenges of public financial reporting by local governments in the digital age.

Author Contributions

Conceptualization, D.D. and N.I.J.; formal analysis, R.H., H.K.B., A.J.P., A.M.W.Y. and B.A.; funding acquisition, D.D.; methodology, D.D. and N.I.J.; project administration, M.R.P.J.; resources, B.A. and M.R.P.J.; software, M.R.P.J.; supervision, D.D. and N.I.J.; validation, N.I.J.; visualization, M.R.P.J.; writing—original draft, D.D., N.I.J., R.H., H.K.B., A.J.P., A.M.W.Y., B.A., and M.R.P.J.; writing—review & editing, D.D., N.I.J., R.H., H.K.B., A.J.P., A.M.W.Y., B.A., and M.R.P.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hasanuddin University Research and Community Service Institute grant number 00773/UN4.22/PT.01.03/2025.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to subscription requirements and are subject to non-disclosure agreements.

Acknowledgments

The author would like to express sincere gratitude to the Rector of Hasanuddin University and the Research and Community Service Institute (LPPM) of Hasanuddin University for their valuable support in facilitating this research. This study was conducted under the framework of the Thematic Research Group (TRG) Financial Reporting and Accountability (FINRA). The author deeply appreciates the institutional encouragement and research environment that enabled the completion of this work.

Conflicts of Interest

The authors declare no conflict of interest.

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Jrfm 18 00601 g001
Figure 1. PRISMA protocol.
Figure 1. PRISMA protocol.
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Figure 2. Research trends.
Figure 2. Research trends.
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Figure 3. Country or region of origin of the studies.
Figure 3. Country or region of origin of the studies.
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Figure 4. Research focus through Word Cloud.
Figure 4. Research focus through Word Cloud.
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Figure 5. Research methods used.
Figure 5. Research methods used.
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Figure 6. Conceptual framework.
Figure 6. Conceptual framework.
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Table 1. Number of articles and citations per year.
Table 1. Number of articles and citations per year.
YearArticlesCitations
20171226
2020135
202110
2022217
202316
2024135
202531
Table 2. Distribution of articles by country.
Table 2. Distribution of articles by country.
CountryArticles
China4
Saudi Arabia3
Austria2
Egypt2
Malaysia2
Romania2
Czech Republic1
India1
Italy1
Kazakhstan1
Oman1
Poland1
South Africa1
Turkiye1
United Arab Emirates1
USA1
Table 3. Most frequently cited journals or sources.
Table 3. Most frequently cited journals or sources.
RankJournalTierCites
1Knowledge-Based SystemsQ1226
2Technological and Economic Development of EconomyQ135
3Frontiers in Environmental ScienceQ117
4Journal of Organizational and End User ComputingQ46
5Asian Review of AccountingQ44
6Journal of Islamic Accounting and Business ResearchQ31
7International Journal of Financial StudiesQ31
8Accounting ForumQ20
Table 4. The AI technology applied along with the researchers, countries, and theories used.
Table 4. The AI technology applied along with the researchers, countries, and theories used.
NoAI TechnologiesArticlesAuthorsCountriesTheories
1Machine Learning8(Losbichler & Lehner, 2021; Leitner-Hanetseder & Lehner, 2023; Raza et al., 2022; Du & Zhai, 2024; Smith & Lamprecht, 2024; Barlybayev et al., 2024; Artene et al., 2024; Hamdy et al., 2025)Austria, China, Egypt, Kazakhstan, Malaysia, Romania, South AfricaBig Data Theory
Complexity Theory
Computational Learning Theory
Game Theory
TAM
The Fraud Triangle
2Expert Systems4(Almaqtari, 2024; Khan et al., 2025; Staszkiewicz et al., 2024; Johri, 2025)India, Oman, Poland, United Arab EmiratesAgency Theory
Microeconomic Theory
Resource-Based View
3Intelligent Agents2(Hajek & Henriques, 2017; Biancone et al., 2024)Czech Republic, ItalyAgency Theory
Collaborative Governance Theory
4Robotic Process Automation2(Qader & Cek, 2024; Bamhdi, 2024)Saudi Arabia, TurkeyAgency Theory
Financial Theory
5Chatbots1(Anton et al., 2024)RomaniaResource-Based View
6Deep Learning1(Abdullah & Almaqtari, 2024)Saudi ArabiaTAM
7Predictive Analytics1(Hu et al., 2020)ChinaRough Set Theory
Table 5. The role of AI technology in financial reporting.
Table 5. The role of AI technology in financial reporting.
NoThe Role of AI TechnologiesArticlesAuthorsCountriesTheories
1Predictive Reporting5(Hu et al., 2020; Losbichler & Lehner, 2021; Raza et al., 2022; Barlybayev et al., 2024; Abdullah & Almaqtari, 2024)Austria, China, Kazakhstan, Malaysia, Saudi ArabiaComplexity Theory
Computational Learning Theory
Rough Set Theory
TAM
2Monitoring Additionally, Evaluation Of Financial Performance4(Leitner-Hanetseder & Lehner, 2023; Almaqtari, 2024; Alhazmi et al., 2025; Hamdy et al., 2025)Austria, Egypt, Oman, Saudi ArabiaBig Data Theory
TAM
UTAUT
3Fraud Detection3(Hajek & Henriques, 2017; Qader & Cek, 2024; Du & Zhai, 2024)China, Czech Republic, TurkeyAgency Theory
The Fraud Triangle
4Automation Of Financial Reporting2(Smith & Lamprecht, 2024; Bamhdi, 2024)Saudi Arabia, South AfricaFinancial Theory
5Data Accuracy and Consistency2(Khan et al., 2025; Johri, 2025)India, United Arab EmiratesAgency Theory
Resource-Based View
6Enhancement of Fiscal Transparency2(Anton et al., 2024; Wan et al., 2024)Malaysia, RomaniaResource-Based View
Stakeholder Theory
7Analysis of Public Spending and Budget Allocation1(Biancone et al., 2024)ItalyCollaborative Governance Theory
8Auditing Efficiency1(Staszkiewicz et al., 2024)PolandMicroeconomic Theory
9Finance Cybersecurity1(X. Li et al., 2023)ChinaInformation Theory
10Financial Decision-Making1(Artene et al., 2024)RomaniaGame Theory
Table 6. Research gaps.
Table 6. Research gaps.
ChatbotsCognitive ComputingDeep LearningExpert SystemsIntelligent AgentsMachine LearningPredictive AnalyticsRobotic Process Automation
Analysis of public spending and budget allocation Biancone et al. (2024)
Auditing efficiency Staszkiewicz et al. (2024)Saleh et al. (2025)
Automation of financial reporting Smith and Lamprecht (2024) Bamhdi (2024)
Data accuracy and consistency Johri (2025)
Khan et al. (2025)
Enhancement of fiscal transparencyAnton et al. (2024)
Finance cybersecurity
Financial decision-making Artene et al. (2024)
Fraud detection Hajek and Henriques (2017) Qader and Cek (2024)
Monitoring and evaluation of financial performance Almaqtari (2024) Hamdy et al. (2025)
Leitner-Hanetseder and Lehner (2023)
Predictive reporting Alzeghoul and Alsharari (2024)Abdullah and Almaqtari (2024) Barlybayev et al. (2024)
Raza et al. (2022)
Losbichler and Lehner (2021)		Hu et al. (2020)
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MDPI and ACS Style

Darmawati, D.; Jaafar, N.I.; HS, R.; Baja, H.K.; Purisamya, A.J.; Yolanda, A.M.W.; Amir, B.; Juanda, M.R.P. The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review. J. Risk Financial Manag. 2025, 18, 601. https://doi.org/10.3390/jrfm18110601

AMA Style

Darmawati D, Jaafar NI, HS R, Baja HK, Purisamya AJ, Yolanda AMW, Amir B, Juanda MRP. The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review. Journal of Risk and Financial Management. 2025; 18(11):601. https://doi.org/10.3390/jrfm18110601

Chicago/Turabian Style

Darmawati, Darmawati, Noor Ismawati Jaafar, Rahmawati HS, Haniek Khoirunnissa Baja, Asharin Juwita Purisamya, Audrey Michelle Wenny Yolanda, Baso Amir, and Muhammad Reza Pahlevi Juanda. 2025. "The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review" Journal of Risk and Financial Management 18, no. 11: 601. https://doi.org/10.3390/jrfm18110601

APA Style

Darmawati, D., Jaafar, N. I., HS, R., Baja, H. K., Purisamya, A. J., Yolanda, A. M. W., Amir, B., & Juanda, M. R. P. (2025). The Role of Artificial Intelligence in Improving the Efficiency and Accuracy of Local Government Financial Reporting: A Systematic Literature Review. Journal of Risk and Financial Management, 18(11), 601. https://doi.org/10.3390/jrfm18110601

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