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A Hybrid Sampling and Distribution Refinement Method for Reducing Behavioral Overlap

In: Neural Network-Based Deep Learning for Online Payment Fraud Detection

Author

Listed:
  • Yu Xie

    (Shanghai Maritime University, College of Information Engineering)

  • Yue Tian

    (Shanghai Normal University, Department of Computer Science and Technology)

  • Jiamin Yao

    (Shanghai Maritime University, College of Information Engineering)

  • Guanjun Liu

    (Tongji University, Department of Computer Science)

Abstract

Extending the generative data augmentation method proposed in Chap. 3, this chapter addresses the challenge posed by the substantial overlap between fraudulent and legitimate samples. This chapter proposes a GAN-based Hybrid Sampling (GANHS) framework [33], which integrates a Behavior-Boundary Aware Generative Adversarial Network (BBAGAN) for data augmentation with an Adaptive Neighborhood Cleaning Strategy (ANCS). This approach simultaneously generates high-quality fraudulent samples while eliminating ambiguous legitimate samples. It is particularly effective in detecting fraudulent activities characterized by weak discriminatory signals and strong concealment, such as micro-payment abuse and behavioral-camouflage transactions [14].

Suggested Citation

  • Yu Xie & Yue Tian & Jiamin Yao & Guanjun Liu, 2026. "A Hybrid Sampling and Distribution Refinement Method for Reducing Behavioral Overlap," Springer Books, in: Neural Network-Based Deep Learning for Online Payment Fraud Detection, chapter 4, pages 53-76, Springer.
    • Handle: RePEc:spr:sprchp:978-981-95-8513-7_4
    DOI: 10.1007/978-981-95-8513-7_4
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