具有边界意识的双重歧视者生成对抗网络,用于增加金融交易中的数据,检测欺诈行为
Honghao Zhu1, Zhanchao Wang2, Yu Xie2
1School of Computer and Information Engineering, Bengbu University, Bengbu, China.
PloS one
|February 20, 2026
概括
由于数据不平衡,金融交易欺诈检测 (FTFD) 面临着挑战. 一个新的边界意识的双歧视生成对抗网络 (BADGAN) 通过在决策边界附近生成现实的合成欺诈数据来改善欺诈检测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 数字支付的增长增加了金融交易欺诈检测 (FTFD) 的复杂性.
- 极端的阶级不平衡,很少有欺诈案例,阻碍了FTFD模型的准确性.
- 现有的数据增强方法与异常的欺诈模式和隐策略作斗争.
研究的目的:
- 为解决金融交易欺诈检测 (FTFD) 中的类不平衡问题.
- 提高FTFD模型准确学习和检测欺诈模式的能力.
- 提高合成欺诈数据生成的质量,以改善模型培训.
主要方法:
- 提出了一个边界意识的双重歧视者生成对抗网络 (BADGAN).
- 集成了一个边界样本分类器和双约束机制,使用远程对抗式学习.
- 使得发电机能够产生合成样本,坚持真正的欺诈分布,并保持与决策边界的距离.
主要成果:
- 巴德甘有效地产生高质量的近分类边界的合成欺诈样本.
- 边界意识方法优化了样本质量,改善了下游分类器的性能.
- 在真实世界和公共数据集上的实验证实了BADGAN在同行方法上的优越性.
结论:
- 巴德甘成功地减轻了FTFD中的阶级不平衡.
- 拟议的方法提高了欺诈检测模型的准确性和稳定性.
- 巴德甘为改善金融交易欺诈检测系统提供了一个有前途的解决方案.
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