Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
arXiv:2608.07471v1 Announce Type: new Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study`s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0,946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.
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[Submitted on 23 Apr 2026]
Title:Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
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Abstract:This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study`s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0,946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.
Comments: 22 pages, 6 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
Cite as: arXiv:2608.07471 [cs.LG]
(or arXiv:2608.07471v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.07471
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.3390/bdcc7020093
DOI(s) linking to related resources
Submission history
From: Solomia Fedushko [view email] [v1] Thu, 23 Apr 2026 11:14:49 UTC (515 KB)
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