International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

Current Issues
     2026:7/3

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

The Future of Tax Technology in the United States: A Conceptual Framework for AI-Driven Tax Transformation

Full Text (PDF)

Open Access - Free to Download

Download Full Article (PDF)

Alternative download link

Abstract

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.

How to Cite This Article

Enuma Ezeife, Eseoghene Kokogho, Princess Eloho Odio, Mary Oyenike Adeyanju (2021). The Future of Tax Technology in the United States: A Conceptual Framework for AI-Driven Tax Transformation . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 2(6), 428-437. DOI: https://doi.org/10.54660/IJMRGE.2021.2.1.542-551

Export Citation:

BibTeX RIS EndNote

References

  1. 1. Al Karaawy NAA. Theimpactofmakingtaxdigitalapplicationontheaccountingcosts. Academyof Accountingand Financial Studies Journal.2018;22(3\:1-13.
  2. 2. Alm J, Beebe J, Kirsch MS, Marian O, Soled JA. Newtechnologiesandtheevolutionoftaxcompliance. Virginia Tax Review.2019;39:287.
  3. 3. Awasthi R, Lee HC, Poulin P, Choi JG, Kim WC, Lee OJ, Chang SY. The Benefitsof Electronic Tax Administrationin Developing Economies: AKorean Case Studyand Discussionof Key Challenges. World Bank;2019.
  4. 4. Bentley D. Taxpayerrightsandprotectionsinadigitalglobalenvironment. In: Ethicsand Taxation.2020:251-294.
  5. 5. Bhattarai BP, Paudyal S, Luo Y, Mohanpurkar M, Cheung K, Tonkoski R, etal. Bigdataanalyticsinsmartfuturedirections. IETSmart Grid.2019;2(2\:141-154.
  6. 6. Blank JD, Osofsky L. Automatedlegalguidance. Cornell Law Review.2020;106:179.
  7. 7. Boucher P, Unit SF. Panelforthe Futureof Scienceand Technology. Scientific Foresight Unit(STOA\.2020; PE:641.
  8. 8. Bumgarner N, Vasarhelyi MA. Continuousauditing Anewview. In: Continuous Auditing. Emerald Publishing Limited;2018:7-
  9. 51. International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com
  10. 4369. Cath C. Governingartificialintelligence: ethical, legalandtechnicalopportunitiesandchallenges. Philosophical Transactionsofthe Royal Society A: Mathematical, Physicaland Engineering Sciences.2018;376(2133\:20180080.
  11. 10. Chand V, Kostic S, Reis A. Taxing Artificial Intelligenceand Robots: Critical Assessmentofpotential Policy Solutionsand Recommendationfor Alternative Approaches-Sovereign Measure: Education Taxes/Global MEASURE: Global Education Taxor Planetary Tax. World Tax Journal.2020;711.
  12. 11. Cong Y, Du H, Vasarhelyi MA. Technologicaldisruptioninaccountingandauditing. Journalof Emerging Technologiesin Accounting.2018;15(2\:1-10.
  13. 12. Cooper LA, Holderness Jr DK, Sorensen TL, Wood DA. Roboticprocessautomationinpublicaccounting. Accounting Horizons.2019;33(4\:15-35.
  14. 13. Desouza KC, Dawson GS, Chenok D. Designing, developing, anddeployingartificialintelligencesystems: Lessonsfromandforthepublicsector. Business Horizons.2020;63(2\:205-213.
  15. 14. Dimitropoulou C, Govind S, Turcan L. Applyingmodern, disruptivetechnologiestoimprovetheeffectivenessoftaxtreatydisputeresolution: Part
  16. 1. Intertax.2018;46:856.
  17. 15. Engstrom DF, Ho DE, Sharkey CM, Cu?llar MF. Governmentbyalgorithm: Artificialintelligenceinfederaladministrativeagencies. NYUSchoolof Law, Public Law Research Paper.2020;(20-54\.
  18. 16. Ernst E, Merola R, Samaan D. Economicsofartificialintelligence: Implicationsforthefutureofwork. IZAJournalof Labor Policy.2019;9(1\.
  19. 17. Faith DO. Areviewoftheeffectofpricingstrategiesonthepurchaseofconsumergoods. International Journalof Researchin Management, Science&Technology.2018;2.
  20. 18. Feij?o C, Kwon Y, Bauer JM, Bohlin E, Howell B, Jain R, etal. Harnessingartificialintelligence(AI\toincreasewellbeingforall: Thecaseforanewtechnologydiplomacy. Telecommunications Policy.2020;44(6\:101988.
  21. 19. Fenwick M, Vermeulen EP, Corrales M. Businessandregulatoryresponsestoartificialintelligence: Dynamicregulation, innovationecosystemsandthestrategicmanagementofdisruptivetechnology. In: Robotics, AIandthe Futureof Law.2018:81-103.
  22. 20. Gichohi BW. Leveragingonbigdataandadvancedtechnologiestoenhancedomesticrevenuemobilization. Statistical Journalofthe IAOS.2020;36(S1\:111-119.
  23. 21. Jarrahi MH. Artificialintelligenceandthefutureofwork: Human-AIsymbiosisinorganizationaldecisionmaking. Business Horizons.2018;61(4\:577-586.
  24. 22. Kalkanci B, Rahmani M, Toktay LB. Theroleofinclusiveinnovationinpromotingsocialsustainability. Productionand Operations Management.2019;28(12\:2960-2982.
  25. 23. Kimani D, Adams K, Attah-Boakye R, Ullah S, Frecknall-Hughes J, Kim J. Blockchain, businessandthefourthindustrialrevolution: Whence, whither, whereforeandhow?. Technological Forecastingand Social Change.2020;161:120254.
  26. 24. Kovacev R. Ataxingdilemma: robottaxesandthechallengesofeffectivetaxationof AI, automationandroboticsinthefourthindustrialrevolution. Ohio State Technology Law Journal.2020;16:182.
  27. 25. Lauterbach A. Artificialintelligenceandpolicy: quovadis?. Digital Policy, Regulationand Governance.2019;21(3\:238-263.
  28. 26. Lezoche M, Hernandez JE, D?az MMEA, Panetto H, Kacprzyk J. Agri-food4.0: Asurveyofthesupplychainsandtechnologiesforthefutureagriculture. Computersin Industry.2020;117:103187.
  29. 27. Lips W. itscross-platformimpactinthe EUandthe OECD. Journalof European Integration.2020;42(7\:975-990.
  30. 28. Mc Kee M, Siladke CA, Vossler CA. Behavioraldynamicsoftaxcompliancewhentaxpayerassistanceservicesareavailable. International Taxand Public Finance.2018;25:722-756.
  31. 29. Migai CO, de Jong J, Owens JP. Thesharingeconomy: Turningchallengesintocomplianceopportunitiesfortaxadministrations. e Journalof Tax Research.2018;16:395.
  32. 30. Moin S, Karim A, Safdar Z, Safdar K, Ahmed E, Imran M. Securing Io Tsindistributedblockchain: Analysis, requirementsandopenissues. Future Generation Computer Systems.2019;100:325-343.
  33. 31. Munoko I, Brown-Liburd HL, Vasarhelyi M. Theethicalimplicationsofusingartificialintelligenceinauditing. Journalof Business Ethics.2020;167(2\:209-234.
  34. 32. Nazarov MA, Mikhaleva OL, Chernousova KS. Digitaltransformationoftaxadministration. In: Digital Age: Chances, Challengesand Future
  35. 7. Springer International Publishing;2020:144-149.
  36. 33. Ng YF, O'Sullivan M, Paterson M, Witzleb N. Revitalisingpubliclawinatechnologicalera: Rights, transparencyandadministrativejustice. Universityof New South Wales Law Journal.2020;43(3\:1041-1077.
  37. 34. Oyedokun OO. Greenhumanresourcemanagementpracticesanditseffectonthesustainablecompetitiveedgeinthe Nigerianmanufacturingindustry(Dangote\. Dublin Business School;2019.
  38. 35. Pencheva I, Esteve M, Mikhaylov SJ. Big Dataand AIAtransformationalshiftforgovernment: So, whatnextforresearch?. Public Policyand Administration.2020;35(1\:24-44.
  39. 36. Prichard W, Custers AL, Dom R, Davenport SR, Roscitt MA. Innovationsintaxcompliance: Conceptualframework. World Bank Policy Research Working Paper.2019;(9032\.
  40. 37. Purnamasari D, Tahir R, Sudaryo Y. Implementationof E-Filing Information Systemasa Public Policy Formin Increasing Taxpayer Compliance. Solid State Technology.2020;63(4\:5340-5349.
  41. 38. Ruan J, Yan Z, Dong B, Zheng Q, Qian B. Identifyingsuspiciousgroupsofaffiliated-transaction-basedtaxevasioninbigdata. Information Sciences.2019;477:508-532.
  42. 39. Scarcella L. Taxcomplianceandprivacyrightsinprofilingandautomateddecisionmaking. Internet Policy Review.2019;8(4\.
  43. 40. Setyowati MS, Utami ND, Saragih AH, Hendrawan A. Blockchaintechnologyapplicationforvalue-addedtaxsystems. Journalof Open Innovation: Technology, Market, and Complexity.2020;6(4\:
  44. 156. International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com
  45. 43741. Cornelius KB. Smartcontractsasevidence: Trust, records, andthefutureofdecentralizedtransactions. In: Second International Handbookof Internet Research.2020:627-646.
  46. 42. Strauss H, Fawcett T, Schutte D. Taxriskassessmentandassurancereforminresponsetothedigitalisedeconomy. Journalof Telecommunicationsandthe Digital Economy.2020;8(4\:96-126.
  47. 43. Tan E, Kleizen B, Simonofski A, Willem P, Sabbe M. Digital(R\evolutionin Belgian Federal Government: An Open Governance Ecosystemfor Big Data, Artificial Intelligence, and Blockchain(DIGI4FED\. In: Database Systemsfor Advanced Applications(DASFAA2020\.2020;12112.
  48. 44. Tapscott D, Kaplan A. Blockchainrevolutionineducationandlifelonglearning. Blockchain Research Institute-IBMInstitutefor Business Value;2019.
  49. 45. Truby J. Governingartificialintelligencetobenefitthe UNsustainabledevelopmentgoals. Sustainable Development.2020;28(4\:946-959.
  50. 46. Vishnevsky VP, Chekina VD. Robotvs. taxinspectororhowthefourthindustrialrevolutionwillchangethetaxsystem: areviewofproblemsandsolutions. Journalof Tax Reform.2018;4(1\:6-26.
  51. 47. Wong A. Thelawsandregulationof AIandautonomoussystems. In: Unimagined Futures ICTOpportunitiesand Challenges.2020:38-54.
  52. 48. Wu J, Tal A. Frompollutionchargetoenvironmentalprotectiontax: acomparativeanalysisofthepotentialinitiative. Journalof Comparative Policy Analysis: Researchand Practice.2018;20(2\:223-236.
  53. 49. Yang L, Elisa N, Eliot N. Privacyandsecurityaspectsof E-governmentinsmartcities. In: Smart Cities Cybersecurityand Privacy. Elsevier;2019:89-102.
  54. 50. Zheng S, Trott A, Srinivasa S, Naik N, Gruesbeck M, Parkes DC, Socher R. The AIeconomist: Improvingequalityandproductivitywith AI-driventaxpolicies. ar Xivpreprintar Xiv:2004.13332.2020.

Share This Article: