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在机器学习下分析金融市场的欺诈检测
Jing Jin1,2, Yongqing Zhang3
1Medical Device College, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China. jinj@sumhs.edu.cn.
Scientific reports
|August 15, 2025
概括
本研究介绍了一种用于检测金融欺诈的堆叠组合学习模型. 与传统方法相比,先进的模型显著提高了准确性和稳定性,为金融机构提供了更好的风险控制.
科学领域:
- 金融技术 金融技术
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 金融欺诈对市场稳定构成重大威胁,并造成重大经济损失.
- 传统的欺诈检测方法与不断发展的欺诈策略作斗争,导致适应能力差,错误报警率高.
研究的目的:
- 使用堆叠合体学习开发一种先进的金融欺诈检测模型.
- 为了提高金融交易中的欺诈检测准确性和稳定性.
主要方法:
- 提出了一个集成多个基础学习者的堆叠组合模型:逻辑回归 (LR),决策树 (DT),随机森林 (RF),梯度增强树 (GBT),支持向量机 (SVM) 和神经网络 (NN).
- 实现了特征重要性权重和动态权重调整机制.
- 在超过一百万个真实金融交易数据点上验证了模型.
主要成果:
- 堆叠模型以95%的准确性,93%的回忆力和94%的F1得分实现了卓越的性能.
- 与传统单个模型相比,显示出明显更强的概括能力和稳定性.
- 在检测复杂和不断变化的金融欺诈模式方面表现优于传统方法.
结论:
- 堆叠组合模型是金融机构加强风险控制能力的强大工具.
- 尽管计算成本和延迟存在挑战,但它的准确性和稳定性提供了显著的优势.
- 未来的工作可以专注于通过在线学习和增量更新优化实时适应能力.
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