Journals / Paradoks Ekonomi Sosyoloji ve Politika Dergisi / 2025 / Cilt: 21 Sayı: 1

INCREASING EFFICIENCY WITH ARTIFICIAL INTELLIGENCE APPLICATIONS IN FRAUD DETECTION AND PREVENTION PROCESSES IN ENTERPRISES

İŞLETMELERDE, HİLE TESPİTİ VE ÖNLENMESİ SÜREÇLERİNDE YAPAY ZEKÂ UYGULAMALARIYLA VERİMLİLİĞİN ARTIRILMASI

Mehmet Erkan(GEDİK ÜNİVERSİTESİ)0009-0007-9578-9292
İsmail Özdemir(GEDİK ÜNİVERSİTESİ, GEDİK MESLEK YÜKSEKOKULU)0009-0007-0438-9518
Ahmet Erkasap(İSTANBUL GEDİK ÜNİVERSİTESİ)0000-0002-6239-1700
Pages
56–77
Publication Date
2025-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Therefore, businesses need to use classical methods integrated with artificial intelligence technology to create an effective defence against fraud.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The role of artificial intelligence applications in the detection and prevention of financial fraud is becoming increasingly important.
📊
Key Findings & Data:In today's business world where traditional audit methods are insufficient to detect sophisticated fraud techniques, artificial intelligence technology stands out with capabilities such as detecting anomalies by analysing large data sets, predicting future fraudulent transactions with predictive analyses and real-time monitoring.
💡
Conclusion & Impact:Therefore, businesses need to use classical methods integrated with artificial intelligence technology to create an effective defence against fraud.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zeka · hile · hile denetimi

Abstract

The role of artificial intelligence applications in the detection and prevention of financial fraud is becoming increasingly important. In today's business world where traditional audit methods are insufficient to detect sophisticated fraud techniques, artificial intelligence technology stands out with capabilities such as detecting anomalies by analysing large data sets, predicting future fraudulent transactions with predictive analyses and real-time monitoring. These capabilities offer the advantages of reducing operational costs and minimising false positives. However, it should be noted that artificial intelligence faces challenges such as data quality, model explainability and regulatory requirements. Therefore, businesses need to use classical methods integrated with artificial intelligence technology to create an effective defence against fraud.

Özet

Finansal hilelerin tespit ve önlenmesinde yapay zekâ uygulamalarının rolü giderek önem kazanmaktadır. Geleneksel denetim yöntemlerinin sofistike hile tekniklerini tespit etmede yetersiz kaldığı günümüz iş dünyasında, yapay zekâ teknolojisi, büyük veri setlerini analiz ederek anormallikleri tespit etme, öngörücü analizlerle gelecekteki hileli işlemleri tahmin etme ve gerçek zamanlı izleme gibi yeteneklerle öne çıkmaktadır. Bu yetenekler, işletmelerde operasyonel maliyetleri azaltma ve yanlış pozitif sonuçları minimize etme avantajları sunmaktadır. Ancak, yapay zekânın veri kalitesi, model açıklanabilirliği ve düzenleyici gereklilikler gibi zorluklarla karşı karşıya kaldığı da unutulmamalıdır. Bu nedenle, işletmelerin hileye karşı etkin bir savunma oluşturmak için klasik yöntemleri yapay zekâ teknolojisi ile entegre bir şekilde kullanmaları gerekmektedir

🔖

Cite This Article

Erkan, M., Özdemir, İ., Erkasap, A. (2025). INCREASING EFFICIENCY WITH ARTIFICIAL INTELLIGENCE APPLICATIONS IN FRAUD DETECTION AND PREVENTION PROCESSES IN ENTERPRISES. Paradoks Ekonomi Sosyoloji ve Politika Dergisi, Cilt: 21 Sayı: 1, 56–77. https://ajindex.com/articles/1240745

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (29)

  1. Adhikari, P., Hamal, P., & Baidoo, F. (2024). Artificial intelligence in fraud detection: Revolutionizing financial security. International Journal of Science and Research Archive, 13(01), 1457–1472. https://doi.org/10.30574/ijsra.2024.13.1.1860
  2. Adeyelu, O., Ugochukwu, C., & Shonibare, M. (2024). The impact of artificial intelligence on accounting practices: Advancements, challenges, and opportunities. International Journal of Management & Entrepreneurship Research, 6, 1200–1210. https://doi.org/10.51594/ijmer.v6i4.1031
  3. Aziz, L., & Andriansyah, Y. (2023). The role of artificial intelligence in modern banking: An exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110–132. Retrieved from https://www.researchgate.net/publication/373489510_The_Role_Artificial_Intelligence_in_Modern_Banking_An_Exploration_of_AI-Driven_Approaches_for_Enhanced_Fraud_Prevention_Risk_Management_and_Regulatory_Compliance
  4. Benedek, B., & Balint, Z. N. (2023). Traditional versus AI-based fraud detection: Cost efficiency in the field of automobile insurance. Financial and Economic Review, 22(2), 77–98. https://doi.org/10.33893/fer.22.2.77
  5. Bhimani, A., Horngren, C., Foster, G., & Datar, S. (2012). Management and cost accounting (6th ed.). Financial Times Prentice Hall. Retrieved from https://opac.atmaluhur.ac.id/uploaded_files/temporary/DigitalCollection/YWRjNmRlZmFiZjgxMzZiMzUzZDM1M2I4MmQ3ZGNmZjNlM2U2YmI2MQ==.pdf
  6. Bi-Cun, X., Yaonan, W., Xiuwu, L., & Kaidong, W. (2023). Efficient fraud detection using deep boosting decision trees. arXiv preprint arXiv:2302.05918. https://doi.org/10.48550/arXiv.2302.05918
  7. Botond, B., & Zsolt, N. (2023). Traditional versus AI-based fraud detection: Cost efficiency in the field of automobile insurance. Financial and Economic Review, 22(2), 77–98. https://doi.org/10.33893/fer.22.2.77
  8. Buyuktepe, O., Catal, C., Kar, G. B., Yamine, M., Hans, & Gavai, A. (2023). Food fraud detection using explainable artificial intelligence. Expert Systems. https://doi.org/10.1111/exsy.13387
  9. Chen, Y., Zhao, C., Xu, Y., & Nie, C. (2025). Year-over-year developments in financial fraud detection via deep learning: A systematic literature review. arXiv preprint arXiv:2502.00201. https://arxiv.org/html/2502.00201v1
  10. Cagatay, C., Gorkem, K., Yamine, B., & Anand, G. (2023). Food fraud detection using explainable artificial intelligence. Expert Systems. https://doi.org/10.1111/exsy.13387
  11. Day, G. S., & Schoemaker, P. (2016). Adapting to fast-changing markets and technologies. California Management Review, 58(4), 59–77. https://doi.org/10.1525/cmr.2016.58.4.59
  12. Dileep, A. K., Amma, K. S., Gujja, S., Ganesh, D., & Hariharan, S. (2023). Financial fraud detection using deep learning techniques. In 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 1–6). https://doi.org/10.1109/ICDCECE57866.2023.10150467
  13. Farman, M., & Abbas, A. (2023). Artificial intelligence for fraud detection and prevention. Retrieved from https://www.researchgate.net/publication/375671860_Artificial_Intelligence_for_fraud_detection_and_prevention
  14. Formica AI. (2021, July 13). Yapay zeka ile dolandırıcılık tespitinde 4 önemli fayda. LinkedIn. Retrieved from https://www.linkedin.com/pulse/yapay-zeka-ile-doland%C4%B1r%C4%B1c%C4%B1l%C4%B1k-tespitinde-4-%C3%B6nemli-fayda-/?originalSubdomain=tr
  15. Gadimov, E., & Birihanu, E. (2025). Real-time suspicious detection framework for financial data streams. International Journal of Information Technology. https://doi.org/10.1007/s41870-025-02529-6
  16. Gaikwad, S. (2020). AI in fraud detection: Benefits, challenges and uses. GetApp UK.
  17. Ginni, A., & Ruchika, B. (2023). Detection and analysis of fraud phone calls using artificial intelligence. In REEDCON (pp. 592–595). https://doi.org/10.1109/REEDCON57544.2023.10150631
  18. Hassan, M., Aziz, L. A. R., & Andriansyah, Y. (2023). The role of artificial intelligence in modern banking: An exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110–132.
  19. Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., & Rodriguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130. https://doi.org/10.1057/s41599-024-03606-0
  20. Kaggwa, S., Eleogu, T., Okonkwo, F., Farayola, O., Uwaoma, P., & Akinoso, A. (2024). AI in decision making: Transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423–444. https://doi.org/10.51244/IJRSI.2023.1012032
  21. Longbing, C. (2021). AI in finance: Challenges, techniques and opportunities. arXiv preprint arXiv:2107.09051. https://doi.org/10.48550/arXiv.2107.09051
  22. Mienye, I. D., & Jere, N. (2024). Deep learning for credit card fraud detection: A review of algorithms, challenges, and solutions. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2024.3424170
  23. Ruhanen, S. (2023). Contemporary advancements in financial technology and adoption (Bachelor's thesis, University of Twente). Retrieved from https://essay.utwente.nl/95295/1/Ruhanen_BA_BMS.pdf
  24. Sukanya, A., & David, J. (2021, June 22). AI in fraud detection: Benefits, challenges and uses. GetApp UK.
  25. Tewari, N. (2023). Artificial intelligence in finance and industry: Opportunities and challenges. In Decision Strategies and Artificial Intelligence Navigating the Business Landscape. https://doi.org/10.59646/edbookc5/009
  26. Uwaoma, P. U., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Kaggwa, S., & Akinoso, A. (2024). AI's role in sustainable business practices and environmental management. International Journal of Research and Scientific Innovation, 10(12), 359–379.
  27. Xu, B., Wang, Y., Liao, X., & Wang, K. (2023). Efficient fraud detection using deep boosting decision trees. arXiv preprint arXiv:2302.05918. https://doi.org/10.48550/arXiv.2302.05918
  28. Yusuf, I., İlker, K., & Jale, S. (2023). Detection of fraudulent transactions using artificial neural networks and decision tree methods. Business and Management Studies: An International Journal, 11(2), 451–467. https://doi.org/10.15295/bmij.v11i2.2200
  29. Vuković, D. B., Dekpo-Adza, S., & Matović, S. (2025). AI integration in financial services: A systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 12, Article 562. https://doi.org/10.1057/s41599-025-04850-8