Real-time Detection of Abnormal Financial Transactions Using Generative Adversarial Networks: An Enterprise Application

Authors

  • Shuaiqi Zheng Illinois Institute of Technology
  • Maoxi Li Fordham University
  • Wenyu Bi University of Southern California
  • Yining Zhang University of Southern California

DOI:

https://doi.org/10.70393/6a69656173.323431

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n6a10

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

36

Keywords:

Financial Fraud Detection, Generative Adversarial Networks, Real-time Processing, Enterprise Security

Abstract

This paper presents a novel real-time financial fraud detection framework utilizing Generative Adversarial Networks (GANs) for enterprise applications. The proposed system addresses critical challenges in fraud detection, including class imbalance, real-time processing requirements, and enterprise scalability. Implementing a sophisticated multi-layered architecture, the system integrates advanced preprocessing techniques with an optimized GAN model explicitly designed for fraud pattern recognition. The framework incorporates parallel processing capabilities and adaptive batch processing mechanisms to maintain high throughput while ensuring sub-second latency. The experimental evaluation uses a subset of the European Credit Card Transaction dataset, comprising 50,000 transactions with a balanced representation achieved through strategic sampling and SMOTE technique. The proposed model achieves 97.8% accuracy, 96.5% precision, and 95.8% recall, demonstrating competitive performance compared to traditional machine learning approaches. Real-time performance analysis shows consistent sub-100ms latency while maintaining robust performance under varying load conditions. The system demonstrates linear scalability up to 32 nodes, with high availability and failover capabilities. The comprehensive assessment validates the framework's effectiveness in enterprise environments, providing practical solutions for financial institutions facing evolving fraud challenges. This research contributes to the advancement of financial security technology through the innovative application of adversarial learning in fraud detection.

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Author Biographies

Shuaiqi Zheng, Illinois Institute of Technology

Data Analytics, Illinois Institute of Technology, IL, USA.

Maoxi Li, Fordham University

Business Analytics, Fordham University, NY, USA.

Wenyu Bi, University of Southern California

Science in Applied Economics and Econometrics, University of Southern California, CA, USA.

Yining Zhang, University of Southern California

Applied Data Science, University of Southern California, CA, USA.

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Published

2024-12-01

How to Cite

[1]
S. Zheng, M. Li, W. Bi, and Y. Zhang, “Real-time Detection of Abnormal Financial Transactions Using Generative Adversarial Networks: An Enterprise Application”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 6, pp. 86–96, Dec. 2024.

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