网络金融传染：建模AI供应商受损通过银行系统的传播
Cyber-Financial Contagion: Modeling the Propagation of an AI Vendor Compromise Through the Banking System
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中文总结 AI 辅助
本文研究AI供应商受损如何通过银行系统传播，提出CFC-Prop模型和CFC-GNN预警模型，在合成数据上验证了重尾损失分布，为监管者提供定量工具。
中文摘要 AI 辅助
银行系统现在依赖一小部分共享的人工智能供应商进行欺诈筛查、信用决策、反洗钱分诊、客户分析和内部决策支持。本文研究这些供应商之一的内部受损如何沿着运营、信息和金融联系的链条传播，直到引发从外部看起来像经典银行危机的损失。我们构建了一个四层异构网络，将AI供应商、金融机构、银行间敞口和客户账户耦合在一起，并提出了CFC-Prop，一种在该网络上运行的随机流行病与清算模型。在一个包含60家供应商、220家银行、约2500条供应商-银行服务边和1400条银行间敞口的合成数据集上，CFC-Prop重现了重尾损失分布和对补丁延迟的急剧依赖性，这与先前的网络金融证据一致。我们还训练了一个早期预警模型CFC-GNN，该模型利用供应商侧事件遥测和图结构，在影响发生前标记高级联风险供应商。在四个基线上，所提出的模型达到了AUROC 0.82和AUPRC 0.60，同时校准误差有界。我们发布了完整的代码、合成数据和可复现脚本。结果表明，AI供应商之间的网络集中度是一个一阶金融稳定问题，并为监管者提供了一个具体的定量工具来推理这一问题。
英文摘要
The banking system now depends on a small set of shared artificial intelligence vendors for fraud screening, credit decisioning, anti-money-laundering triage, customer analytics, and internal decision support. This paper studies how a compromise inside one of those vendors can propagate along a chain of operational, informational, and financial linkages until it triggers losses that look, from the outside, like a classical banking crisis. We build a four-layer heterogeneous network that couples AI vendors, financial institutions, interbank exposures, and customer accounts, and we propose CFC-Prop, a stochastic epidemic-and-clearing model that runs on that network. On a synthetic dataset with 60 vendors, 220 banks, roughly 2,500 vendor-bank service edges, and 1,400 interbank exposures, CFC-Prop reproduces the heavy-tailed loss distributions and the sharp dependence on patch latency that are consistent with prior cyber-financial evidence. We also train an early-warning model, CFC-GNN, that uses vendor-side incident telemetry and graph structure to flag high-cascade-risk vendors before impact. Across four baselines the proposed model reaches AUROC 0.82 and AUPRC 0.60 while keeping calibration errors bounded. We release the full code, synthetic data, and reproducible scripts. The results argue that cyber concentration among AI vendors is a first-order financial-stability problem and give supervisors a concrete quantitative tool for reasoning about it.
发表机构
- NICE Actimize
机构由 AI 辅助整理，请以论文原文为准。
