ROLE OF MACHINE LEARNING IN INTERNAL FRAUD DETECTION

Authors : Teoman Samet Temuçin

Pages : 151-172

View : 111 | Download : 99

Publication Date : 2025-05-19

Article Type : Research Paper

Abstract :One of the operational risks faced by the entities is internal fraud. In addition to preventive proactive controls, the existence of reactive continuous risk monitoring to quickly detect them is of great importance. In this paper written as the continuation of our first study (Temuçin et al., 2021), we take one step further the big data transformation story of Garanti BBVA - one of the most important banks of Türkiye - regarding detection of internal frauds. We will explain how machine learning was integrated into the “rule-based” approach with proven success thanks to the accumulated data pool. The hybrid model supported with machine learning has established a more effective continuous monitoring approach and also ensured the maintenance of sustainable critical metrics such as high internal fraud detection ratio and low Bank loss. We hope that our current methodology with proven success in detection of internal frauds and with its recent integration with machine learning will be an inspiration for the sector.
Keywords : İç suiistimal, Büyük veri, Makine öğrenmesi, XGBoost

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