Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing

• URL: http://arxiv.org/abs/2510.17088v1
• Date: Mon, 20 Oct 2025 01:30:41 GMT
• Title: Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing
• Authors: Zan Li, Rui Fan,
• Abstract summary: Existing detectors treat all anomalies uniformly, producing scores without revealing which mechanism is failing.<n>We address these via adaptive graph learning with specialized expert networks that provide built-in interpretability.<n>We achieve 92.3% detection of 13 major events with 3.8-day lead time, outperforming best baseline by 30.8pp.
• Score: 9.3237091894548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial anomalies exhibit heterogeneous mechanisms (price shocks, liquidity freezes, contagion cascades, regime shifts), but existing detectors treat all anomalies uniformly, producing scalar scores without revealing which mechanism is failing, where risks concentrate, or how to intervene. This opacity prevents targeted regulatory responses. Three unsolved challenges persist: (1) static graph structures cannot adapt when market correlations shift during regime changes; (2) uniform detection mechanisms miss type-specific signatures across multiple temporal scales while failing to integrate individual behaviors with network contagion; (3) black-box outputs provide no actionable guidance on anomaly mechanisms or their temporal evolution. We address these via adaptive graph learning with specialized expert networks that provide built-in interpretability. Our framework captures multi-scale temporal dependencies through BiLSTM with self-attention, fuses temporal and spatial information via cross-modal attention, learns dynamic graphs through neural multi-source interpolation, adaptively balances learned dynamics with structural priors via stress-modulated fusion, routes anomalies to four mechanism-specific experts, and produces dual-level interpretable attributions. Critically, interpretability is embedded architecturally rather than applied post-hoc. On 100 US equities (2017-2024), we achieve 92.3% detection of 13 major events with 3.8-day lead time, outperforming best baseline by 30.8pp. Silicon Valley Bank case study demonstrates anomaly evolution tracking: Price-Shock expert weight rose to 0.39 (33% above baseline 0.29) during closure, peaking at 0.48 (66% above baseline) one week later, revealing automatic temporal mechanism identification without labeled supervision.

Related papers

• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Generalizing GNNs with Tokenized Mixture of Experts [75.8310720413187]
  We show that improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor.<n>We propose STEM-GNN, a pretrain-then-finetune framework with a mixture-of-experts encoder for diverse computation paths.<n>Across nine node, link, and graph benchmarks, STEM-GNN achieves a stronger three-way balance, improving robustness to degree/homophily shifts and to feature/edge corruptions while remaining competitive on clean graphs.
  arXiv  Detail & Related papers  (2026-02-09T22:48:30Z)
• Agentic Uncertainty Quantification [76.94013626702183]
  We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
  arXiv  Detail & Related papers  (2026-01-22T07:16:26Z)
• The Red Queen's Trap: Limits of Deep Evolution in High-Frequency Trading [1.9290392443571385]
  "Galaxy Empire" is a hybrid framework coupling LSTM/Transformer-based perception with a genetic "Time-is-Life" survival mechanism.<n>We observed a catastrophic divergence between training metrics and live performance.<n>Our findings provide empirical evidence that increasing asymmetry in the absence of information exacerbates systemic fragility.
  arXiv  Detail & Related papers  (2025-12-05T19:30:26Z)
• Beyond Mimicry: Preference Coherence in LLMs [0.19116784879310025]
  We investigate whether large language models exhibit genuine preference structures by testing their responses to AI-specific trade-offs.<n>We find 23 combinations (47.9%) demonstrated statistically significant relationships between scenario intensity and choice patterns.<n>Only 5 combinations (10.4%) demonstrate meaningful preference coherence through adaptive or threshold-based behavior.<n>The prevalence of unstable transitions (45.8%) and stimulus-specific sensitivities suggests current AI systems lack unified preference structures.
  arXiv  Detail & Related papers  (2025-11-17T17:41:48Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Semi-supervised and unsupervised learning for health indicator extraction from guided waves in aerospace composite structures [0.6922116595976161]
  Health indicators (HIs) are central to diagnosing and prognosing the condition of aerospace composite structures.<n>This study presents a data-driven framework that learns HIs via two learning approaches integrated with multi-domain signal processing.
  arXiv  Detail & Related papers  (2025-10-28T16:44:11Z)
• T3former: Temporal Graph Classification with Topological Machine Learning [4.4924444466378555]
  Temporal graph classification plays a critical role in applications such as cybersecurity, brain connectivity analysis, and traffic monitoring.<n>We introduce T3former, a novel Topological Temporal Transformer that leverages sliding-window topological and spectral descriptors as first-class tokens, integrated via a specialized Descriptor-Attention mechanism.<n>T3former achieves state-of-the-art performance across multiple benchmarks, including dynamic social networks, brain functional connectivity datasets, and traffic networks.
  arXiv  Detail & Related papers  (2025-10-15T17:46:32Z)
• Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails [103.05296856071931]
  We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving Large Language Model (LLM) agents.<n>ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies.<n>Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states.
  arXiv  Detail & Related papers  (2025-10-06T14:48:39Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [74.56971641937771]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems [25.567981742631005]
  Causal Graph-based Anomaly Detection framework designed for reliable cyberattack detection in public infrastructure systems.<n>CGAD follows a two-phase supervised framework -- causal profiling and anomaly scoring.<n>By leveraging causal structures, CGAD achieves superior adaptability and accuracy in non-stationary and imbalanced time series environments.
  arXiv  Detail & Related papers  (2025-08-13T05:26:43Z)
• Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures [25.567981742631005]
  The escalating threat of cyberattacks on real-time critical infrastructures poses serious risks to public safety.<n>Traditional real-time anomaly detection techniques often suffer from excessive false positives due to their statistical sensitivity to high data variance and class imbalance.<n>We propose INCADET, a novel framework for incremental causal graph learning tailored to real-time cyberattack detection.
  arXiv  Detail & Related papers  (2025-07-18T22:27:13Z)
• Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection [2.1161810694525807]
  This paper proposes an Augmented Temporal-aware Graph Attention Network (ATGAT) that enhances detection performance.<n>Experiments on the Elliptic++ cryptocurrency dataset demonstrate that ATGAT achieves an AUC of 0.9130, representing a 9.2% improvement over the best traditional method.
  arXiv  Detail & Related papers  (2025-06-26T15:34:06Z)
• Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals [15.249261198557218]
  Semi-supervised anomaly detection for sensor signals is critical in ensuring system reliability in smart manufacturing. This paper innovatively constructs a counterfactual causal reinforcement learning model, termed Triple-Assisted Causal Reinforcement Learning Anomaly Detector (Tri-CRLAD) Experimental results across seven diverse sensor signal datasets demonstrate that Tri-CRLAD outperforms nine state-of-the-art baseline methods.
  arXiv  Detail & Related papers  (2024-05-11T06:10:05Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [68.86835407617778]
  Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
  arXiv  Detail & Related papers  (2021-10-06T10:33:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.