arXiv AI August 18, 2026 By Zan Li, Rui Fan

Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing

arXiv:2510. 17088v3 Announce Type: replace-cross 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv Machine Learning

Jul 28

Extreme Volatility Warning under Label Scarcity via Multi-Source Anomaly Fusion

arXiv:2607. 23682v1 Announce Type: new Abstract: Early warning of extreme market volatility is central to financial risk management, but actionable events are rare, nonstationary, and often triggered by exogenous information shocks.

By Jin Qian, Zhangzhi Xiong, Mingrui Li, Zhen Liu

arXiv Machine Learning

2d ago

Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes

arXiv:2609.38424v1 Announce Type: new Abstract: Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD mode...

By Fred Xu, Thomas Markovich, Florence Regol, Yizhou Sun

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arXiv AI

Jul 17

CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.

By Rabimba Karanjai, Hemanth Madhavarao, Lei Xu, Weidong Shi

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arXiv AI

Aug 14

Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

arXiv:2608. 12441v1 Announce Type: cross Abstract: Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason.

By Iyad Assaad Nekka, Hamida Seba, Khaled Walid Hidouci, Karima Amrouche

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arXiv AI

Jun 26

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.

By Keyang Chen, Mingxuan Jiang, Yongsheng Zhao, Zeping Li, Zaiyuan Chen, Weiqi Luo, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Xihong Wu, Hongfeng Chai

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arXiv Machine Learning

Sep 15

GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection

arXiv:2609.15483v1 Announce Type: new Abstract: Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation dis...

By Zepeng Zhang, Fuad Khuri, Keivan Faghih Niresi, Olga Fink

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Related stories

Extreme Volatility Warning under Label Scarcity via Multi-Source Anomaly Fusion

Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes

CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection