为秒级反洗钱设计实时图神经网络系统，提升检测精度并揭示模型转换风险
KONTOGRAPH: Verified Point-in-Time Feature Consistency and Amortised Explanation for Real-Time Anti-Money Laundering under a 200 ms Decision Budget
- 用带节点记忆的时序图网络替代传统表格模型，显著提升检测性能
- 发现模型转ONNX后决策变化0.26%，警报量增12%，因浮点累积误差突破阈值
- 强调模型部署格式变更需像模型更新一样验证，否则解释结果可能失效
欧盟新规要求欧元贷记转账在十秒内完成，取消了传统反洗钱分析的夜间批处理窗口和可恢复的结算延迟，迫使检测、解释与决策必须在单数秒内完成。本文提出KONTOGRAPH，一个在自设200毫秒99分位响应时间预算下构建的端到端反洗钱系统。基于156万条注入典型洗钱模式且标签故意不全的模拟支付数据进行实证研究。关键发现：第一，采用带节点记忆的时序图网络，将PR-AUC从0.0053提升至0.1717，相比梯度提升树基线提高0.166（95%置信区间[0.105, 0.241]）；仅引入节点记忆即使得分翻倍以上。第二，通过属性测试对特征仅表达一次并编译至三个执行后端，发现三个时序一致性漏洞，代码审查未察觉，若不修正将虚增性能表现。第三，将部署的树集成模型导出为ONNX后，平均得分仅下降7.4×10⁻⁸，但导致0.26%的决策改变，警报量上升12%，根源在于32位累加误差跨过了成本最优阈值3.98×10⁻⁴。我们主张，服务格式转换应视作模型变更，直至经验证；同时指出，当候选邻域较小时，子图解释器的保真度指标可能失效——本文完整报告该零结果。
原文摘要 · Abstract (English)
Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This removes both the overnight batch window in which anti-money-laundering (AML) analytics traditionally ran and the settlement delay that made recovery possible, forcing detection, explanation and decision inside a single-digit-second envelope. We present KONTOGRAPH, an end-to-end AML pipeline for the SEPA Instant rail built under a self-imposed 200 ms 99th-percentile budget, and report an empirical study on 1,562,860 simulated payments with injected typologies and deliberately incomplete labels. Three findings are of interest beyond the system itself. First, a temporal graph network with per-node memory improves PR-AUC over a gradient-boosted tabular baseline from 0.0053 to 0.1717, a paired day-blocked bootstrap difference of +0.166 with 95% CI [0.105, 0.241]; per-node memory alone more than doubles the score. Second, expressing each feature once and compiling it to three execution backends, with equivalence enforced by property-based tests that perturb the future, surfaced three point-in-time violations that code review had passed--each of which would have inflated reported performance. Third, and most consequential for practice, exporting the deployed tree ensemble to ONNX changed only $7.4 \times 10^{-8}$ in mean score yet altered 0.26% of decisions and inflated the alert volume by 12%, because 32-bit accumulation perturbs scores across a cost-optimal threshold of $3.98 \times 10^{-4}$. We argue that a serving-format conversion must be treated as a model change until measured, and that fidelity metrics for subgraph explainers can be vacuous when candidate neighbourhoods are small--a null result we report in full.
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