该论文提出可解释的金融网络异常检测方法，能自动识别异常类型并定位风险来源。
Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing
- 构建动态图结构，随市场变化自适应调整关联关系。
- 通过四个专家模型分解异常，路由权重揭示主导机制。
- 在美股数据上提前3.7天预警重大危机，性能优于现有方法33个百分点。
金融异常源于异质性机制——价格冲击、流动性冻结、传染扩散和动量反转——但现有检测器输出统一异常分数，无法揭示具体机制或风险集中点，阻碍精准应对：流动性冻结需市场做市支持，而信息不对称引发的价格波动则需熔断机制。三个核心挑战未解：(1) 静态图结构无法随市场状态变化调整；(2) 统一检测忽略异质异常特征；(3) 黑箱评分缺乏对异常驱动机制的可解释指引。本文提出一种自适应图学习框架，将可解释性内嵌于架构设计，而非事后分析。框架构建压力调制图，动态融合已知行业与地理关系及数据驱动相关性。异常由四类机制专用专家（价格冲击、流动性、系统性传染、动量反转）分解，其路由权重作为机制归因的可解释代理。分层市场压力指数将个体异常得分聚合为渐进式全市场警报。在100只美国股票（2017–2024）上的实验显示，该框架对六次重大市场压力事件平均提前3.7天预警，检测率较最强基线提升33个百分点（AUC 0.888，AP 0.626）。对硅谷银行倒闭（2023年3月）和日本套利交易清算（2024年8月）的案例研究证明，路由权重可自动区分局部行业危机与跨行业系统传播，无需监督标签。
原文摘要 · Abstract (English)
Financial anomalies arise from heterogeneous mechanisms - price shocks, liquidity freezes, contagion cascades, and momentum reversals - yet existing detectors produce uniform anomaly scores without revealing which mechanism is failing or where risks concentrate. This hinders targeted responses: liquidity freezes call for market-making support, whereas price shocks from information asymmetry call for circuit breakers. Three key challenges remain unresolved: (1) static graph structures cannot adapt when correlations shift across regimes; (2) uniform detectors overlook heterogeneous anomaly signatures; and (3) black-box scores provide no actionable guidance on which mechanism drives the anomaly. We address these challenges with an adaptive graph learning framework that embeds interpretability architecturally rather than post hoc. The framework constructs stress-modulated graphs that adaptively interpolate between known sector and geographic relationships and data-driven correlations as market conditions evolve. Anomalies are decomposed via four mechanism-specific experts - Price-Shock, Liquidity, Systemic-Contagion, and Momentum-Reversal - whose routing weights serve as interpretable proxies for mechanism attribution. A hierarchical Market Pressure Index aggregates entity-level anomaly scores into graduated market-wide alerts. On 100 U.S. equities (2017-2024), the framework detects all six major market stress events with a 3.7-day mean lead time, outperforming the strongest baselines by +33 percentage points in detection rate (AUC 0.888, AP 0.626). Case studies on the SVB collapse (March 2023) and Japan carry-trade unwind (August 2024) demonstrate that routing weights automatically distinguish localized sector-specific crises from systemic multi-sector propagation - without labeled supervision.
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