用交易关联特征检测银行欺诈，让每条报告都有可追溯的数字依据。
SCAFDS: Edge-Feature Graph Attention for Interbank Fraud Detection with Attribution-Grounded SAR Generation
- 基于欺诈共现频率构建银行网络边特征，捕捉欺诈传播路径。
- 欺诈检测准确率提升15.9个百分点，报告可逐项追踪到具体数值结果。
- 适合金融监管与反欺诈系统研发，解决审计合规难题。
美国每日处理约130万笔银行间交易，现有研究未利用欺诈共现边特征建模欺诈传播。传统图神经网络依赖信用危机信号，不适用于欺诈溯源。现有系统无法生成可追溯至具体数值输出的可疑交易报告（SAR），导致监管审计缺口。本文提出SCAFDS系统，包含七个阶段的监控流程，解决五项技术缺陷：（1）基于FinCEN SAR记录的欺诈共现频率指标f(u,v,t)编码银行拓扑；（2）结合节点表征与欺诈共现边特征计算图注意力系数；（3）双线性融合欺诈共现风险，生成机构级系统性风险评分；（4）基于归因条件生成可追溯每项声明的SAR叙述；（5）根据监管裁决动态更新图注意力权重。在IEEE-CIS欺诈检测数据集（590,540笔交易）和模拟的FDIC对齐银行网络（8,103家机构，169,800条边）上，SCAFDS实现AUPRC=0.515±0.032、AUROC=0.802±0.018，较GraphSAGE-AML提升15.9pp和13.7pp。部分验证基于FDIC执法记录（n=4,279），模型排名一致。已申请美国临时专利（64/061,083，2026年5月8日提交）。
原文摘要 · Abstract (English)
The U.S. financial system processes approximately 1.3 million interbank transactions daily, yet no system in the reviewed literature models fraud propagation across the interbank network using fraud co-occurrence edge features. Prior interbank GNN architectures model credit contagion using credit distress supervision signals, producing systems misaligned for fraud forensics. No existing system generates SAR narratives with per-assertion forensic traceability to specific numerical detection outputs, creating regulatory auditability gaps in FinCEN-submitted reports. This paper introduces SCAFDS (Systemic Contagion-Aware Fraud Detection System), a seven-stage integrated surveillance pipeline addressing five structural limitations of prior art: (1) fraud-specific interbank topology encoding using fraud co-occurrence frequency metrics f(u,v,t) derived from FinCEN SAR registry records; (2) edge-feature-informed graph attention where coefficients are computed from both node representations and fraud co-occurrence edge features; (3) bilinear fraud co-occurrence risk fusion producing institution-level systemic fraud risk scores; (4) attribution-conditioned SAR narrative generation with per-assertion significance thresholds ensuring each FinCEN SAR assertion is traceable to a specific numerical pipeline output; and (5) topology-aware adaptive forensic feedback updating graph attention weights from regulatory dispositions. Experiments on the IEEE-CIS Fraud Detection Dataset (590,540 transactions) and a synthetic FDIC-aligned interbank network (8,103 institutions, 169,800 edges) show SCAFDS achieves AUPRC=0.515+/-0.032 and AUROC=0.802+/-0.018, representing +15.9pp and +13.7pp improvements over GraphSAGE-AML. Partial validation on FDIC enforcement action records (n=4,279) confirms consistent model ranking. USPTO Provisional Patent Application No. 64/061,083, filed May 8, 2026.
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