Financial fraud detection in transaction networks involves modeling sparse anomalies, dynamic patterns, and severe class imbalance in the presence of temporal drift in the data. In real-world transaction systems, a suspicious transaction is rarely isolated: rather, legitimate and suspicious transactions are often connected through accounts, intermediaries or through temporal transaction sequences. Attribute-based or randomly partitioned learning pipelines are therefore insufficient to detect relationally structured fraud. STC-MixHop, a graph-based framework combining spatial multi-resolution propagation with lightweight temporal consistency modeling for anomaly and fraud detection in dynamic transaction networks. It integrates three components: a MixHop-inspired multi-scale neighborhood diffusion encoder a multi-scale neighborhood diffusion MixHop-based encoder for learning structural patterns; a spatial-temporal attention module coupling current and preceding graph snapshots to stabilize representations; and a temporally informed self-supervised pretraining strategy exploiting unlabeled transaction interactions to improve representation quality. We evaluate the framework primarily on the PaySim dataset under strict chronological splits, supplementing the analysis with Porto Seguro and FEMA data to probe cross-domain component behavior. Results show that STC-MixHop is competitive among graph methods and achieves strong screening-oriented recall under highly imbalanced conditions. The experiments also reveal an important boundary condition: when node attributes are highly informative, tabular baselines remain difficult to outperform. Graph structure contributes most clearly where hidden relational dependencies are operationally important. These findings support a stability-focused view of graph learning for financial fraud detection.

翻译：交易网络中的金融欺诈检测涉及在数据存在时间漂移的情况下对稀疏异常、动态模式和严重类别不平衡进行建模。在实际交易系统中，可疑交易很少是孤立的：合法交易与可疑交易往往通过账户、中介机构或时间交易序列相互关联。因此，基于属性或随机划分的学习流程不足以检测具有关系结构的欺诈行为。STC-MixHop是一种基于图的框架，结合了空间多分辨率传播与轻量级时间一致性建模，用于动态交易网络中的异常与欺诈检测。该框架整合了三个组件：一个受MixHop启发的多尺度邻域扩散编码器，用于学习结构模式；一个时空注意力模块，耦合当前与先前图快照以稳定表示；以及一种时间感知的自监督预训练策略，利用未标记的交易交互提升表示质量。我们主要在PaySim数据集上按严格时间顺序划分进行评估，并辅以Porto Seguro和FEMA数据进行跨领域组件行为分析。结果表明，STC-MixHop在图学习方法中具有竞争力，并在高度不平衡条件下实现了面向筛查的强召回率。实验还揭示了一个重要的边界条件：当节点属性信息高度丰富时，表格基线方法仍难以被超越。图结构在隐藏关系依赖具有实际重要性的场景中贡献最为显著。这些发现支持了以稳定性为中心的图学习视角在金融欺诈检测中的应用。