arXiv:2607. 26859v1 Announce Type: cross Abstract: The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data.
arXiv:2609.25542v1 Announce Type: new Abstract: Corporate default prediction is a core problem in financial risk management, yet traditional credit models rely heavily on financial statements that ar...
arXiv:2603. 04818v3 Announce Type: replace Abstract: Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings.
Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services.
arXiv:2607. 11212v1 Announce Type: new Abstract: Relational fraud detection can exploit both label-free graph context and label-derived neighborhood evidence, but these two information sources obey different validity conditions.
arXiv:2608. 02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems.
arXiv:2608. 12594v1 Announce Type: cross Abstract: As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer companies for comparison is a fundamental challenge for valuation, due diligence, portfolio construction, and risk management.
Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection.
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.
arXiv:2602. 19591v3 Announce Type: replace-cross Abstract: Small and Medium Enterprises (SMEs) constitute 99.
arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.
arXiv:2606. 24509v1 Announce Type: cross Abstract: Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention.
The paper introduces SEMGNN, an end‑to‑end self‑explainable multi‑label graph neural network that simultaneously classifies nodes and identifies edges contributing to each predicted label. Unlike post‑hoc explainers, SEMGNN jointly learns a predictor and a sparse edge‑mask explainer, leveraging label‑label correlations to improve classification and generate distinct, coherent explanations for each label. Experiments on synthetic and real‑world networks in social, entertainment, and life‑science domains demonstrate competitive predictive performance and more faithful, compact label‑conditioned explanations.