A Hybrid Sampling and Distribution Refinement Method for Reducing Behavioral Overlap

Yu Xie (), Yue Tian, Jiamin Yao () and Guanjun Liu ()
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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

Chapter 4 in Neural Network-Based Deep Learning for Online Payment Fraud Detection, 2026, pp 53-76 from Springer

Abstract: 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].

Date: 2026
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DOI: 10.1007/978-981-95-8513-7_4

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