تحلیل کتاب‌سنجی استفاده از هوش مصنوعی در کشف فساد سیاسی و اقتصادی: روندهای فعلی و جهت‌گیری‌های آینده
Translator
Translator
Translator
الموضوعات : دو فصلنامه علمی - تخصصی اقتصاد توسعه و برنامه ریزی
رضا رضایی
1
,
سیما اسکندری سبزی
2
1 - دانشجوی دکتری، گروه اقتصاد،واحد میانه،دانشگاه آزاد اسلامی، میانه، ایران
2 - استادیار دانشگاه آزاد واحد میانه، گروه اقتصاد
الکلمات المفتاحية: هوش مصنوعی, فساد اقتصادی, فساد سیاسی, تحلیل کتاب‌سنجی,
ملخص المقالة :
علیرغم دامنه گسترده نقش هوش مصنوعی در کشف فسادهای سیاسی و اقتصادی، ادبیات منتشر شده تاکنون، عملکرد فعالیت علمی این حوزه را ارزیابی نکرده است. هدف این تحقیق شناسایی روند و تأثیرگذاری ادبیات منتشر شده در زمینه استفاده از هوش مصنوعی در کشف فساد اقتصادی و سیاسی است. به این منظور داده‌های کتاب‌سنجی 101 سند مرتبط بین سال‌های 2000 تا 2025 از پایگاه داده اسکوپوس استخراج شده است. برای تجزیه و تحلیل از نرم‌افزار VOSviewer نسخه 1.6.20.0 استفاده شد. بر اساس یافته‌ها، حوزه‌های مورد مطالعه را می‌توان در 7 خوشه در نظر گرفت؛ خوشه اول- کشف تقلب و ناهنجاری در رویه‌های عمومی با استفاده از داده‌های باز؛ خوشه دوم- یادگیری ماشین و نقش آن در نظارت بر امور عمومی و اداری ؛ خوشه سوم- جرم، سیاست‌های ضدفساد، و هوش مصنوعی؛ خوشه چهارم- داده‌کاوی در پیش‌بینی و تحلیل در فرایندهای خرید عمومی؛ خوشه پنجم- فساد و پاسخگویی و شفافیت در حاکمیت دولتی؛ خوشه ششم- هوش مصنوعی در قراردادها و رویه‌ها و خوشه هفتم: تکنیک‌های یادگیری عمیق در تحلیل فساد. این تحقیق از طریق به‌کارگیری تحلیل کتاب‌سنجی، ضمن شناسایی مقالات و نویسندگان برجسته، ارتباطات همکاری بین رشته‌ای و روندهای نوظهور در این حوزه را نیز آشکار ساخته است.
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.
