Detecting fraud activities in financial transactions using SMOTENN model
https://doi.org/10.17586/2226-1494-2025-25-3-466-474
Abstract
The financial industry plays an important role in national economic growth. Because of their critical function, banks have become prime targets for numerous financial crimes. Among these, fraudulent financial transactions are regarded as a severe issue in the financial industry. Conventional approaches are frequently criticized for being ineffective in dealing with fraud in finance; therefore, machine learning approaches have a potential answer to deal with this problem. The goal of this research is to introduce a novel SMOTENN model to establish early detection of cyber fraudulent activities in financial transactions accurately. Two methods are used in this study: first, the Neural Network algorithm is applied to a dataset that contains unbalanced classes; second, the dataset is balanced using the SMOTE (Synthetic Minority Over-sampling Technique) algorithm first, followed by the Neural Network algorithm which we refer to as SMOTENN. The both models are assessed using evaluation metrics of Area Under the Curve, F1-score, precision, recall, specificity, accuracy, and processing time. The comparative analysis shows that the performance of the new SMOTENN model with a balanced dataset is significantly better than that of the neural network approach with an imbalanced dataset, implying that the new SMOTENN model is effective in detecting fraud activities in financial transactions.
Supporting agencies: The author would like to thank supports from Politeknik Negeri Ujung Pandang, Indonesia and Universitas Muhammadiyah Makassar, Indonesia.
About the Authors
I. Syamsuddin
Politeknik Negeri Ujung Pandang
Indonesia
Indonesia
Irfan Syamsuddin — Professor
Makassar, 90245
sc 2592752650
S. Omsa
Politeknik Negeri Ujung Pandang
Indonesia
Indonesia
Sirajuddin Omsa — Associate Professor
Makassar, 90245
A. Rustam
Universitas Muhammadiyah Makassar
Indonesia
Indonesia
Andi Rustam — Associate Professor
Makassar, 90221
D. Hasan
Politeknik Negeri Ujung Pandang
Indonesia
Indonesia
Dahsan Hasan — Associate Professor
Makassar, 90245
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Review
For citations:
Syamsuddin I., Omsa S., Rustam A., Hasan D. Detecting fraud activities in financial transactions using SMOTENN model. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2025;25(3):466-474. https://doi.org/10.17586/2226-1494-2025-25-3-466-474
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