Analysis of disruptive business models: leveraging AI to ‎transform accounting services

  • Authors

    • Dr. Bamidele Segun Ilugbusi Department of Public Administration, Faculty of Management Sciences, Durban University of Technology, Greyville, Durban, 4001, ‎South Africa
    • Professor Nirmala Phd MPA Dorasamy Department of Public Administration, Faculty of Management Sciences, Durban University of Technology, Greyville, Durban, 4001, ‎South Africa
    https://doi.org/10.14419/w04bmy85

    Received date: May 1, 2025

    Accepted date: June 2, 2025

    Published date: June 4, 2025

  • Accounting; Business; Disruptive; Services; Transform
  • Abstract

    This paper explores the transformative potential of leveraging artificial intelligence (AI) to disrupt and revolutionize accounting services. ‎Acknowledging the limitations of traditional accounting practices, the paper delves into the current landscape of the industry and identifies ‎emerging technologies. It provides an in-depth understanding of AI applications in accounting, emphasizing the benefits of integration. The ‎core of the paper outlines a comprehensive disruptive business model, comprising automated data entry, predictive analytics, fraud detection, ‎client interaction, and customized financial reporting. Each component is discussed in detail, highlighting the role of AI in reshaping ‎conventional processes. Implementation strategies, including phased integration and staff upskilling, are presented to guide businesses ‎through the transition. To illustrate practical applications, the paper includes case studies of successful AI-driven disruptions in accounting ‎services, offering valuable insights and lessons learned. The challenges and risks associated with AI implementation are also addressed, ‎emphasizing the importance of ethical considerations and regulatory compliance. Looking ahead, the paper outlines future trends in AI for ‎accounting services, providing a glimpse into the evolving landscape. It concludes with a compelling call to action for businesses to embrace ‎these disruptive models, recognizing the potential for enhanced efficiency, accuracy, and client satisfaction in the rapidly evolving ‎accounting industry‎.

  • References

    1. Agrawal AK, Gans JS & Goldfarb A (2018), Prediction, Judgment and Complexity: a theory of decision making and artificial intelligence. SSRN Electronic Journal 89–110. https://doi.org/10.2139/ssrn.3103156.
    2. Agyei-Boapeah H, Evans R & Nisar TM (2022), Disruptive innovation: Designing business platforms for new financial services. Journal of Business Research 150, 134–146. https://doi.org/10.1016/j.jbusres.2022.05.066.
    3. Ahmed R, Shaheen S & Philbin SP (2022), The role of big data analytics and Decision-Making in achieving project success. SSRN Electronic Jour-nal 1(03), 148–159. https://doi.org/10.2139/ssrn.4190817.
    4. Al JME (2021), A review of the robotic process automation’s impact as a disruptive innovation in accounting and audit. turcomat.org 12(12), 3675–3682. https://doi.org/10.17762/turcomat.v12i10.5056.
    5. Alao NOB, Dudu NOF, Alonge NEO & Eze NCE (2024), Automation in financial reporting: A conceptual framework for efficiency and accuracy in U.S. corporations. Global Journal of Advanced Research and Reviews 2(2), 040–050. ttps://doi.org/10.58175/gjarr.2024.2.2.0057.
    6. Bahja M (2020), Natural Language Processing Applications in business. In E-Business-higher education and intelligence applications. https://doi.org/10.5772/intechopen.92203.
    7. Bharadiya JP (2023), Machine Learning and AI in Business Intelligence: Trends and Opportunities. International Journal of Computer (IJC) 48(1), 1–12. https://ijcjournal.org/index.php/InternationalJournalOfComputer/index.
    8. Chatzis SP, Siakoulis V, Petropoulos A, Stavroulakis E & Vlachogiannakis N (2018), Forecasting stock market crisis events using deep and statisti-cal machine learning techniques. Expert Systems With Applications 112, 353–371. https://doi.org/10.1016/j.eswa.2018.06.032.
    9. Dai J & Vasarhelyi MA (2017), Toward Blockchain-Based accounting and assurance. Journal of Information Systems 31(3), 5–21. https://doi.org/10.2308/isys-51804.
    10. Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, Duan Y, Dwivedi R, Edwards J, Eirug A, Galanos V, Ilavarasan PV, Janssen M, Jones P, Kar AK, Kizgin H, Kronemann B, Lal B, Lucini B, Williams MD (2019), Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002.
    11. Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR & Turner K (2023), Natural Language Processing in Electronic Health Records in rela-tion to healthcare decision-making: A systematic review. Computers in Biology and Medicine 155, 106649. https://doi.org/10.1016/j.compbiomed.2023.106649.
    12. Jain V & Kulkarni PA (2023), Integrating AI techniques for enhanced financial forecasting and budgeting strategies. International Journal of Eco-nomics and Management Studies 10(09), 9–15. https://doi.org/10.14445/23939125/IJEMS-V10I9P102.
    13. Kapoor K, Bigdeli AZ, Dwivedi YK, Schroeder A, Beltagui A & Baines T (2021), A socio-technical view of platform ecosystems: Systematic re-view and research agenda. Journal of Business Research 128, 94–108. https://doi.org/10.1016/j.jbusres.2021.01.060.
    14. Kehinde O (2025), Leveraging Data-Driven Decision-Making for enhanced risk management and resource allocation in projects. International Jour-nal of Science and Engineering Applications 14(02), 1–17. https://doi.org/10.7753/IJCATR1402.1001.
    15. Khatib ME, Almarri A, Almemari A & Alqassimi A (2023), How does robotics Process Automation (RPA) affect project management practices. Advances in Internet of Things 13(02), 13–30. https://doi.org/10.4236/ait.2023.132002.
    16. Ladda D (2014), Basic Concepts of Accounting. Lulu.com.
    17. Leitner C & Stiefmueller CM (2019), Disruptive technologies and the public sector: the changing dynamics of governance. In Springer eBooks (pp. 237–274). https://doi.org/10.1007/978-981-13-3215-9_8.
    18. Lytvyn A (2024), The evolution of business models in the digital age: trends and implications. European Journal of Economic and Financial Inno-vations 1(13), 219–227. https://doi.org/10.32750/2024-0121.
    19. Manfren M, Caputo P & Costa G (2010), Paradigm shift in urban energy systems through distributed generation: Methods and models. Applied En-ergy 88(4), 1032–1048. https://doi.org/10.1016/j.apenergy.2010.10.018.
    20. McKay EN (2013), UI is Communication: How to design intuitive, user centered interfaces by focusing on effective communication. https://dl.acm.org/citation.cfm?id=2513625.
    21. Montes J, Gómez-Cruz NA, Batz A, Cárdenas LFS & Holguín HM (2024), From crisis to opportunity through innovation. Management Research Review 47(9), 1441–1466. https://doi.org/10.1108/MRR-05-2023-0324.
    22. Nielsen S (2022), Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: a literature study and future directions. Journal of Accounting & Organizational Change 18(5), 811–853. https://doi.org/10.1108/JAOC-08-2020-0107.
    23. Odonkor NB, Kaggwa NS, Uwaoma NPU, Hassan NAO & Farayola NOA (2024), The impact of AI on accounting practices: A review: Exploring how artificial intelligence is transforming traditional accounting methods and financial reporting. World Journal of Advanced Research and Reviews 21(1), 172–188. https://doi.org/10.30574/wjarr.2024.21.1.2721.
    24. Osman IH & Anouze AL (2015), A Cognitive Analytics Management Framework (CAM-Part 3). In Advances in logistics, operations, and man-agement science book series (pp. 190–234). https://doi.org/10.4018/978-1-4666-4474-8.ch003.
    25. Phillips-Wren G, Daly M & Burstein F (2021), Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems 146, 113560. https://doi.org/10.1016/j.dss.2021.113560.
    26. Saeidi H & Prasad B (2019), Impact of Accounting Information Systems (AIS) on Organizational Performance: A case Study of TATA Consultan-cy Services (TCS) - India. Journal of Management and Accounting Studies 2(03), 54–60. https://doi.org/10.24200/jmas.vol2iss03pp54-60.
    27. Singh C, Dorward P & Osbahr H (2016), Developing a holistic approach to the analysis of farmer decision-making: Implications for adaptation poli-cy and practice in developing countries. Land Use Policy 59, 329–343. https://doi.org/10.1016/j.landusepol.2016.06.041.
    28. Thota S, Dixit RS, Nurpeiis M, Parida DK, Iissova A & Nigmetova A (2022), Robotics and automatics in terms of utilizing rules-based business processes. 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), 261–266. https://doi.org/10.1109/ICIRCA54612.2022.9985682.
    29. Wamba-Taguimdje S, Wamba SF, Kamdjoug JRK & Wanko CET (2020), Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411.
    30. William BD & Eric H (2023), Artificial intelligence disruption and its impacts on future employment in Africa - A case of the banking and financial sector in Ghana. I-manager’s Journal on Software Engineering 18(1), 19. https://doi.org/10.26634/jse.18.1.20082
  • Downloads

  • How to Cite

    Ilugbusi, D. B. S., & Dorasamy, P. N. . P. M. . (2025). Analysis of disruptive business models: leveraging AI to ‎transform accounting services. International Journal of Accounting and Economics Studies, 12(2), 43-46. https://doi.org/10.14419/w04bmy85