Forensic Analysis of Corporate Tax Report Manipulation Using Data Mining Techniques
DOI:
https://doi.org/10.70062/jiafc.v1i3.233Keywords:
Artificial Intelligence, Blockchain, Corporate Tax, Data Mining, Fraud DetectionAbstract
Taxation is a critical source of national revenue, supporting public services, infrastructure development, and economic growth. Corporate tax not only contributes substantially to state income but also serves as a regulatory tool for fiscal policy. However, manipulation of corporate tax reports remains a persistent challenge, causing significant losses in government revenue. Traditional manual audit processes, often based on random sampling, are limited in their ability to detect hidden patterns of non-compliance within large and complex datasets, leading to inefficiencies and missed opportunities for revenue recovery. Advances in information technology, particularly data mining, have shown promise in enhancing the accuracy of forensic analysis in detecting potential tax fraud. Techniques such as CHAID decision trees, link analysis, and machine learning algorithms can classify suspicious transactions and support automated fraud detection models. Moreover, integrating artificial intelligence (AI) and blockchain technology improves transparency, security, and traceability in modern tax systems. Empirical studies have demonstrated that AI-based approaches, including Artificial Neural Networks (ANN), achieve high accuracy, precision, and recall in detecting income tax fraud. The combination of blockchain with data mining further strengthens identity verification and data tracking. As financial transactions and data volumes continue to grow globally, adopting these technologies becomes increasingly essential for effective tax surveillance. Successful implementation requires enhanced digital infrastructure, skilled human resources, and cross-agency collaboration. Future research should explore the application of these technologies in diverse national and organizational contexts to optimize tax compliance and fraud prevention strategies.
References
[1] P. C. Oslund, “Corporation income and franchise taxes,” Handbook on Taxation, pp. 205–227, 2019, doi: 10.4324/9781315093161-10.
[2] Z. I. Ruziev, L. K. Kadirov, M. E. Ostonova, B. S. Baratov, and S. Ortiq, “The role of income tax individuals in replenishing state budget revenues,” J. Adv. Res. Dyn. Control Syst., vol. 12, no. 7 (Special Issue), pp. 2033–2037, 2020, doi: 10.5373/JARDCS/V12SP7/20202320.
[3] C. O. Omodero and J. L. Yado, “Financial policies and corporate income tax administration in Nigeria,” Int. J. Financ. Stud., vol. 13, no. 2, art. no. 52, 2025, doi: 10.3390/ijfs13020052.
[4] K. Kaavya et al., “Economic consequences of corporate tax reforms in the post-pandemic era,” Int. J. Account. Econ. Stud., vol. 12, no. SI-1, pp. 286–290, 2025, doi: 10.14419/dgz8gy95.
[5] Y.-H. Deng and T. Luo, “Tax revenue manipulation by local taxation administrations in China,” Asia-Pac. J. Account. Econ., vol. 18, no. 1, pp. 61–75, 2011, doi: 10.1080/16081625.2011.9720874.
[6] M. P. Bach, K. Dumičić, B. Žmuk, T. Ćurlin, and J. Zoroja, “Internal fraud in a project-based organization: CHAID decision tree analysis,” Procedia Comput. Sci., vol. 138, pp. 680–687, 2018, doi: 10.1016/j.procs.2018.10.090.
[7] R.-S. Wu, C. S. Ou, H.-Y. Lin, S.-I. Chang, and D. C. Yen, “Using data mining technique to enhance tax evasion detection performance,” Expert Syst. Appl., vol. 39, no. 10, pp. 8769–8777, 2012, doi: 10.1016/j.eswa.2012.01.204.
[8] G. V. Mabe-Madisa, “A decision tree and Naïve Bayes algorithm for income tax prediction,” Afr. J. Sci. Technol. Innov. Dev., vol. 10, no. 4, pp. 401–409, 2018, doi: 10.1080/20421338.2018.1466440.
[9] L. Balkaran, “Understanding and applying data mining and data analysis techniques,” EDPACS, vol. 56, no. 1, pp. 1–6, 2017, doi: 10.1080/07366981.2017.1343550.
[10] M. P. Bach, T. Ćurlin, K. Dumičić, J. Zoroja, and B. Žmuk, “Data mining approach to internal fraud in a project-based organization,” Int. J. Inf. Syst. Proj. Manag., vol. 8, no. 2, pp. 81–101, 2020, doi: 10.12821/ijispm080204.
[11] B. F. Murorunkwere, O. Tuyishimire, D. Haughton, and J. Nzabanita, “Fraud detection using neural networks: A case study of income tax,” Future Internet, vol. 14, no. 6, art. no. 168, 2022, doi: 10.3390/fi14060168.
[12] T. Ruzgas et al., “Tax fraud reduction using analytics in an East European country,” Axioms, vol. 12, no. 3, art. no. 288, 2023, doi: 10.3390/axioms12030288.
[13] A. J. Shakadwipi, D. C. Jain, and S. Nagini, “Fraud detection system for identity crime using blockchain technology and data mining algorithms,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 9s, pp. 247–251, 2024.
[14] V. M. Sushkov, P. Y. Leonov, O. S. Nadezhina, and I. Y. Blagova, “Integrating data mining techniques for fraud detection in financial control processes,” Int. J. Technol., vol. 14, no. 8, pp. 1675–1684, 2023, doi: 10.14716/ijtech.v14i8.6830.
[15] A. M. Ali, R. F. Futaih, M. Shukur, and A. K. Al-Orfali, “Forensic accounting and fraud detection: Emerging trends and techniques,” J. Ecohumanism, vol. 3, no. 5, pp. 525–542, 2024, doi: 10.62754/joe.v3i5.3921.
[16] A. Balashov and A. Sanina, “Tax Evasion,” in Global Encyclopedia of Public Administration, Public Policy, and Governance, pp. 5861–5867, 2018, doi: 10.1007/978-3-319-20928-9_318.
[17] L. Snider, “Globalized Tax Evasion and Corruption: Two Steps Forward, One Step Back,” Journal of White Collar and Corporate Crime, vol. 5, no. 2, pp. 113–121, 2024, doi: 10.1177/2631309X241236202.
[18] Y. A. Nuraindra and A. C. Laksmi, “Tax Avoidance in State Owned and Family-Owned Enterprises: An Indonesian Study,” in Lecture Notes in Networks and Systems, vol. 924, pp. 193–205, 2024, doi: 10.1007/978-3-031-53998-5_17.
[19] C. Sá and H. Alves, “Tax Avoidance and Companies’ Opacity: A Theoretical Approach,” in Springer Proceedings in Business and Economics, pp. 421–434, 2022, doi: 10.1007/978-3-030-99873-8_33.
[20] E. Sumantri, A. A. Samudra, and A. Suradika, “Global tax avoidance and evasion: A landscape through insights from a systematic literature review and bibliometric analysis,” Journal of Infrastructure, Policy and Development, vol. 8, no. 9, art. no. 8073, 2024, doi: 10.24294/jipd.v8i9.8073.
[21] R.-S. Wu, C. S. Ou, H.-Y. Lin, S.-I. Chang, and D. C. Yen, “Using data mining technique to enhance tax evasion detection performance,” Expert Systems with Applications, vol. 39, no. 10, pp. 8769–8777, 2012, doi: 10.1016/j.eswa.2012.01.204.
[22] M. Nuryani, A. B. Mutiara, I. M. Wiryana, D. Purnamasari, and S. N. W. Putra, “Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes,” APTISI Transactions on Technopreneurship, vol. 6, no. 3, pp. 339–356, 2024, doi: 10.34306/att.v6i3.436.
[23] C. Asbe, A. Sahoo, P. H. Khan, A. M. Kalidhas, I. Kapila, P. Aggarwal, and N. Agrawal, “The Role of Forensic Accounting in Detecting Financial Fraud in Emerging Markets,” International Journal of Accounting and Economics Studies, vol. 12, no. sI-1, pp. 93–96, 2025, doi: 10.14419/2smzjj47.
[24] M. Talha, A. K. Khan, and S. M. Faisal, “Corporate Financial Reporting: Forensic Accounting Techniques for Detecting Financial Fraud and Boosting Profitability,” in Studies in Systems, Decision and Control, vol. 545, pp. 427–440, 2024, doi: 10.1007/978-3-031-65203-5_38.
[25] A. L. Martinez, “Accounting Expertise as an Evidentiary Tool in Economic Litigation,” Revista Eletronica de Direito Processual, vol. 25, no. 2, pp. 88–108, 2024.
[26] S. Ahmad, M. S. Ibrahim, and R. M. Abdou, “Understanding the Role of Forensic Accountants in Due Diligence Within Organizations and Its Impact on Organizational Learning,” in Studies in Big Data, vol. 171, pp. 3–10, 2025, doi: 10.1007/978-3-031-83911-5_1.
[27] M. W. Alkababji et al., “Forensic Accounting Practices: Evaluating the Current Research,” in Studies in Big Data, vol. 163, pp. 767–777, 2024, doi: 10.1007/978-3-031-73632-2_65.
[28] O. E. Akinbowale, P. Mashigo, and M. F. Zerihun, “The Integration of Forensic Accounting and Big Data Technology Frameworks for Internal Fraud Mitigation in the Banking Industry,” Cogent Business and Management, vol. 10, no. 1, art. 2163560, 2023, doi: 10.1080/23311975.2022.2163560.
[29] F. A. Amani and A. M. Fadlalla, “Data Mining Applications in Accounting: A Review of the Literature and Organizing Framework,” International Journal of Accounting Information Systems, vol. 24, pp. 32–58, 2017, doi: 10.1016/j.accinf.2016.12.004.
[30] M. Quintana, S. Uribe, F. Sánchez, and F. Álvarez, “Recommendation Techniques in Forensic Data Analysis: A New Approach,” IET Seminar Digest, vol. 2015, no. 5, 2015, doi: 10.1049/ic.2015.0113.
[31] M. Gupta, P. K. Aggarwal, and R. Gupta, “Revitalizing the Forensic Accounting: An Exploratory Study on Mitigating the Financial Risk Using Data Analytics,” International Journal of Experimental Research and Review, vol. 41, pp. 227–238, 2024, doi: 10.52756/ijerr.2024.v41spl.019.
[32] G. Gabrielli, C. Magri, A. Medioli, and P. L. Marchini, “The Power of Big Data Affordances to Reshape Anti-Fraud Strategies,” Technological Forecasting and Social Change, vol. 205, art. 123507, 2024, doi: 10.1016/j.techfore.2024.123507.
[33] J. O. Odia and O. T. Akpata, “Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection,” in Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, pp. 203–227, 2020, doi: 10.4018/978-1-7998-3053-5.ch011.
[34] G. Yu, “Enhancing Accounting Informatization Through Cloud Data Integrity Verification: A Bilinear Pairing Approach,” Journal of the Knowledge Economy, 2024, doi: 10.1007/s13132-024-01994-x.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Investigative Auditing & Financial Crime
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
