用专家反馈提升金融欺诈检测，让模型更准更快
Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation
- 引入人类专家反馈，动态优化欺诈识别模型
- 图神经网络方法在反馈下准确率显著提升
- 新传播机制扩展反馈效果，适合风控团队使用
人机协同（HITL）反馈机制能显著提升机器学习模型性能，尤其在金融欺诈检测中——欺诈模式变化快、异常样本稀疏。即使少量领域专家（SME）提供的反馈，也能明显改善模型表现。本文在私有和公开数据集上验证了传统与先进方法的效果，结果表明HITL反馈普遍提升准确率，其中图基方法受益最大。我们提出一种新型反馈传播机制，将专家反馈扩展至全数据集，进一步提高检测精度。该方法有效应对欺诈模式演化、数据稀疏与模型可解释性挑战，增强模型鲁棒性并简化标注流程。
原文摘要 · Abstract (English)
Human-in-the-loop (HITL) feedback mechanisms can significantly enhance machine learning models, particularly in financial fraud detection, where fraud patterns change rapidly, and fraudulent nodes are sparse. Even small amounts of feedback from Subject Matter Experts (SMEs) can notably boost model performance. This paper examines the impact of HITL feedback on both traditional and advanced techniques using proprietary and publicly available datasets. Our results show that HITL feedback improves model accuracy, with graph-based techniques benefiting the most. We also introduce a novel feedback propagation method that extends feedback across the dataset, further enhancing detection accuracy. By leveraging human expertise, this approach addresses challenges related to evolving fraud patterns, data sparsity, and model interpretability, ultimately improving model robustness and streamlining the annotation process.
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