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Evaluation of the effectiveness of ensemble classifiers and single neural network models for financial fraud detection
https://doi.org/10.34020/2073-6495-2026-2-010-028
Abstract
This study investigates the effectiveness of single and ensemble classifiers for financial fraud detection based on account-to-account money transfer data. We considered Recurrent Neural Networks, Convolutional Neural Networks, Autoencoders, as well as ensemble methods such as Random Forest, XGBoost, and stacking; simple voting was additionally applied to the neural network models. Experiments were conducted using a large-scale, real-world labeled dataset of transaction records. Classification performance was evaluated using Precision, Recall, F1-score, and ROC-AUC metrics. Computational efficiency was assessed based on training time, inference time, memory usage, and average CPU load. The experimental results demonstrated that the stacking ensemble method achieves the highest accuracy, although it is the most resource-intensive. Meanwhile, the accuracy of more efficient boosting and bagging methods is slightly lower but remains comparable to stacking.
About the Authors
A. I. Pestunov
Novosibirsk State University of Economics and Management
Russian Federation
Russian Federation
Pestunov Andrey I. - Candidate of Physical and Mathematical Sciences, Associate Professor, Head of the Information Technology Department
Novosibirsk
R. V. Samoylenko
Novosibirsk State University of Economics and Management
Russian Federation
Russian Federation
Samoylenko Roman V. - Postgraduate Student
Novosibirsk
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Review
For citations:
Pestunov A.I., Samoylenko R.V. Evaluation of the effectiveness of ensemble classifiers and single neural network models for financial fraud detection. Vestnik NSUEM. 2026;(2):10-28. (In Russ.) https://doi.org/10.34020/2073-6495-2026-2-010-028
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