Artificial Intelligence-Assisted Financial Statement Analysis and Tax Risk Assessment: Evidence from a Quasi-Experimental Study
DOI:
https://doi.org/10.61194/ijtc.v7i3.2374Keywords:
financial statement analysis, tax risk, artificial intelligence, tax accounting, tax supervisionAbstract
The increasing complexity of accounting–tax differences and risk-based tax administration has strengthened the need for more structured approaches to tax risk assessment. Financial statement analysis is widely used to identify potential tax risk signals; however, manual interpretation often produces inconsistent outcomes because financial indicators are highly interconnected and difficult to evaluate systematically. This study examines whether financial statement indicators can identify potential tax risk and whether Artificial Intelligence-assisted analysis improves the quality of tax risk identification compared with manual analysis. Using a quasi-experimental design with a difference-in-differences approach, this study evaluated the analytical performance of 130 Diploma III Tax students analyzing the financial statements of 25 Indonesian publicly listed companies during 2020–2024 through manual and Artificial Intelligence-assisted stages. Tax risk identification performance was assessed using a standardized score based on effective tax rate, book-tax differences, profitability ratios, and cash flow gaps. The findings indicate that Artificial Intelligence-assisted analysis was associated with more consistent, structured, and efficient interpretation of financial statements compared with manual analysis. This study contributes to tax accounting literature by integrating Artificial Intelligence-assisted financial statement analysis into a structured tax risk assessment framework.
References
Allen, E., O’Leary, D. E., Qu, H., & Swenson, C. W. (2021). Tax specific versus generic accounting-based textual analysis and the relationship with effective tax rates: Building context. Journal of Information Systems, 35(2), 115–147.
Al-Okaily, M. (2025). Attitudes toward the adoption of accounting analytics technology in the digital transformation landscape. Journal of Accounting & Organizational Change, 21(3), 593–613. https://doi.org/https://doi.org/10.1108/JAOC-04-2024-0127
Artene, A. E., Domil, A. E., & Ivascu, L. (2024). Unlocking business value: Integrating AI-driven decision-making in financial reporting systems. Electronics, 13(15), 3069.
Ashtiani, M. N., & Raahemi, B. (2021). Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review. Ieee Access, 10, 72504–72525.
Benedek, P., & Bognár, F. (2024). Compliance Risk Assessment–Results of a Comprehensive Literature Revie.
Cerciello, M., Busato, F., & Taddeo, S. (2023). The effect of sustainable business practices on profitability. Accounting for strategic disclosure. Corporate Social Responsibility and Environmental Management, 30(2), 802–819.
Dengel, A., Gehrlein, R., Fernes, D., Görlich, S., Maurer, J., Pham, H. H., Großmann, G., & Eisermann, N. D. genannt. (2023). Qualitative research methods for large language models: Conducting semi-structured interviews with ChatGPT and BARD on computer science education. Informatics, 10(4), 78.
Drobyshevskaya, L., Vylegzhanina, E., Grebennikova, V., & Mamiy, E. (2020). The main approaches to assessing efficiency of tax administration and control in the context of digitalization. International Conference on Integrated Science, 95–111.
El-Feel, H. W. T., Amin, H. M. G., Mohamed, D. M., & Mohamed, E. K. A. (2025). Assessing board attributes in shaping corporate tax behavior: a bibliometric analysis and implications for future research. Journal of Accounting Literature, 1–42.
Fasolo, B., Heard, C., & Scopelliti, I. (2025). Mitigating cognitive bias to improve organizational decisions: An integrative review, framework, and research agenda. Journal of Management, 51(6), 2182–2211.
Filosa, J. R., Huang, J., Lei, L., & Stein, S. E. (2025). Does tax enforcement inform auditors’ risk assessment? Evidence from key audit matters. Contemporary Accounting Research, 42(2), 1423–1454.
Görlitz, A., & Dobler, M. (2023). Financial accounting for deferred taxes: A systematic review of empirical evidence. Management Review Quarterly, 73(1), 113–165.
How, M.-L., Cheah, S.-M., Chan, Y.-J., Khor, A. C., & Say, E. M. P. (2020). Artificial intelligence-enhanced decision support for informing global sustainable development: A human-centric AI-thinking approach. Information, 11(1), 39.
Hung, B. Q., Hoa, T. A., Hoai, T. T., & Nguyen, N. P. (2023). Advancement of cloud-based accounting effectiveness, decision-making quality, and firm performance through digital transformation and digital leadership: Empirical evidence from Vietnam. Heliyon, 9(6).
Jarrahi, M. H., Lutz, C., & Newlands, G. (2022). Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation. Big Data & Society, 9(2), 20539517221142824.
Mgammal, M. H. (2020). Corporate tax planning and corporate tax disclosure. Meditari Accountancy Research, 28(2), 327–364.
Miller, C. J., Smith, S. N., & Pugatch, M. (2020). Experimental and quasi-experimental designs in implementation research. Psychiatry Research, 283, 112452.
Mohammed, H., & Tangl, A. (2023). Taxation perspectives: Analyzing the factors behind viewing taxes as punishment—A comprehensive study of taxes as service or strain. Journal of Risk and Financial Management, 17(1), 5.
Neuman, S. S., Omer, T. C., & Schmidt, A. P. (2020). Assessing tax risk: Practitioner perspectives. Contemporary Accounting Research, 37(3), 1788–1827.
Nguyen, H. N. (2019). Enhancing the capacity of tax authorities and its impact on transfer pricing activities of FDI enterprises in Ha Noi, Ho Chi Minh, Dong Nai, and Binh Duong province of Vietnam. Management Science Letters, 9(8), 1299–1310. https://doi.org/10.5267/j.msl.2019.4.011
Nguyen, J. H. (2021). Tax avoidance and financial statement readability. European Accounting Review, 30(5), 1043–1066.
Nguyen, P. T. (2025). AI Technology in Auditing and Financial Error Detection. International Congress on Information and Communication Technology, 89–105.
Nissim, D. (2021). Earnings quality. Columbia Business School Research Paper Forthcoming. Assessed from Https://Ssrn. Com/Abstract, 3794378.
OECD. (2022). OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations 2022. In OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations. OECD.
Oguttu, A. W. (2020). Challenges of applying the comparability analysis in curtailing transfer pricing: Evaluating the suitability of some alternative approaches in Africa. Intertax, 48(1).
Qatawneh, A. M. (2025). The role of artificial intelligence in auditing and fraud detection in accounting information systems: moderating role of natural language processing. International Journal of Organizational Analysis, 33(6), 1391–1409.
Rahman, S., Sirazy, M. R. M., Das, R., & Khan, R. S. (2024). An exploration of artificial intelligence techniques for optimizing tax compliance, fraud detection, and revenue collection in modern tax administrations. International Journal of Business Intelligence and Big Data Analytics, 7(3), 56–80.
Saragih, A. H., Reyhani, Q., Setyowati, M. S., & Hendrawan, A. (2023). The potential of an artificial intelligence (AI) application for the tax administration system’s modernization: the case of Indonesia. Artificial Intelligence and Law, 31(3), 491–514.
Scientific, L. L. (2024). Optimization of smart taxation using artificial intelligence: risks and opportunities. Journal of Theoretical and Applied Information Technology, 102(5).
Solano, M. C., & Cruz, J. C. (2024). Integrating analytics in enterprise systems: A systematic literature review of impacts and innovations. Administrative Sciences, 14(7), 138.
Spyromitros, E., & Panagiotidis, M. (2022). The impact of corruption on economic growth in developing countries and a comparative analysis of corruption measurement indicators. Cogent Economics & Finance, 10(1), 2129368.
Strauss, H., Fawcett, T., & Schutte, D. (2020). Tax risk assessment and assurance reform in response to the digitalised economy. Journal of Telecommunications and the Digital Economy, 8(4), 96–126.
Tanko, U. M. (2025). Financial attributes and corporate tax planning of listed manufacturing firms in Nigeria: moderating role of real earnings management. Journal of Financial Reporting and Accounting, 23(3), 1024–1056.
Theodorakopoulos, L., Thanasas, G., & Halkiopoulos, C. (2024). Implications of big data in accounting: Challenges and opportunities. Emerging Science Journal, 8(3), 1201–1214.
Velte, P. (2023). The link between corporate governance and corporate financial misconduct. A review of archival studies and implications for future research. Management Review Quarterly, 73(1), 353–411.
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Supriyadi Sukarno, Arief Budi Wardana, I Gede Komang Chahya Bayu Anta Kusuma, Nasikhudin
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
