The Role of Artificial Intelligence in Preventing Corruption
DOI:
https://doi.org/10.59022/ijlp.642Keywords:
Artificial Intelligence, Corruption Prevention, Public Procurement, Algorithmic Accountability, Due Process, Legal Governance, Anti-Corruption LawAbstract
Corruption remains a persistent challenge threatening public trust and economic development worldwide. This study examines how artificial intelligence contributes to preventing corruption within public institutions. It explores machine learning, network analysis, and predictive tools now used across procurement, taxation, and healthcare systems. The research addresses what legal framework should govern these technologies while ensuring accountability, fairness, and due process. Using qualitative doctrinal and document analysis, this study reviews current laws, regulations, and recent scholarly literature published within the past five years. Findings show artificial intelligence significantly improves detection speed and accuracy, yet existing laws rarely address algorithmic bias or the right to challenge automated findings. This gap leaves institutions and individuals exposed to unfair or unaccountable outcomes. The study proposes embedding transparency, human oversight, and appeal mechanisms into legal reform. It concludes that effective corruption prevention requires balancing technological capability with meaningful legal protection for those affected by these systems.
References
Aarab, A., El Marzouki, A., Boubker, O., & El Moutaqi, B. (2025). Integrating AI in public governance: A systematic review. Digital, 5(4), 59. https://doi.org/10.3390/digital5040059
Aizupiete, L., & Jekabsone, I. (2026). Who acts, and who is watched? Differences between top-down and bottom-up AI anti-corruption tool implementation methods. Public Policy and Administration, 25(1), 59–72. https://doi.org/10.5755/j01.ppaa.25.1.41828
Aldana, A., Falcón-Cortés, A., & Morales, H. (2022). A machine learning model to identify corruption in México's public procurement contracts. arXiv. https://doi.org/10.48550/arXiv.2211.01478
Allah Rakha, N. (2023). Artificial intelligence and sustainability. International Journal of Cyber Law, 1(3). https://doi.org/10.59022/ijcl.42
Allah Rakha, N. (2023). Artificial intelligence strategy of Uzbekistan: Policy framework, preferences, and challenges. International Journal of Law and Policy, 1(1). https://doi.org/10.59022/ijlp.27
Belahouaoui, R., & Alm, J. (2025). Tax fraud detection using artificial intelligence-based technologies: Trends and implications. Journal of Risk and Financial Management, 18(9), 502. https://doi.org/10.3390/jrfm18090502
Brandão, M. A., Lacerda, A., & others. (2024). PLUS: A semi-automated pipeline for fraud detection in public bids. Digital Government: Research and Practice, 5(1), 1–16. https://doi.org/10.1145/3616396
Cernăzanu-Glăvan, C., et al. (2025). Explainable AI and fuzzy linguistic interpretation for enhanced transparency in public procurement: Analyzing EU tender awards. Mathematics, 13(13), 2215. https://doi.org/10.3390/math13132215
Chen, X., Li, J., & Wang, Z. (2026). Construction of a hospital intelligent integrity supervision platform: Digital and intelligent practices for anti-corruption in public health. Frontiers in Public Health, 14, 1769451. https://doi.org/10.3389/fpubh.2026.1769451
De Michele, R., & Vieyra, J. C. (2022). From fishing to catching: Developing actionable red flags in public procurement to prevent and control corruption (Technical Note No. IDB-TN-2598). Inter-American Development Bank. https://doi.org/10.18235/0004595
Demarchi, M., et al. (2023). Algorithmic integrity: A predictive framework for combating corruption in public procurement through AI and data analytics. Journal of Financial and Managerial Research, 4(2), 130–141. https://doi.org/10.54660/JFMR.2023.4.2.130
Figueredo, E., Jr. (2026). Challenges of digital services in public administration in the era of artificial intelligence and blockchain. Journal of the Knowledge Economy. Advance online publication. https://doi.org/10.1007/s13132-026-03277-z
Gallego, J., Rivero, G., & Martínez, J. (2021). Preventing rather than punishing: An early warning model of malfeasance in public procurement. International Journal of Forecasting, 37(1), 360–377. https://doi.org/10.1016/j.ijforecast.2020.06.006
Gebrihet, H. G. (2023). Trust, corruption, and tax compliance in fragile states: On a quest for transforming Africa into future global powerhouse. Social Sciences, 13(1), 3. https://doi.org/10.3390/socsci13010003
Golec, M., et al. (2025). Artificial intelligence (AI): Foundations, trends and future directions. Telematics and Informatics Reports, 10, 100265. https://doi.org/10.1016/j.teler.2025.100265
Gupta, R., Jindal, V., & Kashyap, I. (2024). Recent state-of-the-art of fake review detection: A comprehensive review. The Knowledge Engineering Review, 39, e67. https://doi.org/10.1017/S0269888924000067
Irianto, G., & Nahar, S. (2025). Developing a fraud prevention framework for universities: A systematic literature review. Cogent Business & Management, 12(1), 2593736. https://doi.org/10.1080/23311975.2025.2593736
Kazbekova, A. (2025). Risk modelling as a way to diagnose corruption in procurement system: Practice and legislation. Social and Legal Studios, 8(1), 299–309. https://doi.org/10.32518/sals1.2025.299
Khan, H. U., & Nazir, S. (2024). Identifying the AI-based solutions proposed for restricting money laundering in financial sectors: Systematic mapping. Applied Artificial Intelligence, 38(1), 2344415. https://doi.org/10.1080/08839514.2024.2344415
Köbis, N. C., Starke, C., & Rahwan, I. (2022). The promise and perils of using artificial intelligence to fight corruption. Nature Machine Intelligence, 4(5), 418–424. https://doi.org/10.1038/s42256-022-00489-1
Lima, M. S. M., & Delen, D. (2020). Predicting and explaining corruption across countries: A machine learning approach. Government Information Quarterly, 37(1), 101413. https://doi.org/10.1016/j.giq.2019.101413
Lyeonov, S., Drašković, V., Kubaščiková, Z., & Fenyves, V. (2024). Artificial intelligence and machine learning in combating illegal financial operations: Bibliometric analysis. Human Technology, 20(2), 325–360. https://doi.org/10.14254/1795-6889.2024.20-2.5
Medina-Hernández, M., Kertész, J., & Fazekas, M. (2026). Learning from sanctioned government suppliers: A machine learning and network science approach to detecting fraud and corruption in Mexico. Scientific Reports, 16, 22382. https://doi.org/10.1038/s41598-026-48873-w
Monsalvo, A. E., Fletscher, L. A., Restrepo Carmona, J. A., & Villamil, R. M. (2025). Fiscal management and artificial intelligence as strategies to combat corruption in Colombia. Information, 16(11), 998. https://doi.org/10.3390/info16110998
Mousavian, S., & Miah, S. J. (2025). Review of artificial intelligence-based applications for money laundering detection. Intelligent Systems with Applications, 27, 200572. https://doi.org/10.1016/j.iswa.2025.200572
Muñoz-Cancino, R., & Ríos, S. A. (2025). Data-driven transparency: Machine learning and social network analysis for corruption detection in public procurement. Procedia Computer Science, 270, 1788–1795. https://doi.org/10.1016/j.procs.2025.09.299
Nai, R., Sulis, E., Meo, R., & Pensa, R. G. (2022). Public procurement fraud detection and artificial intelligence techniques: A literature review. Companion Proceedings of the 23rd International Conference on Knowledge Engineering and Knowledge Management (EKAW), 1–13. https://doi.org/10.48550/arXiv.2209.08883
Nemati, B., & Tavakkoli-Moghaddam, R. (2025). The impact of blockchain technology and artificial intelligence on accounting and auditing: Challenges and opportunities. American Journal of Economic and Management, 4(12), 1–15. https://doi.org/10.58631/ajemb.v4i12.396
Ngoc, L. H. (2026). The role of governance in the relationship between corruption and sustainable development in developing countries. Discover Sustainability, 7, 222. https://doi.org/10.1007/s43621-026-02586-2
Nguyen Thanh, C., Huy, T. P., Pham Hong, T., & Bui Nguyen Quoc, A. (2025). Credit risk prediction with corruption perception index: Machine learning approaches. Cogent Business & Management, 12(1), 2461731. https://doi.org/10.1080/23311975.2025.2461731
Odilla, F. (2024). Unfairness in AI anti-corruption tools: Main drivers and mitigation strategies. Minds and Machines, 34(3), 389–412. https://doi.org/10.1007/s11023-024-09688-8
Onamah, O. G. (2026). Artificial intelligence and corruption reduction in Nigeria: A study of the EFCC (2021–2026). International Journal of Research and Innovation in Applied Science, 11(6), 1–10. https://doi.org/10.51584/IJRIAS.2026.11060036
Peppes, N., Alexakis, T., Daskalakis, E., & Adamopoulou, E. (2025). Privacy-preserving federated learning for corruption risk detection in public procurement. Electronics, 15(17), 3890. https://doi.org/10.3390/electronics15173890
Peppes, N., Alexakis, T., Daskalakis, E., & Adamopoulou, E. (2026). AI-driven corruption risk indicator detection: A comparative evaluation of transformer-based NLP models in unstructured procurement data. Information, 17(4), 329. https://doi.org/10.3390/info17040329
Prasetiyo, Y. (2026). Artificial intelligence in auditing: A critical and comparative synthesis. Akuntansi: Jurnal Riset Ilmu Akuntansi, 5(1), 206–221. https://doi.org/10.55606/akuntansi.v5i1.3239
Rabuzin, K., & Modrušan, M. (2020). Prediction of public procurement corruption indices using machine learning methods. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 333–340). SciTePress. https://doi.org/10.5220/0008353603330340
Radanliev, P., et al. (2025). AI ethics: Integrating transparency, fairness, and privacy in public sector algorithms. Applied Artificial Intelligence, 39(1), 2463722. https://doi.org/10.1080/08839514.2025.2463722
Ramos, S., & Ellul, J. (2024). Blockchain for artificial intelligence (AI): Enhancing compliance with the EU AI Act through distributed ledger technology. A cybersecurity perspective. Discover Internet of Things, 4, 15. https://doi.org/10.1365/s43439-023-00107-9
Ranta, M., Järvenpää, M., & Teittinen, H. (2023). Machine learning in management accounting research: Literature review and pathways for the future. European Accounting Review, 32(3), 607–636. https://doi.org/10.1080/09638180.2022.2137221
Rodríguez Valencia, L., Corrales-Paredes, A., & others. (2025). A systematic review of artificial intelligence applied to compliance: Fraud detection in cryptocurrency transactions. Journal of Risk and Financial Management, 18(11), 612. https://doi.org/10.3390/jrfm18110612
Rosemary, A., & others. (2021). Intelligent fraud detection frameworks for next-generation financial systems. International Journal of Financial Data Science, 1(1), 1–32. https://doi.org/10.34218/IJFDS_01_01_001
Sampaio, I., Bernardini, F., & Viterbo, J. (2024). Enhancing transparency through explainable artificial intelligence: An exploratory analysis on a collusion and corruption scenario in digital government. In Proceedings of the 17th International Conference on Theory and Practice of Electronic Governance (pp. 1–10). Association for Computing Machinery. https://doi.org/10.1145/3657054.3657274
Schmid, M., & Willems, J. (2026). AI for integrity: Predicting public servants' susceptibility to corruption via supervised machine learning. Government Information Quarterly, 43(3), 102158. https://doi.org/10.1016/j.giq.2026.102158
Schneider dos Santos, E., Machado dos Santos, M., Castro, M., & Carvalho, J. T. (2025). Detection of fraud in public procurement using data-driven methods: A systematic mapping study. EPJ Data Science, 14, 52. https://doi.org/10.1140/epjds/s13688-025-00569-3
Vafaee Najar, A., et al. (2025). A global scoping review on the patterns of medical fraud and abuse: Integrating data-driven detection, prevention, and legal responses. Archives of Public Health, 83, 38. https://doi.org/10.1186/s13690-025-01512-8
Wachs, J., Fazekas, M., & Kertész, J. (2021). Corruption risk in contracting markets: A network science perspective. International Journal of Data Science and Analytics, 12(1), 45–60. https://doi.org/10.1007/s41060-019-00204-1
Waxenecker, H., & Prell, C. (2024). Corruption dynamics in public procurement: A longitudinal network analysis of local construction contracts in Guatemala. Social Networks, 79, 154–167. https://doi.org/10.1016/j.socnet.2024.07.001
Xiao, Q., et al. (2025). Automated detection of corruption reports in text via deep learning. Scientific Reports, 15, 20330. https://doi.org/10.1038/s41598-025-20330-0
Zerilli, J., Bhatt, U., & Weller, A. (2022). How transparency modulates trust in artificial intelligence. Patterns, 3(4), 100455. https://doi.org/10.1016/j.patter.2022.100455
Zhu, X., & Wang, Y. (2026). Public administration with, of, and through AI: Toward a new paradigm of digital governance and anti-corruption. Public Management Review, 28(1), 1–22. https://doi.org/10.1080/23812346.2025.2578589
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Shamsiddin Khursanov, Javlonbek Omonboev
This work is licensed under a Creative Commons Attribution 4.0 International License.
