Bibliometric Analysis of the Use of Artificial Intelligence in the Detection of Political and Economic Corruption: Current Trends and Future Directions
Translator
Translator
Subject Areas : Bi-quarterly Journal of development economics and planning
Reza Rezaee
1
,
sima eskandari sabzi
2
*
1 -
2 - Assistant professor of Islamic Azad University, Miyaneh Branch, Miyaneh. Iran
Keywords: Artificial intelligence, economic corruption, political corruption, bibliometric analysis,
Abstract :
Despite the wide scope of the role of artificial intelligence in detecting political and economic corruption, the published literature has not evaluated the performance of scientific activity in this field. The aim of this research is to identify the trend and impact of the published literature on the use of artificial intelligence in detecting economic and political corruption. For this purpose, bibliometric data of 101 relevant documents between 2000 and 2025 were extracted from the Scopus database. VOSviewer version 1.6.20.0 software was used for analysis. Based on the findings, the areas of study can be considered in 7 clusters; Cluster 1 - Detecting fraud and irregularities in public procedures using open data; Cluster 2 - Machine learning and its role in monitoring public and administrative affairs; Cluster 3 - Crime, anti-corruption policies, and artificial intelligence; Cluster 4 - Data mining in prediction and analysis in public procurement processes; Cluster 5 - Corruption and accountability and transparency in government governance; Cluster 6: Artificial Intelligence in Contracts and Procedures and Cluster 7: Deep Learning Techniques in Corruption Analysis. This research, by using bibliometric analysis, has identified prominent articles and authors, as well as revealed interdisciplinary collaborations and emerging trends in this field
Abedzadeh, M., Mehrabadi, R., and Kamali Janfada, B. (2023). Artificial Intelligence as a Tool for Fighting Corruption and Promoting Transparency. National Conference on Promoting Transparency. Tehran. [In persain]
Abreu, W. M., & Gomes, R. C. (2022). Shackling the Leviathan: balancing state and society powers against corruption. Public Management Review, 24(8), 1182-1207.
Adam, I., & Fazekas, M. (2021). Are emerging technologies helping win the fight against corruption? A review of the state of evidence. Information Economics and Policy, 57, 100950.
Adobor, H., & Yawson, R. (2023). The promise of artificial intelligence in combating public corruption in the emerging economies: A conceptual framework. science and public policy, 50(3), 355-370.
Agostino, D., Bracci, E., & Steccolini, I. (2021). Accounting and accountability for the digital transformation of public services. Financial Accountability & Management, 38(2), 145-151.
Aivaz, K. A., Florea, I. O., & Munteanu, I. (2024). Economic Fraud and Associated Risks: An Integrated Bibliometric Analysis Approach. Risks, 12(5), 74.
Al-Hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 100402.
Ang, Y. Y. (2020). Unbundling corruption: Revisiting six questions on corruption. Global Perspectives, 1(1), 12036.
Ball, C. (2009). What is transparency?. Public integrity, 11(4), 293-308.
Bao, Y., Hilary, G., & Ke, B. (2022). Artificial intelligence and fraud detection. Innovative Technology at the Interface of Finance and Operations: Volume I, 223-247.
Batrancea, I., Batrancea, L., Maran Rathnaswamy, M., Tulai, H., Fatacean, G., & Rus, M. I. (2020). Greening the financial system in USA, Canada and Brazil: A panel data analysis. Mathematics, 8(12), 2217.
Caputo, F., Ligorio, L., & Venturelli, A. (2025). Framing research on corruption and public administration in management studies: research trends and future directions. Journal of Global Responsibility.
Caruso, S., Bruccoleri, M., Pietrosi, A., & Scaccianoce, A. (2023). Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations. Business process management journal, 29(4), 1227-1248.
Choi, D., & Lee, K. (2018). An artificial intelligence approach to financial fraud detection under IoT environment: A survey and implementation. Security and Communication Networks, 2018(1), 5483472.
del Rey‐Puech, P., Balabanova, D., & McKee, M. (2025). Artificial Intelligence and Corruption: Opportunities and Challenges in the Health Sector. The International journal of health planning and management.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296.
Ghasemipour, M (2025). The Role of Artificial Intelligence in the Transformation of the Judicial System: From Accelerating Trials to Fighting Corruption, Sixth International Conference and Seventh National Conference on Law and Political Science, Tehran. [In persain]
Gong, T., Wang, S., & Ren, J. (2015). Corruption in the eye of the beholder: Survey evidence from Mainland China and Hong Kong. International Public Management Journal, 18(3), 458-482.
Hafez, I. Y., Hafez, A. Y., Saleh, A., Abd El-Mageed, A. A., & Abohany, A. A. (2025). A systematic review of AI-enhanced techniques in credit card fraud detection. Journal of Big Data, 12(1), 6.
Hashim, H. A., Salleh, Z., Shuhaimi, I., & Ismail, N. A. N. (2020). The risk of financial fraud: a management perspective. Journal of Financial Crime, 27(4), 1143-1159.
Hoseinabadi, S. & Amini, H. (2021). Studying the role of Information and Communications Technology in reducing corruption. Journal of Evaluation Knowledge, 12(4), 43-56. [In persain]
Karimzadeh, M., Dizaji, M. & Nahidi, M. (2024). The effect of Iranian Islamic model of progress on inflation in terms of corruption control governance index (Markov switching method), Bi-quarterly journal of development economics and planning, 11 (1), 59-77. [persain]
Khalel, K., Nataliia, G., Leonid, P., Larysa, P., Bohdana, V., & Oleksandr, A. (2023). Anti-corruption management mechanisms and the construction of a security landscape in the financial sector of the EU economic system against the background of challenges to european integration: Implications for artificial intelligence technologies.
Khanalipour vajargah, S. (2022). The Problem of the Economic Corruption from the View of the Political-Security Concerns. Criminal Law Doctrines, 18(22), 35-58. 1594. [In persain]
Köbis, N., Starke, C., & Rahwan, I. (2022). The promise and perils of using artificial intelligence to fight corruption. Nature Machine Intelligence, 4(5), 418-424.
Lyra, M. S., Damásio, B., Pinheiro, F. L., & Bacao, F. (2022). Fraud, corruption, and collusion in public procurement activities, a systematic literature review on data-driven methods. Applied Network Science, 7(1), 83.
Menke, W., Gomes, R., & Xavier, F. (2024). Impacts of AI-based anti-corruption audits on risk aversion in decision-making: a case study of the Brazilian ALICE tool. Global Public Policy and Governance, 4(3), 273-286.
Nai, R., Meo, R., Morina, G., & Pasteris, P. (2023). Public tenders, complaints, machine learning and recommender systems: a case study in public administration. Computer Law & Security Review, 51, 105887.
Nai, R., Sulis, E., & Meo, R. (2022). Public procurement fraud detection and artificial intelligence techniques: a literature review. In Companion Proceedings of the 23rd International Conference on Knowledge Engineering and Knowledge Management (pp. 1-13). CEUR-WS.
Nicholls, C., Baracese, A., Maton, J., Scott, R. and Hatchard, J. (2024), Corruption and Misuse of Public Office, Oxford University Press, 29 August, doi:10.1093/oso/9780198907329.001.0001
Odilla, F. (2023). Bots against corruption: Exploring the benefits and limitations of AI-based anti-corruption technology. Crime, Law and Social Change, 80(4), 353-396.
Rabuzin, K., & Modrusan, N. (2019). Prediction of Public Procurement Corruption Indices using Machine Learning Methods. In KMIS (pp. 333-340).
Ralha, C. G., & Silva, C. V. S. (2012). A multi-agent data mining system for cartel detection in Brazilian government procurement. Expert Systems with Applications, 39(14), 11642-11656.
Rowshan, S. A. , yaqoubi, N. & momeni, A. (2021). Application of artificial intelligence in the public sector (meta-combination study). Iranian journal of management sciences, 16(61), 117-145. [In persain]
Shojaei, S.H., Zare, H. & Ebrahimi, M. (2024). Investigating the effect of using artificial intelligence in international trade, Bi-quarterly journal of development economics and planning, 11 (2), 46-64. [In Persian]
Taghizadeh, A. (2024). Explaining Economic and Political Corruption in the Islamic Republic of Iran, Eighth International Conference on Jurisprudence, Law, Advocacy and Social Sciences, Hamadan. [In persain]
Torabi, M. A. & Rajabi Farjad, H. (2025). Predictive Artificial Intelligence Model for Inspection to Detect Corruption and Risk, Supervision and Inspection, 18(68), 45-84. [In persain]
Torres Berru, Y., López Batista, V. F., Torres-Carrión, P., & Jimenez, M. G. (2020). Artificial intelligence techniques to detect and prevent corruption in procurement: a systematic literature review. In Applied Technologies: First International Conference, ICAT 2019, Quito, Ecuador, December 3–5, 2019, Proceedings, Part II 1 (pp. 254-268). Springer International Publishing.