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

Year 2025, Volume: 21 Issue: 1, 62 - 86

Mehmet Erkan , İsmail Özdemir , Ahmet Erkasap

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.

Keywords

Artificial Intelligence fraud fraud audit business efficiency

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Gaikwad, S. (2020). AI in fraud detection: Benefits, challenges and uses. GetApp UK.
  • 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
  • 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.
  • 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
  • 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
  • Longbing, C. (2021). AI in finance: Challenges, techniques and opportunities. arXiv preprint arXiv:2107.09051. https://doi.org/10.48550/arXiv.2107.09051
  • 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
  • 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
  • Sukanya, A., & David, J. (2021, June 22). AI in fraud detection: Benefits, challenges and uses. GetApp UK.
  • 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
  • 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.
  • 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
  • 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
  • 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

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

Year 2025, Volume: 21 Issue: 1, 62 - 86

Mehmet Erkan , İsmail Özdemir , Ahmet Erkasap

Abstract

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

Keywords

Yapay zeka hile hile denetimi işletme verimlilik.

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Gaikwad, S. (2020). AI in fraud detection: Benefits, challenges and uses. GetApp UK.
  • 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
  • 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.
  • 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
  • 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
  • Longbing, C. (2021). AI in finance: Challenges, techniques and opportunities. arXiv preprint arXiv:2107.09051. https://doi.org/10.48550/arXiv.2107.09051
  • 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
  • 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
  • Sukanya, A., & David, J. (2021, June 22). AI in fraud detection: Benefits, challenges and uses. GetApp UK.
  • 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
  • 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.
  • 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
  • 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
  • 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
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Organisational Planning and Management
Journal Section Articles
Authors

Mehmet Erkan 0009-0007-9578-9292

İsmail Özdemir 0009-0007-0438-9518

Ahmet Erkasap 0000-0002-6239-1700

Early Pub Date July 2, 2025
Publication Date
Submission Date December 22, 2024
Acceptance Date June 13, 2025
Published in Issue Year 2025 Volume: 21 Issue: 1

Cite

APA Erkan, M., Özdemir, İ., & Erkasap, A. (n.d.). İŞLETMELERDE, HİLE TESPİTİ VE ÖNLENMESİ SÜREÇLERİNDE YAPAY ZEKÂ UYGULAMALARIYLA VERİMLİLİĞİN ARTIRILMASI. Paradoks Ekonomi Sosyoloji Ve Politika Dergisi, 21(1), 62-86.