Abstract

This study proposes an adaptive edge–cloud collaborative framework for real-time fraud detection in network lending terminals, addressing the challenges posed by extreme class imbalance and latency constraints. Using the publicly available Credit Card Fraud Detection dataset, nine machine learning algorithms were evaluated in combination with four oversampling techniques (SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN). The results demonstrate that ensemble tree-based methods—particularly Random Forest, LightGBM, and XGBoost combined with SMOTE—achieve the best trade-off between accuracy, fraud recall, and false-positive rate. The framework integrates edge-level pre-scoring with cloud-based model refinement, reducing end-to-end latency by up to 35% while preserving detection accuracy. These findings underscore the potential of hierarchical, cost-sensitive learning pipelines to strengthen financial transaction security in real-time environments.

Keywords

Fraud DetectionEdge-Cloud CollaborationClass ImbalanceMachine Learning Algorithms

Publication

Accepted 2025 December 26 ; Published 2026 January 5

Journal of Computer Technology and Applied Mathematics, 2026, 3(1), 3007-4126

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Metadata

DOI

10.70393/6a6374616d.333630

ARK

ark:/40704/JCTAM.v3n1a07

Pages

55–62

References

15

Disciplines

Network Technology

Subjects

Wireless Networks

Cite This Article

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Funding

Not applicable.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Informed Consent Statement

Not applicable.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Not applicable.

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Publisher's Note

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Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
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