这篇论文用PaySim数据集测了套欺诈检测流程，发现图特征和AI调查智能体效果有限，智能体解释好看但决策反而更差，值得做风控的人看看。
该论文在PaySim数据集上测试了分层欺诈检测流程，结合梯度提升分类器、图结构特征、自编码器异常信号、TreeSHAP解释以及LLM调查智能体。研究发现，在修正数据集平衡性捷径后，图特征和异常信号仅在中间分数子集上提升欺诈排序能力。注入多账户欺诈团伙的实验中，图特征恢复了所有测试交易，而基线遗漏约四分之一。LLM调查智能体在60例平衡样本上准确率65.0%，低于阈值化基线71.7%，且其8次改判中有6次引入错误。基于分歧的升级规则正确标记了两次智能体错误。
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation
Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline on the PaySim dataset that combines a gradient-boosted classifier, graph-derived structural features, an autoencoder-based anomaly signal, TreeSHAP explanations, and a bounded LLM investigation agent applied to cases the classifier scores uncertainly. Before any model comparison, we identify and remove a simulator-specific balance shortcut that would otherwise inflate baseline performance. After this correction, neither the graph features nor the anomaly signal improves Average Precision on the full test set. Both, however, rank fraud better within the subset of cases receiving intermediate baseline scores. In a controlled experiment with injected multi-account fraud rings, engineered structural features recover all injected test transactions, while the tabular baseline misses roughly a quarter of them. The investigation agent underperforms direct thresholding of the classifier it relies on, reaching 65.0% accuracy against 71.7% on a balanced 60-case sample, despite having access to model explanations, graph context, and retrieved reference cases. Of the eight decisions the agent changed, six replaced correct classifier outputs with errors, and it produced a coherent written rationale in each case. An exploratory disagreement-based escalation rule flagged two of these agent errors for human review without flagging any correct decision. We conclude that each component of a layered fraud system contributes only under specific conditions, and that a plausible rationale from an investigation agent is not evidence of a better decision.