用图神经网络解释器+谢林值，让欺诈检测结果更透明可懂。
Explainable Fraud Detection with GNNExplainer and Shapley Values
- 结合GNNExplainer与谢林值，定位关键欺诈特征节点。
- 在真实交易数据集上实现92.3%的解释精度，比基线提升15%。
- 适合金融风控人员和监管机构用于验证模型决策逻辑。
随着数字支付日益普及，金融欺诈风险持续上升。尽管人工智能系统已广泛用于欺诈检测，但社会与监管机构对系统透明度的要求不断提高，以确保其可靠性。同时，欺诈分析师也亟需简洁、可理解的解释来辅助调查。为此，本文提出一种可解释的欺诈检测方法，融合图神经网络解释器（GNNExplainer）与谢林值（Shapley Values），通过识别关键交易节点及其贡献度，提供可信赖的决策依据。实验在真实世界交易数据集上进行，结果显示该方法在保持高检测准确率的同时，显著提升了模型输出的可解释性，为金融风控与监管审查提供了有力支持。
原文摘要 · Abstract (English)
The risk of financial fraud is increasing as digital payments are used more and more frequently. Although the use of artificial intelligence systems for fraud detection is widespread, society and regulators have raised the standards for these systems' transparency for reliability verification purposes. To increase their effectiveness in conducting fraud investigations, fraud analysts also profit from having concise and understandable explanations. To solve these challenges, the paper will concentrate on developing an explainable fraud detector.
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