Transparent Peer Review By Scholar9
ARTIFICIAL INTELLIGENCE IN TAX ADMINISTRATION
Abstract
Artificial Intelligence (AI) is rapidly transforming tax administration by enhancing efficiency, accuracy, and taxpayer services. This study provides an overview of AI’s impact on tax administration, focusing on its key applications and benefits. AI technologies, such as machine learning and natural language processing, are revolutionizing how tax authorities manage tasks and interact with taxpayers. One significant application of AI in tax administration is fraud detection. AI algorithms analyze large volumes of transaction data to identify patterns and anomalies indicative of fraudulent activities, thereby improving the ability to combat tax evasion. Automated compliance is another area where AI excels, streamlining routine tasks like data entry, document verification, and tax calculations, which reduces human error and increases operational efficiency. Predictive analytics powered by AI enables tax authorities to forecast revenue trends, assess risks, and evaluate the impact of policy changes. This data-driven approach enhances decision-making and resource allocation. Personalized services, facilitated by AI-driven chatbots and virtual assistants, offer tailored guidance and support to taxpayers, improving their overall experience and compliance. AI also enhances data analysis and reporting by processing vast datasets quickly and generating detailed insights. This capability supports more informed decision-making and strategic planning. Additionally, AI-driven risk assessment tools identify high-risk areas for targeted audits, optimizing resource use and improving audit effectiveness.
Uma Babu Chinta Reviewer
09 Sep 2024 01:30 PM
Approved
Relevance and Originality:
The article addresses a crucial topic—how AI is reshaping tax administration. Its focus on applications like fraud detection, automated compliance, and predictive analytics is highly relevant to current trends in administrative efficiency and taxpayer services. The integration of various AI technologies to enhance tax functions reflects originality and addresses a significant gap in understanding the practical benefits of AI in this sector.
Methodology:
The article provides an overview of AI applications but lacks a detailed description of the methodology used to study these impacts. It would benefit from more specifics on how data was collected and analyzed to assess the effectiveness of AI in tax administration. Detailed information on case studies, pilot projects, or empirical data used to evaluate AI’s impact would strengthen the methodological rigor.
Validity & Reliability:
The discussion on AI's applications is based on theoretical and general examples. To enhance validity and reliability, the article should include empirical evidence or case studies demonstrating AI’s performance in real-world tax administration scenarios. Data on the success rates of fraud detection algorithms, improvements in compliance tasks, or feedback on AI-driven services would provide a more solid foundation for the claims made.
Clarity and Structure:
The article is well-structured, with a clear presentation of the key applications and benefits of AI in tax administration. The explanations of each application are concise and informative. However, the clarity could be improved by providing more detailed examples or case studies that illustrate the practical implementation of AI tools. Organizing the content into distinct sections with headings for each application and its benefits would enhance readability.
Result Analysis:
The article outlines the potential benefits of AI, such as improved fraud detection and enhanced operational efficiency. However, it lacks detailed results or specific analyses from real-world implementations. Providing quantitative or qualitative results demonstrating the effectiveness of AI tools, such as reductions in fraud rates, improvements in compliance efficiency, or enhanced decision-making, would offer a more comprehensive result analysis. Discussing any challenges or limitations faced during AI implementation would also provide a more balanced view.
IJ Publication Publisher
Done Sir
Uma Babu Chinta Reviewer