A Hybrid AI-Driven Decision Support Framework for Intelligent Tax Filing and Regulatory Compliance

  • Unique Paper ID: 197012
  • Volume: 12
  • Issue: 11
  • PageNo: 6709-6714
  • Abstract:
  • Tax filing and regulatory compliance present significant challenges for individuals and small businesses due to complex tax legislation, frequent policy revisions, and limited user awareness of applicable regulations. Although existing electronic filing platforms provide basic automation, they lack intelligent decision support, real-time error detection, and personalized user assistance. This paper proposes a Hybrid AI-Driven Decision Support Framework for intelligent tax filing that integrates rule-based reasoning, machine learning (ML), and natural language processing (NLP) into a unified four-layer architecture comprising a frontend application, secure backend processing, an AI and automation layer powered by large language models and the n8n workflow engine, and a persistent database layer. The system automates tax computation, performs multi-regime tax liability comparison, delivers AI-based investment recommendations, and provides conversational query resolution through NLP. Experimental evaluation on a dataset of 1,200 anonymized taxpayer records demonstrates 100% accuracy in tax computation, F1-scores ranging from 0.895 to 1.00 across NLP query categories, and a 1.78× computational efficiency gain over traditional rule-based systems. The proposed framework significantly reduces manual effort, minimizes filing errors, and enhances compliance for individual taxpayers and small enterprises.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{197012,
        author = {Piyusha Patil and Trupti Jagtap and Rutuja Patil and Dr. Girish Navale},
        title = {A Hybrid AI-Driven Decision Support Framework for Intelligent Tax Filing and Regulatory Compliance},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6709-6714},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197012},
        abstract = {Tax filing and regulatory compliance present significant challenges for individuals and small businesses due to complex tax legislation, frequent policy revisions, and limited user awareness of applicable regulations. Although existing electronic filing platforms provide basic automation, they lack intelligent decision support, real-time error detection, and personalized user assistance. This paper proposes a Hybrid AI-Driven Decision Support Framework for intelligent tax filing that integrates rule-based reasoning, machine learning (ML), and natural language processing (NLP) into a unified four-layer architecture comprising a frontend application, secure backend processing, an AI and automation layer powered by large language models and the n8n workflow engine, and a persistent database layer. The system automates tax computation, performs multi-regime tax liability comparison, delivers AI-based investment recommendations, and provides conversational query resolution through NLP. Experimental evaluation on a dataset of 1,200 anonymized taxpayer records demonstrates 100% accuracy in tax computation, F1-scores ranging from 0.895 to 1.00 across NLP query categories, and a 1.78× computational efficiency gain over traditional rule-based systems. The proposed framework significantly reduces manual effort, minimizes filing errors, and enhances compliance for individual taxpayers and small enterprises.},
        keywords = {Artificial Intelligence, Automation, Decision Support System, Hybrid AI Framework, Machine Learning, Natural Language Processing, Regulatory Compliance, Rule-Based Reasoning, Tax Filing, Tax Regime Optimization},
        month = {April},
        }

Cite This Article

Patil, P., & Jagtap, T., & Patil, R., & Navale, D. G. (2026). A Hybrid AI-Driven Decision Support Framework for Intelligent Tax Filing and Regulatory Compliance. International Journal of Innovative Research in Technology (IJIRT). https://doi.org/doi.org/10.64643/IJIRTV12I11-197012-459

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