Araştırma Makalesi

AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring

Cilt: 16 Sayı: 4 30 Aralık 2025
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AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring

Öz

Accurate tracking of project progress is crucial for timely delivery, cost control, and fraud prevention. Issues in progress reporting, whether due to real mistakes, employee inefficiencies, or internal threats, present considerable risks to major projects. This study aims to examine statistical and machine learning techniques to identify data inconsistencies, fraudulent reporting, and other anomalies in project tracking. Utilizing a dataset of 118 weekly snapshots, including genuine and tainted data, this research assesses the effectiveness of the interquartile range, isolation forest, and an ensemble approach in detecting anomalies. The results underscore the strengths and weaknesses of statistical and machine learning models while proposing an optimal detection framework for effective project management.

Anahtar Kelimeler

Kaynakça

  1. [1] Jin, S. (2024). Measuring complexity in mega construction projects: fuzzy comprehensive evaluation and grey relational analysis. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ecam-07-2024-0951
  2. [2] Zhao, X., Liu, Y., Lang, X., Liu, K., Yang, X., & Liu, L. (2024). Study on the Characteristics and Operational Mechanisms of Industry–University–Research Collaborative Innovation in Megaprojects: The Case from China. Systems. https://doi.org/10.3390/systems12120553
  3. [3] Xiang, P., Yang, Y., Yan, K., & Jin, L. (2024). Identification of Key Safety Risk Factors and Coupling Paths in Mega Construction Projects. Journal of Management in Engineering. https://doi.org/10.1061/jmenea.meeng-5926
  4. [4] Mostofi, F., Toğan, V., & Tokdemir, O. B. (2025). A cost estimation recommendation system for improved contingency management in construction projects. Neural Computing & Applications 37, 3521–3538. https://doi.org/10.1007/s00521-024-10740-y
  5. [5] Mostofi, F., Tokdemir, O. B., & Toğan, V. (2024). Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management. (2024). https://doi.org/10.1016/j.aei.2024.102606
  6. [6] Shyam, R., & Tiwari, S. (2025). A Comprehensive Review of Machine Learning and Multi-Criteria Decision Analysis in Construction Delay Management. International Research Journal on Advanced Science Hub. https://doi.org/10.47392/irjash.2025.002
  7. [7] Manu, B. A. (2024). Leveraging Artificial Intelligence for optimized project management and risk mitigation in construction industry. World Journal of Advanced Research and Reviews. https://doi.org/10.30574/wjarr.2024.24.3.4026
  8. [8] Zapata-Cortes, O., Serna, M. D. A., Zapata-Cortés, J. A., & Restrepo-Carmona, J. A. (2024). Machine Learning Models and Applications for Early Detection. Sensors. https://doi.org/10.3390/s24144678

Ayrıntılar

Birincil Dil

İngilizce

Konular

Uygulamalı Bilgi İşleme (Diğer), Planlama ve Karar Verme, Yapı İşletmesi

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

30 Aralık 2025

Gönderilme Tarihi

13 Mart 2025

Kabul Tarihi

29 Ekim 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 16 Sayı: 4

Kaynak Göster

APA
Sandıkkaya, M. T., & Tokdemir, O. B. (2025). AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 16(4), 1103-1111. https://doi.org/10.24012/dumf.1656802
AMA
1.Sandıkkaya MT, Tokdemir OB. AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring. DÜMF MD. 2025;16(4):1103-1111. doi:10.24012/dumf.1656802
Chicago
Sandıkkaya, Mehmet Tahir, ve Onur Behzat Tokdemir. 2025. “AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 16 (4): 1103-11. https://doi.org/10.24012/dumf.1656802.
EndNote
Sandıkkaya MT, Tokdemir OB (01 Aralık 2025) AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 16 4 1103–1111.
IEEE
[1]M. T. Sandıkkaya ve O. B. Tokdemir, “AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring”, DÜMF MD, c. 16, sy 4, ss. 1103–1111, Ara. 2025, doi: 10.24012/dumf.1656802.
ISNAD
Sandıkkaya, Mehmet Tahir - Tokdemir, Onur Behzat. “AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 16/4 (01 Aralık 2025): 1103-1111. https://doi.org/10.24012/dumf.1656802.
JAMA
1.Sandıkkaya MT, Tokdemir OB. AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring. DÜMF MD. 2025;16:1103–1111.
MLA
Sandıkkaya, Mehmet Tahir, ve Onur Behzat Tokdemir. “AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, c. 16, sy 4, Aralık 2025, ss. 1103-11, doi:10.24012/dumf.1656802.
Vancouver
1.Mehmet Tahir Sandıkkaya, Onur Behzat Tokdemir. AI-Driven Anomaly Detection for Fraud Prevention in Project Monitoring. DÜMF MD. 01 Aralık 2025;16(4):1103-11. doi:10.24012/dumf.1656802
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