营销-AutoM3L:用于金融客户分析的域内自动化机器学习
Ye Tian1, Wenqian Shao2, Zihan Deng3
1Engage Element, Albany, NY, United States.
Frontiers in artificial intelligence
|February 12, 2026
概括
本研究引入了金融客户分析的自动化框架,增强了机器学习管道开发. 它提高了准确性,并大大缩短了营销相关特征的开发时间.
科学领域:
- 机器学习 机器学习
- 金融分析 金融分析
- 客户行为分析 客户行为分析
背景情况:
- 自动机器学习 (AutoML) 经常在金融客户分析的领域特定功能工程方面扎.
- 手动模型开发需要专门的专业知识,很难维持.
- 现有的系统缺乏用于营销相关的功能生成的能力,例如RFM指标或客户终身价值 (CLV).
研究的目的:
- 提出针对金融客户分析的端到端机器学习管道生成的自动化框架.
- 为了使业务利益相关者能够使用自然语言指令配置ML管道.
- 为了解决AutoML系统在域意识特征工程方面的差距.
主要方法:
- 该框架通过自然语言指令处理原始客户数据.
- 它自主执行数据模式识别,域意识的特征工程和模型选择.
- 关键营销指标,如RFM指标,CLV和参与度等,都会自动计算出来.
主要成果:
- 与现有的自动化ML技术相比,实现了1.4%至5.4%的精度改进.
- 将机器学习管道开发时间缩短了近七倍.
- 证明了框架能够自主生成与营销相关的功能.
结论:
- 拟议的框架有效地自动化了用于金融客户分析的机器学习管道的创建.
- 它提高了预测模型的准确性,并大大提高了开发效率.
- 自然语言接口使 ML 管道配置为没有深度技术专业知识的商业用户民主化.
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