税务领域人工智能相关的税务数字化产品、实施项目、税务政策法规及税务共享运营

In recent years, there have been notable advances in robotic process automation (RPA), especially in cases of integration with commercial transactions in electronic markets. Tax administrations have gathered this experience at the forefront in assuming the new challenges of technologies. However, there are dark regulatory areas in the face of the large amount of data that a tax administration can access through an RPA, whose lack of regulation can violate human rights. This research, precisely, accounts for the problems that can arise from the use of RPA by tax administrations, especially in cases where this type of tool is integrated with artificial intelligence. In this line, the results of this work show that the main difficulties that could arise are related to the transparency of the tax administration acts and possible discriminatory acts in the application of RPA.

The future of tax technology in the United States is undergoing a paradigm shift driven by Artificial Intelligence (AI) and digital transformation. As tax systems become more complex, AI-powered solutions offer unprecedented opportunities to enhance tax compliance, enforcement, and policy-making. This review explores a conceptual framework for AI-driven tax transformation, examining key technologies such as machine learning, natural language processing (NLP), robotic process automation (RPA), and blockchain. These innovations are reshaping tax administration by streamlining compliance processes, improving fraud detection, and optimizing tax policy modeling. Machine learning and predictive analytics enable real-time risk assessment and fraud detection, reducing tax evasion while enhancing efficiency. NLP applications, including AI-powered chatbots, are revolutionizing taxpayer interactions by providing automated assistance and legal interpretations. RPA enhances the speed and accuracy of tax return processing, while blockchain technology promotes transparency and data integrity. AI-driven policy modeling further allows governments to simulate tax reforms and optimize revenue collection strategies. Despite its potential, AI-driven tax transformation faces critical challenges, including data security risks, ethical concerns regarding algorithmic bias, and integration hurdles with legacy tax systems. Regulatory adaptation is essential to ensure accountability, fairness, and taxpayer trust in AI-powered tax processes. This paper highlights policy recommendations to foster a balanced approach to AI integration, emphasizing the need for robust regulatory frameworks, public-private collaboration, and AI literacy among tax professionals. By leveraging AI-driven tax technologies, the U.S. can achieve greater efficiency, compliance accuracy, and taxpayer engagement. However, careful implementation, ethical safeguards, and continuous innovation will be key to ensuring an equitable and transparent tax system. This conceptual framework serves as a foundation for understanding the transformative role of AI in modern tax administration and shaping its future trajectory.

Technology development has grown rapidly in the last decades and gained importance for accounting and auditing through its identified potentials. Particularly the automation of judgment systems and systems that require human intervention, are deemed to be more relevant to confront a transformation through Robotic Process Automation (RPA). During the continuous development, the augmentation of such systems through Artificial Intelligence (AI) presents a greenfield project with high expectations. However theoretical frameworks have not yet been elaborative and sufficient to capture how such deployments can be conducted. Addressing this research gap, this study presents a summarized overview of the transforming RPA ecosystem and indicates what challenges are critical to being confronted for a successful implementation of such systems in accounting and auditing.

The research looks at how AI, Blockchain and Autonomous Compliance Technologies can be added to tax administrations to help increase transparency, efficiency and compliance in managing public finances. This research uses Rogers’ theory about innovation diffusion, it examines how various technologies are gaining acceptance and use in institutions. To analyze five progressive countries in the digital world, I used a qualitative multiple-case study design such as Estonia, Armenia, Singapore, South Korea and the United States, since they all differ in their digital and government technology levels. The study included 15 informants who were chosen because they were actively engaged in digital transformation activities for their tax agencies.  Semi-structured interviews and an in-depth review of documents like national policies, technical reports and evaluation reports from the World Wide Web Foundation, the findings were reached. Codes were made in NVivo 12 and each code related to concepts present in the study’s theory. It has been found that AI improves the handling of risks, AI is effective in identifying fraud, blockchain ensures that transactions are unchangeable and visible to auditors and AI-powered compliance keeps reports accurate and regularly updated.It is clear from the findings that clearly written regulations, strong safety rules, improved institutions and engaging many stakeholders help implement the guidelines better. Additionally, it is advised to organize coordination across nations, test the relevant technologies and continuously observe their process to responsibly scale them up.

… tax data – conditions often hindered by stringent privacy concerns and fragmented data governance … : The role of artificial intelligence and cryptocurrency in tax systems optimizing and …

The study aims to examine trends in the integration of artificial intelligence within the operational processes of tax administrations across the Member States of the European Union. It explores both the functional domains in which AI can be deployed and the institutional, ethical, regulatory and technological constraints that shape its deeper integration. The analysis relies on publicly available data from the Organisation for Economic Co-operation and Development (OECD), complemented by information from other open sources. Based on this dataset, the study develops a Tax AI Index (TAI) to provide a comparative quantitative assessment of the extent to which AI systems have been operationally integrated into EU tax administrations. The index is constructed from four subindices capturing (1) the use of artificial intelligence in communication between tax administrations and economic agents (TAIIS); (2) the integration of artificial intelligence in data management systems (TAIDS); (3) the application of algorithmic systems in tax enforcement, compliance control and administrative decisions (TAIRES); and (4) mechanisms for accountability, transparency and ethical oversight in the use of artificial intelligence (TAIGS). The empirical results indicate significant heterogeneity in the levels of digital transformation among the EU-27 Member States. In most countries, the adoption of artificial intelligence remains at an experimental or pilot stage, suggesting that its broader operational application is still evolving. To place these findings in a broader context, the analysis is complemented by an external measure of digital government development, allowing for a comparative assessment between AI adoption in tax administrations and overall public sector digital maturity.

This article explores how Artificial Intelligence (AI) can enhance tax administration by improving compliance, increasing trans-parency, and addressing ethical and regulatory challenges. It specifically analyzes the role of natural language processing (NLP), machine learning (MI), and intelligent chatbots in simplifying complex tax legislation, thereby enabling more effective taxpayer interaction with tax authorities. Through an examination of Al implementations in selected countries, the paper illustrates tangible improvements in compliance processes and transparency. Additionally, the discussion critically addresses the ethical concerns and practical challenges associated with AI deployment, including data privacy, potential biases, and compliance with regulatory frameworks such as the EU AI Act. The article concludes by emphasizing the necessity of balancing technological advancement with robust governance mechanisms to maintain taxpayer trust and promote equitable outcomes.

BACKGROUND The topicality of the research lies in the fact that the pace of artificial intelligence (AI) and Big Data technology adoption in the public administration sector is shifting radically the approaches to the administration of taxation, the transparency and accountability of the institutions. The growing digital aspect of fiscal frameworks necessitates the development of a unified regulatory framework and global standard of the ethical application of AI. The purpose of the study is to identify the regulatory prerequisites for the introduction of AI and Big Data in the public administration of tax systems in the international context. The object of the study is tax administrations of different countries that integrate analytical algorithms into tax risk management processes. MATERIALS AND METHODS The methodological basis is based on comparative and analytical, content analysis of regulatory documents, as well as empirical generalization of statistical indicators of digital maturity of fiscal authorities. RESULTS As a result, it is established that the highest level of tax administration efficiency is achieved in countries where the regulatory framework is harmonized with international ethical principles and provides for transparent mechanisms for controlling algorithms. The connection between the digital maturity index and growth in the tax revenue is disclosed, which proves the possibility to use AI in the fiscal processes analytically. CONCLUSION The author evaluates the major international documents (OECD, IMF, World Bank, Council of Europe, European Parliament) which are a single set of laws on the responsible usage of intellectual technologies. The practical value of the obtained results is associated with the fact that the suggested analytical model could be implemented to create national plans of the digital transformation of tax authorities and enhance their openness and effectiveness in management. The study is the foundation of the subsequent investigation of the interplay between regulatory frameworks, technological maturity and ethics of digital governance.

Artificial intelligence (AI) has emerged as a strategic driver of digital transformation in public financial management, reshaping how tax administrations manage compliance oversight, detect fraud, and deliver services. The rapid expansion of digital transactions, increasingly complex taxpayer behaviors, and the limitations of conventional audit methods have created an urgent need for evidence-based insights into how AI is being deployed within modern tax systems. This study aims to synthesize current scientific knowledge across three analytical dimensions: how AI is implemented in tax administrations, the technological, organizational, and regulatory challenges encountered during adoption, and the measurable performance outcomes resulting from AI integration. This research employs a qualitative Systematic Literature Review (SLR) approach, drawing exclusively from peer-reviewed publications indexed in Scopus between 2020 to 2025. Data were collected through a structured keyword search, followed by multi-stage screening based on relevance, publication year, language, and open-access availability. The final dataset consisted of 37 eligible studies. Data analysis was conducted using thematic synthesis, enabling the identification of recurring patterns and cross-study insights related to implementation strategies, adoption barriers, and administrative impacts. The findings show that AI is implemented through digitization, automation, and intelligence-enhancing strategies, supported by machine learning, predictive analytics, and intelligent taxpayer service systems. Key challenges include data fragmentation, legacy infrastructures, skill gaps, and regulatory constraints. The review also confirms substantial improvements in administrative efficiency, fraud detection accuracy, and revenue performance. This study concludes that successful AI integration requires strong data governance, institutional readiness, and ethical safeguards.

… intelligent tax system, the Tax … data governance model in the country’s tax affairs organization. According to the plan of the country’s tax organization regarding the smartening of the tax …