Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)

arXiv:2510.17088 [cs.LG]

(or arXiv:2510.17088v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2510.17088 Focus to learn more arXiv-issued DOI via DataCite

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