A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection

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Published: 2026-02-26
Keywords:
fraud detection, deep learning, ensemble learning, feature engineering

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Jin Zhang
Computer Science, Illinois Institute of Technology, IL, USA

Abstract

The exponential growth of digital payment platforms has introduced unprecedented security challenges in detecting fraudulent transactions. This study presents a comprehensive comparative evaluation of deep learning architectures and ensemble learning algorithms for online payment fraud detection. We systematically assess Long Short-Term Memory networks, Recurrent Neural Networks, logistic regression, and gradient boosting methods across detection accuracy, precision-recall trade-offs, and computational efficiency. Through rigorous experimentation on real-world transaction datasets, we evaluate two feature engineering strategies: user behavior-based features from RFM analysis and transaction amount patterns. Our analysis reveals that ensemble methods achieve superior F1-scores of 0.876, while LSTM architectures demonstrate enhanced capability in capturing temporal dependencies. The study establishes quantitative guidelines for algorithm selection based on dataset characteristics and operational constraints.

Article Details

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Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection. (2026). Journal of Science, Innovation & Social Impact, 2(1), 164-177. https://sagespress.com/index.php/JSISI/article/view/92

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